Archive for the ‘Topic Maps’ Category

We’re Bringing Learning to Rank to Elasticsearch [Merging Properties Query Dependent?]

Tuesday, February 14th, 2017

From the post:

It’s no secret that machine learning is revolutionizing many industries. This is equally true in search, where companies exhaust themselves capturing nuance through manually tuned search relevance. Mature search organizations want to get past the “good enough” of manual tuning to build smarter, self-learning search systems.

That’s why we’re excited to release our Elasticsearch Learning to Rank Plugin. What is learning to rank? With learning to rank, a team trains a machine learning model to learn what users deem relevant.

When implementing Learning to Rank you need to:

1. Measure what users deem relevant through analytics, to build a judgment list grading documents as exactly relevant, moderately relevant, not relevant, for queries
2. Hypothesize which features might help predict relevance such as TF*IDF of specific field matches, recency, personalization for the searching user, etc.
3. Train a model that can accurately map features to a relevance score
4. Deploy the model to your search infrastructure, using it to rank search results in production

Don’t fool yourself: underneath each of these steps lie complex, hard technical and non-technical problems. There’s still no silver bullet. As we mention in Relevant Search, manual tuning of search results comes with many of the same challenges as a good learning to rank solution. We’ll have more to say about the many infrastructure, technical, and non-technical challenges of mature learning to rank solutions in future blog posts.

… (emphasis in original)

A great post as always but of particular interest for topic map fans is this passage:

Many of these features aren’t static properties of the documents in the search engine. Instead they are query dependent – they measure some relationship between the user or their query and a document. And to readers of Relevant Search, this is what we term signals in that book.
… (emphasis in original)

Do you read this as suggesting the merging exhibited to users should depend upon their queries?

That two or more users, with different query histories could (should?) get different merged results from the same topic map?

Now that’s an interesting suggestion!

Enjoy this post and follow the blog for more of same.

(I have a copy of Relevant Search waiting to be read so I had better get to it!)

Researchers found mathematical structure that was thought not to exist [Topic Map Epistemology]

Tuesday, November 15th, 2016

Researchers found mathematical structure that was thought not to exist

From the post:

Researchers found mathematical structure that was thought not to exist. The best possible q-analogs of codes may be useful in more efficient data transmission.

The best possible q-analogs of codes may be useful in more efficient data transmission.

In the 1970s, a group of mathematicians started developing a theory according to which codes could be presented at a level one step higher than the sequences formed by zeros and ones: mathematical subspaces named q-analogs.

While “things thought to not exist” may pose problems for ontologies and other mechanical replicas of truth, topic maps are untroubled by them.

As the Topic Maps Data Model (TMDM) provides:

subject: anything whatsoever, regardless of whether it exists or has any other specific characteristics, about which anything whatsoever may be asserted by any means whatsoever

A topic map can be constrained by its author to be as stunted as early 20th century logical positivism or have a more post-modernist approach, somewhere in between or elsewhere, but topic maps in general are amenable to any such choice.

One obvious advantage of topic maps being that characteristics of things “thought not to exist” can be captured as they are discussed, only to result in the merging of those discussions with those following the discovery things “thought not to exist really do exist.”

The reverse is also true, that is topic maps can capture the characteristics of things “thought to exist” which are later “thought to not exist,” along with the transition from “existence” to being thought to be non-existent.

If existence to non-existence sounds difficult, imagine a police investigation where preliminary statements then change and or replaced by other statements. You may want to capture prior statements, no longer thought to be true, along with their relationships to later statements.

In “real world” situations, you need epistemological assumptions in your semantic paradigm that adapt to the world as experienced and not limited to the world as imagined by others.

Topic maps offer an open epistemological assumption.

The Podesta Emails [In Bulk]

Wednesday, October 19th, 2016

Wikileaks has been posting:

The Podesta Emails, described as:

WikiLeaks series on deals involving Hillary Clinton campaign Chairman John Podesta. Mr Podesta is a long-term associate of the Clintons and was President Bill Clinton’s Chief of Staff from 1998 until 2001. Mr Podesta also owns the Podesta Group with his brother Tony, a major lobbying firm and is the Chair of the Center for American Progress (CAP), a Washington DC-based think tank.

long enough for them to be decried as “interference” with the U.S. presidential election.

You have two search options, basic:

As handy as these search interfaces are, you cannot easily:

• Analyze relationships between multiple senders and/or recipients of emails
• Perform entity recognition across the emails as a corpus
• Process the emails with other software
• Integrate the emails with other data sources
• etc., etc.

Michael Best, @NatSecGeek, is posting all the Podesta emails as they are released at: Podesta Emails (zipped).

As of Podesta Emails 13, there is approximately 2 GB of zipped email files available for downloading.

The search interfaces at Wikileaks may work for you, but if you want to get closer to the metal, you have Michael Best to thank for that opportunity!

Enjoy!

NSA: Being Found Beats Searching, Every Time

Tuesday, September 20th, 2016

From the post:

This week someone auctioning hacking tools obtained from the NSA-based hacking group “Equation Group” released a dump of around 250 megabytes of “free” files for proof alongside the auction.

The dump contains a set of exploits, implants and tools for hacking firewalls (“Firewall Operations”). This post aims to be a comprehensive list of all the tools contained or referenced in the dump.

Mustafa’s post is a great illustration of why “being found beats searching, every time.”

Think of the cycles you would have to spend to duplicate this list. Multiple that by the number of people interested in this list. Assuming their time is not valueless, do you start to see the value-add of Mustafa’s post?

Mustafa found each of these items in the data dump and then preserved his finding for the use of others.

It’s not a very big step beyond this preservation to the creation of a container for each of these items, enabling the preservation of other material found on them or related to them.

Search is a starting place and not a destination.

Unless you enjoy repeating the same finding process over and over again.

No Properties/No Structure – But, Subject Identity

Thursday, September 8th, 2016

Jack Park has prodded me into following some category theory and data integration papers. More on that to follow but as part of that, I have been watching Bartosz Milewski’s lectures on category theory, reading his blog, etc.

In Category Theory 1.2, Mileski goes to great lengths to emphasize:

Objects are primitives with no properties/structure – a point

Morphism are primitives with no properties/structure, but do have a start and end point

Late in that lecture, Milewski says categories are the “ultimate in data hiding” (read abstraction).

Despite their lack of properties and structure, both objects and morphisms have subject identity.

Yes?

I think that is more than clever use of language and here’s why:

If I want to talk about objects in category theory as a group subject, what can I say about them? (assuming a scope of category theory)

1. Objects have no properties
2. Objects have no structure
3. Objects mark the start and end of morphisms (distinguishes them from morphisms)
4. Every object has an identity morphism
5. Every pair of objects may have 0, 1, or many morphisms between them
6. Morphisms may go in both directions, between a pair of morphisms
7. An object can have multiple morphisms that start and end at it

Incomplete and yet a lot of things to say about something that has no properties and no structure. 😉

Bearing in mind, that’s just objects in general.

I can also talk about a specific object at a particular time point in the lecture and screen location, which itself is a subject.

Or an object in a paper or monograph.

We can declare primitives, like objects and morphisms, but we should always bear in mind they are declared to be primitives.

For other purposes, we can declare them to be otherwise.

Data Provenance: A Short Bibliography

Tuesday, September 6th, 2016

The video Provenance for Database Transformations by Val Tannen ends with a short bibliography.

Links and abstracts for the items in Val’s bibliography:

Provenance Semirings by Todd J. Green, Grigoris Karvounarakis, Val Tannen. (2007)

We show that relational algebra calculations for incomplete databases, probabilistic databases, bag semantics and whyprovenance are particular cases of the same general algorithms involving semirings. This further suggests a comprehensive provenance representation that uses semirings of polynomials. We extend these considerations to datalog and semirings of formal power series. We give algorithms for datalog provenance calculation as well as datalog evaluation for incomplete and probabilistic databases. Finally, we show that for some semirings containment of conjunctive queries is the same as for standard set semantics.

Update Exchange with Mappings and Provenance by Todd J. Green, Grigoris Karvounarakis, Zachary G. Ives, Val Tannen. (2007)

We consider systems for data sharing among heterogeneous peers related by a network of schema mappings. Each peer has a locally controlled and edited database instance, but wants to ask queries over related data from other peers as well. To achieve this, every peer’s updates propagate along the mappings to the other peers. However, this update exchange is filtered by trust conditions — expressing what data and sources a peer judges to be authoritative — which may cause a peer to reject another’s updates. In order to support such filtering, updates carry provenance information. These systems target scientific data sharing applications, and their general principles and architecture have been described in [20].

In this paper we present methods for realizing such systems. Specifically, we extend techniques from data integration, data exchange, and incremental view maintenance to propagate updates along mappings; we integrate a novel model for tracking data provenance, such that curators may filter updates based on trust conditions over this provenance; we discuss strategies for implementing our techniques in conjunction with an RDBMS; and we experimentally demonstrate the viability of our techniques in the ORCHESTRA prototype system.

Annotated XML: Queries and Provenance by J. Nathan Foster, Todd J. Green, Val Tannen. (2008)

We present a formal framework for capturing the provenance of data appearing in XQuery views of XML. Building on previous work on relations and their (positive) query languages, we decorate unordered XML with annotations from commutative semirings and show that these annotations suffice for a large positive fragment of XQuery applied to this data. In addition to tracking provenance metadata, the framework can be used to represent and process XML with repetitions, incomplete XML, and probabilistic XML, and provides a basis for enforcing access control policies in security applications.

Each of these applications builds on our semantics for XQuery, which we present in several steps: we generalize the semantics of the Nested Relational Calculus (NRC) to handle semiring-annotated complex values, we extend it with a recursive type and structural recursion operator for trees, and we define a semantics for XQuery on annotated XML by translation into this calculus.

Containment of Conjunctive Queries on Annotated Relations by Todd J. Green. (2009)

We study containment and equivalence of (unions of) conjunctive queries on relations annotated with elements of a commutative semiring. Such relations and the semantics of positive relational queries on them were introduced in a recent paper as a generalization of set semantics, bag semantics, incomplete databases, and databases annotated with various kinds of provenance information. We obtain positive decidability results and complexity characterizations for databases with lineage, why-provenance, and provenance polynomial annotations, for both conjunctive queries and unions of conjunctive queries. At least one of these results is surprising given that provenance polynomial annotations seem “more expressive” than bag semantics and under the latter, containment of unions of conjunctive queries is known to be undecidable. The decision procedures rely on interesting variations on the notion of containment mappings. We also show that for any positive semiring (a very large class) and conjunctive queries without self-joins, equivalence is the same as isomorphism.

Collaborative Data Sharing with Mappings and Provenance by Todd J. Green, dissertation. (2009)

A key challenge in science today involves integrating data from databases managed by different collaborating scientists. In this dissertation, we develop the foundations and applications of collaborative data sharing systems (CDSSs), which address this challenge. A CDSS allows collaborators to define loose confederations of heterogeneous databases, relating them through schema mappings that establish how data should flow from one site to the next. In addition to simply propagating data along the mappings, it is critical to record data provenance (annotations describing where and how data originated) and to support policies allowing scientists to specify whose data they trust, and when. Since a large data sharing confederation is certain to evolve over time, the CDSS must also efficiently handle incremental changes to data, schemas, and mappings.

We focus in this dissertation on the formal foundations of CDSSs, as well as practical issues of its implementation in a prototype CDSS called Orchestra. We propose a novel model of data provenance appropriate for CDSSs, based on a framework of semiring-annotated relations. This framework elegantly generalizes a number of other important database semantics involving annotated relations, including ranked results, prior provenance models, and probabilistic databases. We describe the design and implementation of the Orchestra prototype, which supports update propagation across schema mappings while maintaining data provenance and filtering data according to trust policies. We investigate fundamental questions of query containment and equivalence in the context of provenance information. We use the results of these investigations to develop novel approaches to efficiently propagating changes to data and mappings in a CDSS. Our approaches highlight unexpected connections between the two problems and with the problem of optimizing queries using materialized views. Finally, we show that semiring annotations also make sense for XML and nested relational data, paving the way towards a future extension of CDSS to these richer data models.

Provenance in Collaborative Data Sharing by Grigoris Karvounarakis, dissertation. (2009)

This dissertation focuses on recording, maintaining and exploiting provenance information in Collaborative Data Sharing Systems (CDSS). These are systems that support data sharing across loosely-coupled, heterogeneous collections of relational databases related by declarative schema mappings. A fundamental challenge in a CDSS is to support the capability of update exchange — which publishes a participant’s updates and then translates others’ updates to the participant’s local schema and imports them — while tolerating disagreement between them and recording the provenance of exchanged data, i.e., information about the sources and mappings involved in their propagation. This provenance information can be useful during update exchange, e.g., to evaluate provenance-based trust policies. It can also be exploited after update exchange, to answer a variety of user queries, about the quality, uncertainty or authority of the data, for applications such as trust assessment, ranking for keyword search over databases, or query answering in probabilistic databases.

To address these challenges, in this dissertation we develop a novel model of provenance graphs that is informative enough to satisfy the needs of CDSS users and captures the semantics of query answering on various forms of annotated relations. We extend techniques from data integration, data exchange, incremental view maintenance and view update to define the formal semantics of unidirectional and bidirectional update exchange. We develop algorithms to perform update exchange incrementally while maintaining provenance information. We present strategies for implementing our techniques over an RDBMS and experimentally demonstrate their viability in the ORCHESTRA prototype system. We define ProQL, iv a query language for provenance graphs that can be used by CDSS users to combine data querying with provenance testing as well as to compute annotations for their data, based on their provenance, that are useful for a variety of applications. Finally, we develop a prototype implementation ProQL over an RDBMS and indexing techniques to speed up provenance querying, evaluate experimentally the performance of provenance querying and the benefits of our indexing techniques.

Provenance for Aggregate Queries by Yael Amsterdamer, Daniel Deutch, Val Tannen. (2011)

We study in this paper provenance information for queries with aggregation. Provenance information was studied in the context of various query languages that do not allow for aggregation, and recent work has suggested to capture provenance by annotating the different database tuples with elements of a commutative semiring and propagating the annotations through query evaluation. We show that aggregate queries pose novel challenges rendering this approach inapplicable. Consequently, we propose a new approach, where we annotate with provenance information not just tuples but also the individual values within tuples, using provenance to describe the values computation. We realize this approach in a concrete construction, first for “simple” queries where the aggregation operator is the last one applied, and then for arbitrary (positive) relational algebra queries with aggregation; the latter queries are shown to be more challenging in this context. Finally, we use aggregation to encode queries with difference, and study the semantics obtained for such queries on provenance annotated databases.

Circuits for Datalog Provenance by Daniel Deutch, Tova Milo, Sudeepa Roy, Val Tannen. (2014)

The annotation of the results of database queries with provenance information has many applications. This paper studies provenance for datalog queries. We start by considering provenance representation by (positive) Boolean expressions, as pioneered in the theories of incomplete and probabilistic databases. We show that even for linear datalog programs the representation of provenance using Boolean expressions incurs a super-polynomial size blowup in data complexity. We address this with an approach that is novel in provenance studies, showing that we can construct in PTIME poly-size (data complexity) provenance representations as Boolean circuits. Then we present optimization techniques that embed the construction of circuits into seminaive datalog evaluation, and further reduce the size of the circuits. We also illustrate the usefulness of our approach in multiple application domains such as query evaluation in probabilistic databases, and in deletion propagation. Next, we study the possibility of extending the circuit approach to the more general framework of semiring annotations introduced in earlier work. We show that for a large and useful class of provenance semirings, we can construct in PTIME poly-size circuits that capture the provenance.

Incomplete but a substantial starting point exploring data provenance and its relationship/use with topic map merging.

To get a feel for “data provenance” just prior to the earliest reference here (2007), consider A Survey of Data Provenance Techniques by Yogesh L. Simmhan, Beth Plale, Dennis Gannon, published in 2005.

Data management is growing in complexity as large-scale applications take advantage of the loosely coupled resources brought together by grid middleware and by abundant storage capacity. Metadata describing the data products used in and generated by these applications is essential to disambiguate the data and enable reuse. Data provenance, one kind of metadata, pertains to the derivation history of a data product starting from its original sources.

The provenance of data products generated by complex transformations such as workflows is of considerable value to scientists. From it, one can ascertain the quality of the data based on its ancestral data and derivations, track back sources of errors, allow automated re-enactment of derivations to update a data, and provide attribution of data sources. Provenance is also essential to the business domain where it can be used to drill down to the source of data in a data warehouse, track the creation of intellectual property, and provide an audit trail for regulatory purposes.

In this paper we create a taxonomy of data provenance techniques, and apply the classification to current research efforts in the field. The main aspect of our taxonomy categorizes provenance systems based on why they record provenance, what they describe, how they represent and store provenance, and ways to disseminate it. Our synthesis can help those building scientific and business metadata-management systems to understand existing provenance system designs. The survey culminates with an identification of open research problems in the field.

Another rich source of reading material!

Merge 5 Proxies, Take Away 1 Proxy = ? [Data Provenance]

Monday, September 5th, 2016

Provenance for Database Transformations by Val Tannen. (video)

Description:

Database transformations (queries, views, mappings) take apart, filter,and recombine source data in order to populate warehouses, materialize views,and provide inputs to analysis tools. As they do so, applications often need to track the relationship between parts and pieces of the sources and parts and pieces of the transformations’ output. This relationship is what we call database provenance.

This talk presents an approach to database provenance that relies on two observations. First, provenance is a kind of annotation, and we can develop a general approach to annotation propagation that also covers other applications, for example to uncertainty and access control. In fact, provenance turns out to be the most general kind of such annotation,in a precise and practically useful sense. Second, the propagation of annotation through a broad class of transformations relies on just two operations: one when annotations are jointly used and one when they are used alternatively.This leads to annotations forming a specific algebraic structure, a commutative semiring.

The semiring approach works for annotating tuples, field values and attributes in standard relations, in nested relations (complex values), and for annotating nodes in (unordered) XML. It works for transformations expressed in the positive fragment of relational algebra, nested relational calculus, unordered XQuery, as well as for Datalog, GLAV schema mappings, and tgd constraints. Finally, when properly extended to semimodules it works for queries with aggregates. Specific semirings correspond to earlier approaches to provenance, while others correspond to forms of uncertainty, trust, cost, and access control.

What does happen when you subtract from a merge? (Referenced here as an “aggregation.”)

Although possible to paw through logs to puzzle out a result, Val suggests there are more robust methods at our disposal.

I watched this over the weekend and be forewarned, heavy sledding ahead!

This is an active area of research and I have only begun to scratch the surface for references.

I may discover differently, but the “aggregation” I have seen thus far relies on opaque strings.

Not that all uses of opaque strings are inappropriate, but imagine the power of treating a token as an opaque string for one use case and exploding that same token into key/value pairs for another.

Enjoy!

The rich are getting more secretive with their money [Calling All Cybercriminals]

Tuesday, August 30th, 2016

From the post:

You might think the Panama Papers leak would cause the ultrarich to seek more transparent tax havens.

Not so, according to Jordan Greenaway, a consultant based in London who caters to the ultrawealthy.

Instead, they are going further underground, seeking walled-up havens such as the Marshall Islands, Lebanon, and Antigua, Greenaway, who works for the PR agency Right Angles, told Business Insider.

The Panama Papers leak around Mossack Fonseca, a law firm that helped politicians and businesspeople hide their money, has increased anxiety among the rich over being exposed, Greenaway told New York reporters in a meeting last week.

“The Panama Papers sent them to the ground,” he said

I should hope so.

The Panama Papers leak, what we know of it (hint, hint to data hoarders), was like giants capturing dwarfs in a sack. It takes some effort but not a lot.

Especially when someone dumps the Panama Papers data in your lap. News organizations have labored to make sense of that massive trove of data but its acquisition wasn’t difficult.

From Rachael’s report, the rich want to up their game on data acquisition. Fair enough.

But 2016 cybersecurity reports leave you agreeing that “sieve” is a generous description of current information security.

Cybercriminals are reluctant to share their exploits, but after exploiting data fully, they should dump their data to public repositories.

That will protect their interests (I didn’t say legitimate) in their exploits and at the same time, enable others to track the secrets of the wealthy, albeit with a time delay.

The IRS and EU tax authorities will both subscribe to RSS feeds for such data.

The Iraq Inquiry (Chilcot Report) [4.5x longer than War and Peace]

Wednesday, July 6th, 2016

The Iraq Inquiry

To give a rough sense of the depth of the Chilcot Report, the executive summary runs 150 pages. The report appears in twelve (12) volumes, not including video testimony, witness transcripts, documentary evidence, contributions and the like.

Cory Doctorow reports a Guardian project to crowd source collecting facts from the 2.6 million word report. The Guardian observes the Chilcot report is “…almost four-and-a-half times as long as War and Peace.”

Manual reading of the Chilcot report is doable, but unlikely to yield all of the connections that exist between participants, witnesses, evidence, etc.

How would you go about making the Chilcot report and its supporting evidence more amenable to navigation and analysis?

The Report

The Evidence

Other Material

Unfortunately, sections within volumes were not numbered according to their volume. In other words, volume 2 starts with section 3.3 and ends with 3.5, whereas volume 4 only contains sections beginning with “4.,” while volume 5 starts with section 5 but also contains sections 6.1 and 6.2. Nothing can be done for it but be aware that section numbers don’t correspond to volume numbers.

Functor Fact @FunctorFact [+ Tip for Selling Topic Maps]

Tuesday, June 28th, 2016

JohnDCook has started @FunctorFact, tweets “..about category theory and functional programming.”

John has a page listing his Twitter accounts. It needs to be updated to reflect the addition of @FunctorFact.

BTW, just by accident I’m sure, John’s blog post for today is titled: Category theory and Koine Greek. It has the following lesson for topic map practitioners and theorists:

Another lesson from that workshop, the one I want to focus on here, is that you don’t always need to convey how you arrived at an idea. Specifically, the leader of the workshop said that if you discover something interesting from reading the New Testament in Greek, you can usually present your point persuasively using the text in your audience’s language without appealing to Greek. This isn’t always possible—you may need to explore the meaning of a Greek word or two—but you can use Greek for your personal study without necessarily sharing it publicly. The point isn’t to hide anything, only to consider your audience. In a room full of Greek scholars, bring out the Greek.

This story came up in a recent conversation about category theory. You might discover something via category theory but then share it without discussing category theory. If your audience is well versed in category theory, then go ahead and bring out your categories. But otherwise your audience might be bored or intimidated, as many people would be listening to an argument based on the finer points of Koine Greek grammar. Microsoft’s LINQ software, for example, was inspired by category theory principles, but you’d be hard pressed to find any reference to this because most programmers don’t want to know or need to know where it came from. They just want to know how to use it.

Sure, it is possible to recursively map subject identities in order to arrive at a useful and maintainable mapping between subject domains, but the people with the checkbook are only interested in a viable result.

How you got there could involve enslaved pixies for all they care. They do care about negative publicity so keep your use of pixies to yourself.

Looking forward to tweets from @FunctorFact!

Record Linkage (Think Topic Maps) In War Crimes Investigations

Thursday, June 9th, 2016

Megan is the executive director of the Human Rights Data Analysis Group (HRDAG), an organization that applies data science techniques to documenting violence and potential human rights abuses.

I watched the video expecting extended discussion of machine learning, only to find that our old friend, record linkage, was mentioned repeatedly during the presentation. Along with some description of the difficulty of reconciling lists of identified casualties in war zones.

Not to mention the task of estimating casualties that will never appear by any type of reporting.

When Megan mentioned record linkage I was hooked and stayed for the full presentation. If you follow the link to Human Rights Data Analysis Group (HRDAG), you will find a number of publications, concerning the scientific side of their work.

Oh, record linkage is a technique used originally in epidemiology to “merge*” records from different authorities in order to study the transmission of disease. It dates from the late 1950’s and has been actively developed since then.

Including two complete and independent mathematical models, which arose because terminology differences prevented the second one from discovering the first. There’s a topic map example for you!

Certainly an area where the multiple facets (non-topic map sense) of subject identity would come into play. Not to mention making the merging of lists auditable. (They may already have that capability and I am unaware of it.)

It’s an interesting video and the website even more so.

Enjoy!

* One difference between record linkage and topic maps is that the usual record linkage technique maps diverse data into a single representation for processing. That technique loses the semantics associated with the terminology in the original records. Preservation of those semantics may not be your use case, but be aware you are losing data in such a process.

Balisage 2016 Program Posted! (Newcomers Welcome!)

Monday, May 23rd, 2016

Tommie Usdin wrote today to say:

Balisage: The Markup Conference
2016 Program Now Available
http://www.balisage.net/2016/Program.html

Balisage: where serious markup practitioners and theoreticians meet every August.

The 2016 program includes papers discussing reducing ambiguity in linked-open-data annotations, the visualization of XSLT execution patterns, automatic recognition of grant- and funding-related information in scientific papers, construction of an interactive interface to assist cybersecurity analysts, rules for graceful extension and customization of standard vocabularies, case studies of agile schema development, a report on XML encoding of subtitles for video, an extension of XPath to file systems, handling soft hyphens in historical texts, an automated validity checker for formatted pages, one no-angle-brackets editing interface for scholars of German family names and another for scholars of Roman legal history, and a survey of non-XML markup such as Markdown.

XML In, Web Out: A one-day Symposium on the sub rosa XML that powers an increasing number of websites will be held on Monday, August 1. http://balisage.net/XML-In-Web-Out/

If you are interested in open information, reusable documents, and vendor and application independence, then you need descriptive markup, and Balisage is the conference you should attend. Balisage brings together document architects, librarians, archivists, computer
scientists, XML practitioners, XSLT and XQuery programmers, implementers of XSLT and XQuery engines and other markup-related software, Topic-Map enthusiasts, semantic-Web evangelists, standards developers, academics, industrial researchers, government and NGO staff, industrial developers, practitioners, consultants, and the world’s greatest concentration of markup theorists. Some participants are busy designing replacements for XML while other still use SGML (and know why they do).

Discussion is open, candid, and unashamedly technical.

Balisage 2016 Program: http://www.balisage.net/2016/Program.html

Symposium Program: http://balisage.net/XML-In-Web-Out/symposiumProgram.html

Even if you don’t eat RELAX grammars at snack time, put Balisage on your conference schedule. Even if a bit scruffy looking, the long time participants like new document/information problems or new ways of looking at old ones. Not to mention they, on occasion, learn something from newcomers as well.

It is a unique opportunity to meet the people who engineered the tools and specs that you use day to day.

Be forewarned that most of them have difficulty agreeing what controversial terms mean, like “document,” but that to one side, they are a good a crew as you are likely to meet.

Enjoy!

Flawed Input Validation = Flawed Subject Recognition

Friday, May 13th, 2016

In Vulnerable 7-Zip As Poster Child For Open Source, I covered some of the details of two vulnerabilities in 7-Zip.

Both of those vulnerabilities were summarized by the discoverers:

Sadly, many security vulnerabilities arise from applications which fail to properly validate their input data. Both of these 7-Zip vulnerabilities resulted from flawed input validation. Because data can come from a potentially untrusted source, data input validation is of critical importance to all applications’ security.

The first vulnerability is described as:

An out-of-bounds read vulnerability exists in the way 7-Zip handles Universal Disk Format (UDF) files. The UDF file system was meant to replace the ISO-9660 file format, and was eventually adopted as the official file system for DVD-Video and DVD-Audio.

Central to 7-Zip’s processing of UDF files is the CInArchive::ReadFileItem method. Because volumes can have more than one partition map, their objects are kept in an object vector. To start looking for an item, this method tries to reference the proper object using the partition map’s object vector and the “PartitionRef” field from the Long Allocation Descriptor. Lack of checking whether the “PartitionRef” field is bigger than the available amount of partition map objects causes a read out-of-bounds and can lead, in some circumstances, to arbitrary code execution.

(code in original post omitted)

This vulnerability can be triggered by any entry that contains a malformed Long Allocation Descriptor. As you can see in lines 898-905 from the code above, the program searches for elements on a particular volume, and the file-set starts based on the RootDirICB Long Allocation Descriptor. That record can be purposely malformed for malicious purpose. The vulnerability appears in line 392, when the PartitionRef field exceeds the number of elements in PartitionMaps vector.

I would describe the lack of a check on the “PartitionRef” field in topic maps terms as allowing a subject, here a string, of indeterminate size. That is there is no constraint on the size of the subject, which is here a string.

That may seem like an obtuse way of putting it, but consider that for a subject, here a string that is longer than the “available amount of partition may objects,” can be in association with other subjects, such as the user (subject) who has invoked the application(association) containing the 7-Zip vulnerability (subject).

Err, you don’t allow users with shell access to suid root do you?

If you don’t, at least not running a vulnerable program as root may help dodge that bullet.

Or in topic maps terms, knowing the associations between applications and users may be a window on the severity of vulnerabilities.

Lest you think logging suid is an answer, remember they were logging Edward Snowden’s logins as well.

Suid logs may help for next time, but aren’t preventative in nature.

BTW, if you are interested in the details on buffer overflows, Smashing The Stack For Fun And Profit looks like a fun read.

Deep Learning: Image Similarity and Beyond (Webinar, May 10, 2016)

Friday, May 6th, 2016

Deep Learning: Image Similarity and Beyond (Webinar, May 10, 2016)

From the registration page:

Deep Learning is a powerful machine learning method for image tagging, object recognition, speech recognition, and text analysis. In this demo, we’ll cover the basic concept of deep learning and walk you through the steps to build an application that finds similar images using an already-trained deep learning model.

Recommended for:

• Data scientists and engineers
• Developers and technical team managers
• Technical product managers

What you’ll learn:

• How to leverage existing deep learning models
• How to extract deep features and use them using GraphLab Create
• How to build and deploy an image similarity service using Dato Predictive Services

What we’ll cover:

• Using an already-trained deep learning model
• Extracting deep features
• Building and deploying an image similarity service for pictures

Deep learning has difficulty justifying its choices, just like human judges of similarity, but could it play a role in assisting topic map authors in constructing explicit decisions for merging?

Once trained, could deep learning suggest properties and/or values to consider for merging it has not yet experienced?

I haven’t seen any webinars recently so I am ready to gamble on this being an interesting one.

Enjoy!

No Label (read “name”) for Medical Error – Fear of Terror

Wednesday, May 4th, 2016

From the post:

Medical error is the third leading cause of death in the US, accounting for 250,000 deaths every year, according to an analysis released on Tuesday.

There is no US system for coding these deaths, but Martin Makary and Michael Daniel, researchers at Johns Hopkins University’s school of medicine, used studies from 1999 onward to find that medical errors account for more than 9.5% of all fatalities in the US.

Only heart disease and cancer are more deadly, according to the Centers for Disease Control and Prevention (CDC).

The analysis, which was published in the British Medical Journal, said that the science behind medical errors would improve if data was shared internationally and nationally “in the same way as clinicians share research and innovation about coronary artery disease, melanoma, and influenza”.

But death by medical error is not captured by government reports because the US system for assigning a code to cause of death, the international classification of disease (ICD), does not have a label for medical error.

In contrast to topic maps, where you can talk about any subject you want, the international classification of disease (ICD), does not have a label for medical error.

Impact? Not having a label conceals approximately 250,000 deaths per year in the United States.

What if Fear of Terror press releases were broadcast but along with “deaths due to medical error to date this year” as contextual information?

Medical errors result in approximately 685 deaths per day.

If you heard the report of the shootings in San Bernardino, December 2, 2015 and that 14 people were killed and the report pointed out that to date, approximately 230,160 had died due to medical errors, which one would you judge to be the more serious problem?

Lacking a label for medical error as cause of death, prevents public discussion of the third leading cause of death in the United States.

Contrast that with the public discussion over the largely non-existent problem of terrorism in the United States.

Wednesday, April 27th, 2016

From the post:

Since publishing our post about “Extracting Structured Data From Recipes Using Conditional Random Fields,” we’ve received a tremendous number of requests to release the data and our code. Today, we’re excited to release the roughly 180,000 labeled ingredient phrases that we used to train our machine learning model.

You can find the data and code in the ingredient-phrase-tagger GitHub repo. Instructions are in the README and the raw data is in nyt-ingredients-snapshot-2015.csv.

Reaching a critical mass for any domain is a stumbling block for any topic map. Erica and Adam kick start your foodie topic map adventures with ~ 180,000 labeled ingredient phrases.

You are looking at the end result of six years of data mining and some clever programming so be sure to:

1. Always acknowledge this project along with Erica and Alex in your work.
2. Contribute back improved data.
3. Contribute back improvements on the conditional random fields (CRF).
4. Have a great time extending this data set!

Possible extensions include automatic translation (with mapping of “equivalent” terms), melding in the USDA food database (it’s formally known as: USDA National Nutrient Database for Standard Reference) with nutrient content information on ~8,800 foods, and, of course, the “correct” way to make a roux as reflected in your mother’s cookbook.

It is, unfortunately, true that you can buy a mix for roux in a cardboard box. That requires a food processor to chop up the cardboard to enjoy with the roux that came in it. I’m originally from Louisiana and the thought of a roux mix is depressing, if not heretical.

Reboot Your $100+ Million F-35 Stealth Jet Every 10 Hours Instead of 4 (TM Fusion) Wednesday, April 27th, 2016 Pentagon identifies cause of F-35 radar software issue From the post: The Pentagon has found the root cause of stability issues with the radar software being tested for the F-35 stealth fighter jet made by Lockheed Martin Corp, U.S. Defense Acquisition Chief Frank Kendall told a congressional hearing on Tuesday. Last month the Pentagon said the software instability issue meant the sensors had to be restarted once every four hours of flying. Kendall and Air Force Lieutenant General Christopher Bogdan, the program executive officer for the F-35, told a Senate Armed Service Committee hearing in written testimony that the cause of the problem was the timing of “software messages from the sensors to the main F-35” computer. They added that stability issues had improved to where the sensors only needed to be restarted after more than 10 hours. “We are cautiously optimistic that these fixes will resolve the current stability problems, but are waiting to see how the software performs in an operational test environment,” the officials said in a written statement. … (emphasis added) At$100+ Million plane that requires rebooting every ten hours? I’m not a pilot but that sounds like a real weakness.

The precise nature of the software glitch isn’t described but you can guess one of the problems from Lockheed Martin’s, Software You Wish You Had: Inside the F-35 Supercomputer:

The human brain relies on five senses—sight, smell, taste, touch and hearing—to provide the information it needs to analyze and understand the surrounding environment.

Similarly, the F-35 relies on five types of sensors: Electronic Warfare (EW), Radar, Communication, Navigation and Identification (CNI), Electro-Optical Targeting System (EOTS) and the Distributed Aperture System (DAS). The F-35 “brain”—the process that combines this stellar amount of information into an integrated picture of the environment—is known as sensor fusion.

At any given moment, fusion processes large amounts of data from sensors around the aircraft—plus additional information from datalinks with other in-air F-35s—and combines them into a centralized view of activity in the jet’s environment, displayed to the pilot.

In everyday life, you can imagine how useful this software might be—like going out for a jog in your neighborhood and picking up on real-time information about obstacles that lie ahead, changes in traffic patterns that may affect your route, and whether or not you are likely to pass by a friend near the local park.

F-35 fusion not only combines data, but figures out what additional information is needed and automatically tasks sensors to gather it—without the pilot ever having to ask.

The fusion of data from other in-air F-35s is a classic topic map merging of data problem.

You have one subject, say an anti-aircraft missile site, seen from up to four (in the F-35 specs) F-35s. As is the habit of most physical objects, it has only one geographic location but the fusion computer for the F-35 doesn’t come up with than answer.

“When you have two, three or four F-35s looking at the same threat, they don’t all see it exactly the same because of the angles that they are looking at and what their sensors pick up,” Bogdan told reporters Tuesday. “When there is a slight difference in what those four airplanes might be seeing, the fusion model can’t decide if it’s one threat or more than one threat. If two airplanes are looking at the same thing, they see it slightly differently because of the physics of it.”

For example, if a group of F-35s detect a single ground threat such as anti-aircraft weaponry, the sensors on the planes may have trouble distinguishing whether it was an isolated threat or several objects, Bogdan explained.

As a result, F-35 engineers are working with Navy experts and academics from John’s Hopkins Applied Physics Laboratory to adjust the sensitivity of the fusion algorithms for the JSF’s 2B software package so that groups of planes can correctly identify or discern threats.

“What we want to have happen is no matter which airplane is picking up the threat – whatever the angles or the sensors – they correctly identify a single threat and then pass that information to all four airplanes so that all four airplanes are looking at the same threat at the same place,” Bogdan said.

Unless Bogdan is using “sensitivity” in a very unusual sense, that doesn’t sound like the issue with the fusion computer of the F-35.

Rather the problem is the fusion computer has no explicit doctrine of subject identity to use when it is merging data from different F-35s, whether it be two, three, four or even more F-35s. The display of tactical information should be seamless to the pilot and without human intervention.

I’m sure members of Congress were impressed with General Bogdan using words like “angles” and “physics,” but the underlying subject identity issue isn’t hard to address.

At issue is the location of a potential target on the ground. Within some pre-defined metric, anything located within a given area is the “same target.”

The Air Force has already paid for this type of analysis and the mathematics of what is called Circular Error Probability (CEP) has been published in Use of Circular Error Probability in Target Detection by William Nelson (1988).

You need to use the “current” location of the detecting aircraft, allowances for inaccuracy in estimating the location of the target, etc., but once you call out the subject identity as an issue, its a matter of making choices of how accurate you want the subject identification to be.

Before you forward this to Gen. Bogdan as a way forward on the fusion computer, realize that CEP is only one aspect of target identification. But, calling the subject identity of targets out explicitly, enables reliable presentation of single/multiple targets to pilots.

Your call, confusing displays or a reliable, useful display.

PS: I assume military subject identity systems would not be running XTM software. Same principles apply even if the syntax is different.

Seriously, Who’s Gonna Find It?

Monday, April 25th, 2016

Graphic whimsy via Bruce Sterling, bruces@well.com.

Are your information requirements met by finding something or by finding the right thing?

Similar Pages for Wikipedia – Lateral – Indexing Practices

Saturday, April 23rd, 2016

Similar Pages for Wikipedia (Chrome extension)

I started looking at this software with a mis-impression that I hope you can avoid.

Unless I’m billing time, plowing through page after page of tangentially related material isn’t my idea of a good time.

Ah, but I confused “document” with “page.”

I discovered that error while reading Adding Documents at Lateral, which gives the following example:

Ah! So “document” means as much or as little text as I choose to use when I add the document.

Which means if I were creating a document store of graph papers, I would capture only the new material and not the inevitable a “graph consists of nodes and edges….”

There are pre-populatd data sets, News 350,000+ news and blog articles, updated every 15 mins; arXiv 1M+ papers (all), updated daily; PubMed 6M+ medical journals from before July 2014; SEC 6,000+ yearly financial reports / 10-K filings from 2014; Wikipedia 463,000 pages which had 20+ page views in 2013.

I suspect the granularity on the pre-populated data sets is “document” in the usual sense size.

Glad to see the option to define a “document” to be an arbitrary span of text.

I don’t need to find more “documents” (in the usual sense) but more relevant snippets that are directly on point.

Hmmm, perhaps indexing at the level of paragraphs instead of documents (usual sense)?

Which makes me wonder why we index at the level of documents (usual sense) anyway? Is it simply tradition from when indexes were prepared by human indexers? And indexes were limited by physical constraints?

Corporate Bribery/Corruption – Poland/U.S./Russia – A Trio

Friday, April 22nd, 2016

GIJN (Global Investigation Journalism Network) tweeted a link to Corporate misconduct – individual consequences, 14th Global Fraud Survey this morning.

From the foreword by David L. Stulb:

In the aftermath of recent major terrorist attacks and the revelations regarding widespread possible misuse of offshore jurisdictions, and in an environment where geopolitical tensions have reached levels not seen since the Cold War, governments around the world are under increased pressure to face up to the immense global challenges of terrorist financing, migration and corruption. At the same time, certain positive events, such as the agreement by the P5+1 group (China, France, Russia, the United Kingdom, the United States, plus Germany) with Iran to limit Iran’s sensitive nuclear activities are grounds for cautious optimism.

These issues contribute to volatility in financial markets. The banking sector remains under significant regulatory focus, with serious stress points remaining. Governments, meanwhile, are increasingly coordinated in their approaches to investigating misconduct, including recovering the proceeds of corruption. The reason for this is clear. Bribery and corruption continue to represent a substantial threat to sluggish global growth and fragile financial markets.

Law enforcement agencies, including the United States Department of Justice and the United States Securities and Exchange Commission, are increasingly focusing on individual misconduct when investigating impropriety. In this context, boards and executives need to be confident that their businesses comply with rapidly changing laws and regulations wherever they operate.

For this, our 14th Global Fraud Survey, EY interviewed senior executives with responsibility for tackling fraud, bribery and corruption. These individuals included chief financial officers, chief compliance officers, heads of internal audit and heads of legal departments. They are ideally placed to provide insight into the impact that fraud and corruption is having on business globally.

Despite increased regulatory activity, our research finds that boards could do significantly more to protect both themselves and their companies.

Many businesses have failed to execute anti-corruption programs to proactively mitigate their risk of corruption. Similarly, many businesses are not yet taking advantage of rich seams of information that would help them identify and mitigate fraud, bribery and corruption issues earlier.

Between October 2015 and January 2016, we interviewed 2,825 individuals from 62 countries and territories. The interviews identified trends, apparent contradictions and issues about which boards of directors should be aware.

Partners from our Fraud Investigation & Dispute Services practice subsequently supplemented the Ipsos MORI research with in-depth discussions with senior executives of multinational companies. In these interviews, we explored the executives’ experiences of operating in certain key business environments that are perceived to expose companies to higher fraud and corruption risks. Our conversations provided us with additional insights into the impact that changing legislation, levels of enforcement and cultural behaviors are having on their businesses. Our discussions also gave us the opportunity to explore pragmatic steps that leading companies have been taking to address these risks.

The executives to whom we spoke highlighted many matters that businesses must confront when operating across borders: how to adapt market-entry strategies in countries where cultural expectations of acceptable behaviors can differ; how to get behind a corporate structure to understand a third party’s true ownership; the potential negative impact that highly variable pay can have on incentives to commit fraud and how to encourage whistleblowers to speak up despite local social norms to the contrary, to highlight a few.

Our survey finds that many respondents still maintain the view that fraud, bribery and corruption are other people’s problems despite recognizing the prevalence of the issue in their own countries. There remains a worryingly high tolerance or misunderstanding of conduct that can be considered inappropriate — particularly among respondents from finance functions. While companies are typically aware of the historic risks, they are generally lagging behind on the emerging ones, for instance the potential impact of cybercrime on corporate reputation and value, while now well publicized, remains a matter of varying priority for our respondents. In this context, companies need to bolster their defenses. They should apply anti-corruption compliance programs, undertake appropriate due diligence on third parties with which they do business and encourage and support whistleblowers to come forward with confidence. Above all, with an increasing focus on the accountability of the individual, company leadership needs to set the right tone from the top. It is only by taking such steps that boards will be able to mitigate the impact should the worst happen.

This survey is intended to raise challenging questions for boards. It will, we hope, drive better conversations and ongoing dialogue with stakeholders on what are truly global issues of major importance.

We acknowledge and thank all those executives and business leaders who participated in our survey, either as respondents to Ipsos MORI or through meeting us in person, for their contributions and insights. (emphasis in original)

Apologies for the long quote but it was necessary to set the stage of the significance of:

…increasingly focusing on individual misconduct when investigating impropriety.

That policy grants a “bye” to corporations who benefit from individual mis-coduct, in favor of punishing individual actors within a corporation.

While granting the legitimacy of punishing individuals, corporations cannot act except by their agents, failing to punish corporations enables their shareholders to continue to benefit from illegal behavior.

Another point of significance, listing of countries on page 44, gives the percentage of respondents that agree “…bribery/corrupt practices happen widely…” as follows (in part):

 Rank Country % Agree 30 Poland 34 31 Russia 34 32 U.S. 34

When the Justice Department gets hoity-toity about law and corruption, keep those figures in mind.

If the Justice Department representative you are talking to isn’t corrupt, it happens, there’s one on either side of them that probably is.

Topic maps can help ferret out or manage “corruption,” depending upon your point of view. Even structural corruption, take the U.S. political campaign donation process.

Scope Rules!

Thursday, April 21st, 2016

I was reminded of the power of scope (in the topic map sense) when I saw John D. Cook’s Quaternions in Paradise Lost.

See John’s post for the details but in summary, Kuiper’s Quaternions and Rotation Sequences quoted a passage from Milton that used the term quarterion.

Your search appliance and most if not all of the public search engines will happily return all uses of quarterion without distinction. (Yes, I am implying there is more than one meaning for quarterion. See John’s post for the details.)

In addition to distinguishing between usages in Milton and Kuiper, scope can cleanly separate terms by agency, activity, government or other distinctions.

Or you can simply wade through search glut.

Searching for Subjects: Which Method is Right for You?

Wednesday, April 20th, 2016

Leaving to one side how to avoid re-evaluating the repetitive glut of materials from any search, there is the more fundamental problem of how to you search for a subject?

This is a back-of-the-envelope sketch that I will be expanding, but here goes:

Basic Search

At its most basic, a search consists of a <term> and the search seeks to match strings that match that <term>.

Even allowing for Boolean operators, the matches against <term> are only and forever string matches.

Basic Search + Synonyms

Of course, as skilled searchers you will try not only one <term>, but several <synonym>s for the term as well.

A good example of that strategy is used at PubMed:

If you enter an entry term for a MeSH term the translation will also include an all fields search for the MeSH term associated with the entry term. For example, a search for odontalgia will translate to: “toothache”[MeSH Terms] OR “toothache”[All Fields] OR “odontalgia”[All Fields] because Odontalgia is an entry term for the MeSH term toothache. [PubMed Help]

The expansion to include the MeSH term Odontalgia is useful, but how do you maintain it?

A reader can see “toothache” and “Odontalgia” are treated as synonyms, but why remains elusive.

This is the area of owl:sameAs, the mapping of multiple subject identifiers/locators to a single topic, etc. You know that “sameness” exists, but why isn’t clear.

Subject Identity Properties

In order to maintain a PubMed or similar mapping, you need people who either “know” the basis for the mappings or you can have the mappings documented. That is you can say on what basis the mapping happened and what properties were present.

For example:

toothache

 Key Value symptom pain general-location mouth specific-location tooth

So if we are mapping terms to other terms and the specific location value reads “tongue,” then we know that isn’t a mapping to “toothache.”

How Far Do You Need To Go?

Of course for every term that we use as a key or value, there can be an expansion into key/value pairs, such as for tooth:

tooth

 Key Value general-location mouth composition enamel coated bone use biting, chewing

Observations:

Each step towards more precise gathering of information increases your pre-search costs but decreases your post-search cost of casting out irrelevant material.

Moreover, precise gathering of information will help you avoid missing data simply due to data glut returns.

If maintenance of your mapping across generations is a concern, doing more than mapping of synonyms for reason or reasons unknown may be in order.

The point being that your current retrieval or lack thereof of current and correct information has a cost. As does improving your current retrieval.

The question of improved retrieval isn’t ideological but an ROI driven one.

• If you have better mappings will that give you an advantage over N department/agency?
• Will better retrieval slow down (never stop) the time wasted by staff on voluminous search results?
• Will more precision focus your limited resources (always limited) on highly relevant materials?

Formulate your own ROI questions and means of measuring them. Then reach out to topic maps to see how they improve (or not) your ROI.

Properly used, I think you are in for a pleasant surprise with topic maps.

Dictionary of Fantastic Vocabulary [Increasing the Need for Topic Maps]

Monday, April 18th, 2016

Alexis Lloyd tweeted this link along with:

This is utterly fantastic.

Well, it certainly increases the need for topic maps!

From the bot description on Twitter:

Generating new words with new meanings out of the atoms of English.

Ahem, are you sure about that?

Is a bot is generating meaning?

Or are readers conferring meaning on the new words as they are read?

If, as I contend, readers confer meaning, the utterance of every “new” word, opens up as many new meanings as there are readers of the “new” word.

Example of people conferring different meanings on a term?

Ask a dozen people what is meant by “shot” in:

It’s just a shot away

When Lisa Fischer breaks into her solo in:

(Best played loud.)

Differences in meanings make for funny moments, awkward pauses, blushes, in casual conversation.

What if the stakes are higher?

What if you need to produce (or destroy) all the emails by “bobby1.”

Is it enough to find some of them?

What have you looked for lately? Did you find all of it? Or only some of it?

New words appear everyday.

You are already behind. You will get further behind using search.

Visualizing Data Loss From Search

Thursday, April 14th, 2016

I used searches for “duplicate detection” (3,854) and “coreference resolution” (3290) in “Ironically, Entity Resolution has many duplicate names” [Data Loss] to illustrate potential data loss in searches.

Here is a rough visualization of the information loss if you use only one of those terms:

If you search for “duplicate detection,” you miss all the articles shaded in blue.

If you search for “coreference resolution,” you miss all the articles shaded in yellow.

Suggestions for improving this visualization?

It is a visualization that could be performed on client’s data, using their search engine/database.

In order to identify the data loss they are suffering now from search across departments.

With the caveat that not all data loss is bad and/or worth avoiding.

Imaginary example (so far): What if you could demonstrate no overlapping of terminology for two vendors for the United States Army and the Air Force. That is no query terms for one returned useful results for the other.

That is a starting point for evaluating the use of topic maps.

While the divergence in terminologies is a given, the next question is: What is the downside to that divergence? What capability is lost due to that divergence?

Assuming you can identify such a capacity, the next question is to evaluate the cost of reducing and/or eliminating that divergence versus the claimed benefit.

I assume the most relevant terms are going to be those internal to customers and/or potential customers.

Interest in working this up into a client prospecting/topic map marketing tool?

Separately I want to note my discovery (you probably already knew about it) of VennDIS: a JavaFX-based Venn and Euler diagram software to generate publication quality figures. Download here. (Apologies, the publication itself if firewalled.)

The export defaults to 800 x 800 resolution. If you need something smaller, edit the resulting image in Gimp.

It’s a testimony to the software that I was able to produce a useful image in less than a day. Kudos to the software!

“Ironically, Entity Resolution has many duplicate names” [Data Loss]

Wednesday, April 13th, 2016

Nancy Baym tweeted:

“Ironically, Entity Resolution has many duplicate names” – Lise Getoor

I can’t think of any subject that doesn’t have duplicate names.

Can you?

In a “search driven” environment, not knowing the “duplicate” names for a subject means data loss.

Data loss that could include “smoking gun” data.

Topic mappers have been making that pitch for decades but it never has really caught fire.

I don’t think anyone doubts that data loss occurs, but the gravity of that data loss remains elusive.

For example, let’s take three duplicate names for entity resolution from the slide, duplicate detection, reference reconciliation, coreference resolution.

Supplying all three as quoted strings to CiteSeerX, any guesses on the number of “hits” returned?

As of April 13, 2016:

• duplicate detection – 3,854
• reference reconciliation – 253
• coreference resolution – 3,290

When running the query "duplicate detection" "coreference resolution", only 76 “hits” are returned, meaning that there are only 76 overlapping cases reported in the total of 7,144 for both of those terms separately.

That’s assuming CiteSeerX isn’t shorting me on results due to server load, etc. I would have to cross-check the data itself before I would swear to those figures.

But consider just the raw numbers I report today: duplicate detection – 3,854, coreference resolution – 3,290, with 76 overlapping cases.

That’s two distinct lines of research on the same problem, for the most part, ignoring the other.

What do you think the odds are of duplication of techniques, experiences, etc., spread out over those 7,144 articles?

Instead of you or your client duplicating a known-to-somebody solution, you could be building an enhanced solution.

Well, except for the data loss due to “duplicate names” in a search environment.

And that you would have to re-read all the articles in order to find which technique or advancement was made in each article.

Multiply that by everyone who is interested in a subject and its a non-trivial amount of effort.

How would you like to avoid data loss and duplication of effort?

Coeffects: Context-aware programming languages – Subject Identity As Type Checking?

Tuesday, April 12th, 2016

From the webpage:

Coeffects are Tomas Petricek‘s PhD research project. They are a programming language abstraction for understanding how programs access the context or environment in which they execute.

The context may be resources on your mobile phone (battery, GPS location or a network printer), IoT devices in a physical neighborhood or historical stock prices. By understanding the neighborhood or history, a context-aware programming language can catch bugs earlier and run more efficiently.

This page is an interactive tutorial that shows a prototype implementation of coeffects in a browser. You can play with two simple context-aware languages, see how the type checking works and how context-aware programs run.

This page is also an experiment in presenting programming language research. It is a live environment where you can play with the theory using the power of new media, rather than staring at a dead pieces of wood (although we have those too).

(break from summary)

Programming languages evolve to reflect the changes in the computing ecosystem. The next big challenge for programming language designers is building languages that understand the context in which programs run.

This challenge is not easy to see. We are so used to working with context using the current cumbersome methods that we do not even see that there is an issue. We also do not realize that many programming features related to context can be captured by a simple unified abstraction. This is what coeffects do!

What if we extend the idea of context to include the context within which words appear?

For example, writing a police report, the following sentence appeared:

There were 20 or more <proxy string=”black” pos=”noun” synonym=”African” type=”race”/>s in the group.

For display purposes, the string value “black” appears in the sentence:

There were 20 or more blacks in the group.

But a search for the color “black” would not return that report because the type = color does not match type = race.

On the other hand, if I searched for African-American, that report would show up because “black” with type = race is recognized as a synonym for people of African extraction.

Inline proxies are the easiest to illustrate but that is only one way to serialize such a result.

If done in an authoring interface, such an approach would have the distinct advantage of offering the original author the choice of subject properties.

The advantage of involving the original author is that they have an interest in and awareness of the document in question. Quite unlike automated processes that later attempt annotation by rote.

No Perception Without Cartography [Failure To Communicate As Cartographic Failure]

Saturday, April 9th, 2016

Dan Klyn tweeted:

No perception without cartography

with an image of this text (from Self comes to mind: constructing the conscious mind by Antonio R Damasio):

The nonverbal kinds of images are those that help you display mentally the concepts that correspond to words. The feelings that make up the background of each mental instant and that largely signify aspects of the body state are images as well. Perception, in whatever sensory modality, is the result of the brain’s cartographic skill.

Images represent physical properties of entities and their spatial and temporal relationships, as well as their actions. Some images, which probably result from the brain’s making maps of itself making maps, are actually quite abstract. They describe patterns of occurrence of objects in time and space, the spatial relationships and movement of objects in terms of velocity and trajectory, and so forth.

Dan’s tweet spurred me to think that our failures to communicate to others could be described as cartographic failures.

If we use a term that is unknown to the average reader, say “daat,” the reader lacks a mental mapping that enables interpretation of that term.

Even if you know the term, it doesn’t stand in isolation in your mind. It fits into a number of maps, some of which you may be able to articulate and very possibly into other maps, which remain beyond your (and our) ken.

Not that this is a light going off experience for you or me but perhaps the cartographic imagery may be helpful in illustrating both the value and the risks of topic maps.

The value of topic maps is spoken of often but the risks of topic maps rarely get equal press.

How would topic maps be risky?

Felienne Hermans in Spreadsheets: The Ununderstood Dark Matter of IT makes a persuasive case that spreadsheets are on an average five years old with little or no documentation.

If those spreadsheets remain undocumented, both users and auditors are equally stymied by their ignorance, a cartographic failure that leaves both wondering what must have been meant by columns and operations in the spreadsheet.

To the extent that a topic map or other disclosure mechanism preserves and/or restores the cartography that enables interpretation of the spreadsheet, suddenly staff are no longer plausibly ignorant of the purpose or consequences of using the spreadsheet.

Facile explanations that change from audit to audit are no longer possible. Auditors are chargeable with consistent auditing from one audit to another.

Does it sound like there is going to be a rush to use topic maps or other mechanisms to make spreadsheets transparent?

Still, transparency that befalls one could well benefit another.

Or to paraphrase King David (2 Samuel 11:25):

Ready to inflict transparency on others?

“No One Willingly Gives Away Power”

Friday, April 8th, 2016

Matthew Schofield in European anti-terror efforts hobbled by lack of trust, shared intelligence hits upon the primary reason for resistance to topic maps and other knowledge integration technologies.

Especially in intelligence, knowledge is power. No one willingly gives away power.” (Magnus Ranstorp, Swedish National Defense University)

From clerks who sort mail to accountants who cook the books to lawyers that defend patents and everyone else in between, everyone in an enterprise has knowledge, knowledge that gives them power others don’t have.

Topic maps have been pitched on a “greater good for the whole” basis but as Magnus points out, who the hell really wants that?

When confronted with a new technique, technology, methodology, the first and foremost question on everyone’s mind is:

Do I have more/less power/status with X?

A

approach loses power.

A

approach gains power.

Relevant lyrics:

Oh, there ain’t no rest for the wicked
Money don’t grow on trees
I got bills to pay
I got mouths to feed
No I can’t slow down
I can’t hold back
Though you know I wish I could
No there ain’t no rest for the wicked
Until we close our eyes for good

Sell topic maps to increase/gain power.

PS: Keep the line, “No one willingly gives away power” in discussions of why the ICIJ refuses to share the Panama Papers with the public.

Pentagon Confirms Crowdsourcing of Map Data

Tuesday, April 5th, 2016

I have mentioned before, Tracking NSA/CIA/FBI Agents Just Got Easier, The DEA is Stalking You!, how citizens can invite federal agents to join the gold fish bowl being prepared for the average citizen.

Of course, that’s just me saying it, unless and until the Pentagon confirms the crowdsourcing of map data!

“What a great idea if we can get our soldiers adding fidelity to the maps and operational picture that we already have” in Defense systems, Gordon told Nextgov. “All it requires is pushing out our product in a manner that they can add data to it against a common framework.”

Comparing mapping parties to combat support activities, she said, soldiers are deployed in some pretty remote areas where U.S. forces are not always familiar with the roads and the land, partly because they tend to change.

If troops have a base layer, “they can do basically the same things that that social party does and just drop pins and add data,” Gordon said from a meeting room at the annual Esri conference. “Think about some of the places in Africa and some of the less advantaged countries that just don’t have addresses in the way we do” in the United States.

Of course, you already realize the value of crowd-sourcing surveillance of government agents but for the c-suite crowd, confirmation from a respected source (the Pentagon) may help push your citizen surveillance proposal forward.

BTW, while looking at Army GeoData research plans (pages 228-232), I ran across this passage:

This effort integrates behavior and population dynamics research and analysis to depict the operational environment including culture, demographics, terrain, climate, and infrastructure, into geospatial frameworks. Research exploits existing open source text, leverages multi-media and cartographic materials, and investigates data collection methods to ingest geospatial data directly from the tactical edge to characterize parameters of social, cultural, and economic geography. Results of this research augment existing conventional geospatial datasets by providing the rich context of the human aspects of the operational environment, which offers a holistic understanding of the operational environment for the Warfighter. This item continues efforts from Imagery and GeoData Sciences, and Geospatial and Temporal Information Structure and Framework and complements the work in PE 0602784A/Project T41.

Doesn’t that just reek with subjects that would be identified differently in intersecting information systems?

One solution would be to fashion top down mapping systems that are months if not years behind demands in an operational environment. Sort of like tanks that overheat in jungle warfare.

Or you could do something a bit more dynamic that provides a “good enough” mapping for operational needs and yet also has the information necessary to integrate it with other temporary solutions.

Pardon the Intermission

Friday, March 18th, 2016

Apologies for the absence of posts starting on March 15, 2016 until this one today.

I made an unplanned trip to the local hospital via ambulance around 8:00 AM on the 15th and managed to escape on the afternoon of March 17, 2016.

On the downside I didn’t have anyway to explain my sudden absence from the Net.

On the upside I had a lot of non-computer assisted time to think about topic maps, etc., while being poked, prodded, waiting for lab results, etc.

Not to mention I re-read the first two Harry Potter books. 😉

I have one interesting item for today and will be posting about my non-computer assisted thinking about topic maps in the near future.