Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

Semantic Data Integration For Free With IO Informatics’ Knowledge Explorer Personal Edition

From the post:

Bioinformatics software provider IO Informatics recently released its free Knowledge Explorer Personal Edition. Version 3.6 of the Personal Edition can handle most of what Knowledge Explorer Professional 3.6, launched in October, can, but it does all its work in memory without direct connectivity to a back-end database.

“In particular, a lot of the strengths of Knowledge Explorer have to do with modeling data as RDF and then testing queries, visualizing and browsing the data to see that you have the ontologies and data mappings you need for your integration and application requirements.” says Robert Stanley, IO Informatics president and CEO. The Personal version is aimed at academic experts focused on data integration and semantic data modeling, as well as personal power users in life sciences and other data-intensive industries, or anyone who wants to learn the tool in anticipation of leveraging their enterprise data sets for collaboration and integration projects.

The latest Knowledge Explorer 3.6 feature set extends the thesaurus application in the product, so that users can bring in additional thesauri and vocabularies, as well as the user interaction options for importing, merging and modifying ontologies. For the Pro edition, IO Informatics has also been working with database vendors to increase query speed and loading.

I am not sure what we did collectively to merit presents so early in the holiday seasons but I won’t spend a lot of time worrying about it.

Particularly interested in the “…additional thesauri and vocabularies…” aspect of the software. In part because it isn’t that big a step to a topic map to add in which could help provide context and other factors to better enable integration of information.

Oh, and from further down on the webpage:

Stanley sees a number of potential applications for those who might like to try the Personal version for integrating and modeling smaller data sets. “Maybe a customer has a number of reports on protein expression experiments and lot of clinical data associated with that, including healthcare records and various report spreadsheets, and they must integrate those to do some research for themselves or their internal customers,” he says, as one example. “You can do that even using the Personal version to create a well integrated, semantically formatted file.”

Sure and when researchers move on, how do their successors maintain those integrations? Inquiring minds want to know? What do we do about semantic rot?

Classification and Novel Class Detection in Data Streams with Active Mining Authors(s): Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, Bhavani Thuraisingham

Abtract:

We present ActMiner, which addresses four major challenges to data stream classification, namely, infinite length, concept-drift, concept-evolution, and limited labeled data. Most of the existing data stream classification techniques address only the infinite length and concept-drift problems. Our previous work, MineClass, addresses the concept-evolution problem in addition to addressing the infinite length and concept-drift problems. Concept-evolution occurs in the stream when novel classes arrive. However, most of the existing data stream classification techniques, including MineClass, require that all the instances in a data stream be labeled by human experts and become available for training. This assumption is impractical, since data labeling is both time consuming and costly. Therefore, it is impossible to label a majority of the data points in a high-speed data stream. This scarcity of labeled data naturally leads to poorly trained classifiers. ActMiner actively selects only those data points for labeling for which the expected classification error is high. Therefore, ActMiner extends MineClass, and addresses the limited labeled data problem in addition to addressing the other three problems. It outperforms the state-of-the-art data stream classification techniques that use ten times or more labeled data than ActMiner.

I would have liked this article better had it not said that the details of the test data could be found in another article.

Specifically: Masud, M.M., Gao, J., Khan, L., Han, J., Thuraisingham, B.M.: “Integrating novel class detection with classification for concept-drifting data streams.” In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD c 2009. LNCS, vol. 5782, pp. 79–94. Springer, Heidelberg (2009)

Which directed me to: “Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams,” http://www.utdallas.edu/?mmm058000/reports/UTDCS-13-09.pdf

I leave it as an exercise for the readers to guess the names of the authors of the last paper.

Otherwise interesting research marred by presentation in dribs and drabs.

Now that I have all three papers I will have to see what questions arise, other than questionable publishing practices.

Searching with Tags: Do Tags Help Users Find Things? Authors: Margaret E.I. Kipp, and D. Grant Campbell

Abstract:

This study examines the question of whether tags can be useful in the process of information retrieval. Participants searched a social bookmarking tool specialising in academic articles (CiteULike) and an online journal database (Pubmed). Participant actions were captured using screen capture software and they were asked to describe their search process. Users did make use of tags in their search process, as a guide to searching and as hyperlinks to potentially useful articles. However, users also made use of controlled vocabularies in the journal database to locate useful search terms and of links to related articles supplied by the database.

Good review of the literature, such as it is, on use of user supplied tagging for searching.

Worth reading on the question raised about the use of tags but there is another question lurking in the background.

The authors say in various forms:

The ability to discover useful resources is of increasing importance where web searches return 300 000 (or more) sites of unknown relevance and is equally important in the realm of digital libraries and article databases. The question of the ability to locate information is an old one and led directly to the creation of cataloguing and classification systems for the organisation of knowledge. However, such systems have not proven to be truly scalable when dealing with digital information and especially information on the web.

Since at least 1/3 of the web is pornography and that is not usually relevant to scientific, technical or medical searching, we can reduce the searching problem by 1/3 right there. I don’t know the percentage for shopping, email archives, etc., but when you come down to the “core” literature for field, it really isn’t all that large is it?

Questions:

1. Do search applications need to “scale” to web size or just enough to cover “core” literature? (discussion)
2. For library science, how would you go about constructing a list of the “core” literature? (3-5 pages, no citations)
3. If you use tagging, describe your experience with assigning tags. (3-5 pages, no citations)
4. If you use tagging for searching purposes, describe your experience (3-5 pages, no citations)

First seen at: ResourceBlog

Google Refine 2.0 has been released.

From the website:

Google Refine is a power tool for working with messy data sets, including cleaning up inconsistencies, transforming them from one format into another, and extending them with new data from external web services or other databases. Version 2.0 introduces a new extensions architecture, a reconciliation framework for linking records to other databases (like Freebase), and a ton of new transformation commands and expressions.

Freebase Gridworks 1.0 has already been well received by the data journalism and open government data communities (you can read how the Chicago Tribune, ProPublica and data.gov.uk have used it) and we are very excited by what they and others will be able to do with this new release. To learn more about what you can do with Google Refine 2.0, watch…[screencasts]

If you don’t watch any videos this month, you have to watch http://www.youtube.com/watch?v=m5ER2qRH1OQ!

Google uses the term reconciliation but what is being demonstrated is mapping information to a subject representative.

Note that unlike topic maps, the basis (read properties) for that mapping is not disclosed, so it isn’t possible for a program or person to be sure to repeat the same mapping.

The Linking Open Data cloud diagram is maintained by This page is maintained by Richard Cyganiak and Anja Jentzsch.

I suppose having DBpedia at the center of linked data is better than the CIA Factbook. 😉

I find large visualizations like this one useful as marketing tools or “that’s cool” examples, but not terribly useful for actual analysis.

Has your experience been different?

Apologies for the lack of a post for August 18, 2010.

I was working on a post late yesterday evening when my ISP lost connectivity to the Net. 🙁

I could not stay up late enough to see if it would be repaired before the end of the day.

Hence, no post for August 18, 2010.

Have a lot of stuff in the queue so will try to get an early post out most days.

PGS – Pretty Good Semantics is the result of months of conversation with Sam Hunting.

Our starting premise: Users want to say things of interest to them, as simply as possible, for them.

Note the focus on users. Not on description logic. Not on formal ontologies. Not on reasoning, artificial or otherwise. Not even on complex mappings between identifications. But on users.

All of those other things are worthwhile enterprises, some of them anyway, which you can pursue your own leisure.

The question is how to empower users to say things about what interests them? And if possible, how to do so without re-writing the WWW to deal with 303 clouds, etc. ?

Our answer to those questions: PGS – Pretty Good Semantics. It asks very little of users yet can annotate any identifier on the WWW to say whatever a user likes.

It uses existing HTML techniques and works with existing web servers and search engines.

Enjoy!

In an exchange over a MapReduce resource, Robert Barta observed how large that ecosystem has grown in just a year, and suggested there is a lesson for the TM community in that growth. But what lesson is that? (He didn’t say, but I have written to ask.)

“MapReduce” isn’t a cooler a name than “Topic Maps” so that’s not lesson.

MapReduce isn’t less complex than topic maps so that’s not the lesson as well.

Two issues that MapReduce does not face:

1. Users resisted (and still do resist) markup because it requires making explicit choices about the structure of a text. We learn text structures from users, but for the most part, they are reluctant to name those parts. Is there an analogy to making subjects explicit for a topic map?
2. If we identify our subjects (our insider vocabulary), then what makes us special will be known by others.

MapReduce doesn’t face the first issue because users can create whatever mapping they wish, without ever saying explicitly what subjects are involved. It also preserves the special nature of insider vocabularies since it has no explicit mechanism for identifying subjects.

Are those the lessons? If they are, are there work arounds? Are there other lessons?

Pragmatic Topic Map Streaming by Jan Schreiber raises some interesting questions about how to construct a data stream for a topic map.

I particularly like the idea of creating mini-topic maps as it were. See his post for the details.

He did not touch on was how topic map stream software would recognize subjects. A topic map stream creator with a configurable subject recognition would be really useful. Most of us could use the “topic maps subjects” recognition filter while others, interested in dull subjects like the World Cup (just teasing) could have a subject filter for it. Some of us could have both, feeding different topic maps.

Constructions from Dots and Lines by Marko A. Rodriguez and Peter Neubauer is an engaging introduction to graphs and why they are important.

Abstract:

A graph is a data structure composed of dots (i.e. vertices) and lines (i.e. edges). The dots and lines of a graph can be organized into intricate arrangements. The ability for a graph to denote objects and their relationships to one another allow for a surprisingly large number of things to be modeled as a graph. From the dependencies that link software packages to the wood beams that provide the framing to a house, most anything has a corresponding graph representation. However, just because it is possible to represent something as a graph does not necessarily mean that its graph representation will be useful. If a modeler can leverage the plethora of tools and algorithms that store and process graphs, then such a mapping is worthwhile. This article explores the world of graphs in computing and exposes situations in which graphical models are beneficial.

JErlang: Erlang with Joins by Hubert Ploiniczak should interest anyone implementing distributed topic map systems.

The value of having a distributed architecture (did I hear “Internet?”) has been lost on the Semantic Web advocates. With topic maps you can have multiple locations that “resolve” identifiers to other identifiers and pass on information about something that has been identified.

Most existing topic maps look like data silos but that is more a matter of habit than architectural limitation.

I should put in a plug for the Springer Alert Service, which brought the article with the same title, JErlang: Erlang with Joins to my attention. Highly recommended as a way to stay current on the latest CS research. Remember articles don’t have to say “topic map” in the title or abstract to be relevant.

PS: Topic map observations: The final report and article have the same name. In topic maps the different locations for the items would be treated as subject locators, thus allowing them to retain the same name but being distinguished one from the other. Note that the roles differ with the two subjects as well. Susan Eisenbach is the supervisor of the final report and is a co-author of the article reported by Springer.

Jack Park points to The Forth Paradigm: Data-Intensive Scientific Discovery as a book that merits our attention.

Indeed it does! Lurking just beneath the surface of data-intensive research are questions of semantics. Diverse semantics. How does data-intensive research occur in a multi-semantic world?

Paul Ginsparg (Cornell University), in Text in a Data-centric World, has the usual genuflection towards “linked data” without stopping to consider the cost of evaluating every URI to decide if it is an identifier or a resource. Nor why adding one more name to the welter of names we have now (that is the semantic diversity problem) is going to make our lives any better?

Ginsparg writes:

Such an articulated semantic structure [linked data] facilitates simpler algorithms acting on World Wide Web text and data and is more feasible in the near term than building a layer of complex artificial intelligence to interpret free-form human ideas using some probabilistic approach.

Solving the “perfect language” problem, which has never been solved, is more feasible than “…building a layer of complex artificial intelligence to interpret free-form human ideas using some probabilistic approach” to solve it for us?

Perhaps so but one wonders why that is a useful observation?

On the “perfect language” problem, see The Search for the Perfect Language by Umberto Eco.

The Future of the Journal is another slide deck by Anita de Waard that reads like a promotional piece for topic maps, sans any mention of topic maps.

While Anita makes a strong case for annotation of data in science publishing, the same is true for government, legal, environmental, business, finance, etc., publications. All publications are as complex as depicted on these slides. It isn’t as obvious in the humanities because that “data” has been locked away so long that we have forgotten it is there.

The more complex the information we record, via “annotations” or some other mechanism, the greater the need for librarians to organize it and help us find it. Self-help in research is like the guy about to do a self-appendectomy with his doctor’s advice over the phone. Doable, maybe, but the results are pretty much what you would expect.

Rather than future of the journal, I would say: Future of Information.