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

July 16, 2010

Lesson for Topic Maps?

Filed under: Uncategorized — Patrick Durusau @ 8:10 am

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?

July 15, 2010

Designing A Successful Topic Map Interface

Filed under: Interface Research/Design — Patrick Durusau @ 5:34 pm

Eugene Agichtein and Qi Guo have developed:

a new class of search behavior models that also exploit fine-grained user interactions with the search results. We show that mining these interactions, such as mouse movements and scrolling, can enable more effective detection of the user’s search goals.

Their paper, Ready to Buy or Just Browsing? Detecting Web Searcher Goals from Interaction Data describes how light-weight mouse tracking can yield valuable information about users. (Contrast that with expensive eye tracking approaches.)

If you like that paper, see: Inferring Web Searcher Intent Tutorial and the bibliography of publications.

The design of a successful topic map interface is going to start and stop with user preferences. How fast or clever your topic map application may be, if users don’t want to use it, they won’t. That, by the way, is the definition of a unsuccessful application.

July 14, 2010

Are simplified hadoop interfaces the next web cash cow? – Post

Filed under: Hadoop,Legends,MapReduce,Semantic Diversity,Subject Identity — Patrick Durusau @ 12:06 pm

Are simplified hadoop interfaces the next web cash cow? is a question that Brian Breslin is asking these days.

It isn’t that hard to imagine that not only Hadoop interfaces being cash cows but also canned analysis of public date sets that can be incorporated into those interfaces.

But then the semantics question comes back up when you want to join that canned analysis to your own. What did they mean by X? Or Y? Or for that matter, what are the semantics of the data set?

But we can solve that issue by explicit subject identification! Did I hear someone say topic maps? 😉 So our identifications of subjects in public data sets will themselves become a commodity. There could be competing set-similarity analysis of  public data sets.

If a simplified Hadoop interface is the next cash cow, we need to be ready to stuff it with data mapped to subject identifications to make it grow even larger. A large cash cow is a good thing, a larger cash cow is better and a BP-sized cash cow is just about right.

Coreference via substitution rules – Post

Filed under: Mapping,OWL — Patrick Durusau @ 11:16 am

Coreference via substitution rules by Bernard Vatant develops two interesting notions:

  • Using substitution to test interchange of references
  • Using operational rules rather than declarative assertions

See his post for the full details.

He uses context to define when one reference could be substituted for another.

Also observes that any mapping, such as owl:sameAs can be abused.

As with many things, semantic integration may not be as much a technical issue but a human one. Semantic integration tools aren’t going to lead to semantic integration unless we use them with semantic integration as a goal.

July 13, 2010

The FLAMINGO Project on Data Cleaning – Site

Filed under: Data Integration,Data Mining,Heterogeneous Data,Information Retrieval,MapReduce,Semantic Diversity,Software — Patrick Durusau @ 5:28 am

The FLAMINGO Project on Data Cleaning is the other project that has influenced the self-similarity work with MapReduce.

From the project description:

Supporting fuzzy queries is becoming increasingly more important in applications that need to deal with a variety of data inconsistencies in structures, representations, or semantics. Many existing algorithms require an offline analysis of data sets to construct an efficient index structure to support online query processing. Fuzzy join queries of data sets are more time consuming due to the computational complexity. The PI is studying three research problems: (1) constructing high-quality inverted lists for fuzzy search queries using Hadoop; (2) supporting fuzzy joins of large data sets using Hadoop; and (3) using the developed techniques to improve data quality of large collections of documents.

See the project webpage to learn more about their work on “us[ing] limited programming primitives in the cloud to implement index structures and search algorithms.”

The relationship between “dirty” data and the increase in data overall is at least linear, but probably worse. Far worse. Whether data is “dirty” depends on your perspective. The more data that appears on “***” format (fill in the one you like the least) the dirtier the universe of data has become. “Dirty” data will be with you always.

ASTERIX: A Highly Scalable Parallel Platform for Semistructured Data Management and Analysis – SITE

Filed under: Data Integration,Heterogeneous Data,Information Retrieval,MapReduce,Search Engines,Software — Patrick Durusau @ 5:21 am

ASTERIX: A Highly Scalable Parallel Platform for Semistructured Data Management and Analysis is one of the projects behind the self-similarity and MapReduce posting.

From the project page:

The ASTERIX project is developing new technologies for ingesting, storing, managing, indexing, querying, analyzing, and subscribing to vast quantities of semi-structured information. The project is combining ideas from three distinct areas – semi-structured data, parallel databases, and data-intensive computing – to create a next-generation, open source software platform that scales by running on large, shared-nothing computing clusters.

Home of Hydrax Hyrax: Demonstrating a New Foundation for Data-Parallel Computation, “out-of-the-box support for common distributed communication patterns and set-oriented data operators.” (Need I say more?)

July 12, 2010

Set-Similarity and Topic Maps

Filed under: Mapping,Merging,TMRM,Topic Maps — Patrick Durusau @ 7:09 pm

The set-similarity offers a useful way to think about merging in a topic maps context. The measure of self-similarity that we want for merging in topic maps is the same subject.

Self-similarity, in the TMDM, for topics is:

  • at least one equal string in their [subject identifiers] properties,
  • at least one equal string in their [item identifiers] properties,
  • at least one equal string in their [subject locators] properties,
  • an equal string in the [subject identifiers] property of the one topic item and the [item identifiers] property of the other, or
  • the same information item in their [reified] properties.

The research literature makes it clear that judging self-similarity isn’t subject to one test or even a handful of them for all purposes. Not to mention that more often than not, self-similarity is being judged on high dimensional data.

Despite clever approaches and quite frankly amazing results, I have yet to run across sustained discussion of how to interchange self-similarity tests. Perhaps it is my markup background but that seems like the sort of capability that would be widely desired.

The issue of interchangeable self-similarity tests looks like an area where JTC 1/SC 34/WG 3 could make a real contribution.

July 11, 2010

Efficient Parallel Set-Similarity Joins Using MapReduce

Filed under: Data Integration,Heterogeneous Data,Information Retrieval,MapReduce,Semantic Diversity,Software — Patrick Durusau @ 9:36 am

Efficient Parallel Set-Similarity Joins Using MapReduce by Rares Vernica, Michael J. Carey, and, Chen Li, Department of Computer Science, University of California, Irvine, used Citeseer (1.3M publications) and DBLP (1.2M publications) and “…increased their sizes as needed.”

The contributions of this paper are:

  • “We describe efficient ways to partition a large dataset across nodes in order to balance the workload and minimize the need for replication. Compared to the equi-join case, the set-similarity joins case requires “partitioning” the data based on set contents.
  • We describe efficient solutions that exploit the MapReduce framework. We show how to efficiently deal with problems such as partitioning, replication, and multiple
    inputs by manipulating the keys used to route the data in the framework.
  • We present methods for controlling the amount of data kept in memory during a join by exploiting the properties of the data that needs to be joined.
  • We provide algorithms for answering set-similarity self-join queries end-to-end, where we start from records containing more than just the join attribute and end with actual pairs of joined records.
  • We show how our set-similarity self-join algorithms can be extended to answer set-similarity R-S join queries.
  • We present strategies for exceptional situations where, even if we use the finest-granularity partitioning method, the data that needs to be held in the main memory of one node is too large to fit.”

A number of lessons and insights relevant to topic maps in this paper.

Makes me think of domain specific (as well as possibly one or more “general”) set-similarity join interchange languages! What are you thinking of?

NTCIR (NII Test Collection for IR Systems) Project

Filed under: Conferences,Heterogeneous Data,Information Retrieval,Search Engines,Software — Patrick Durusau @ 7:47 am

NTCIR (NII Test Collection for IR Systems) Project focuses on information retrieval tasks in Japanese, Chinese, Korean, English and cross-lingual information retrieval.

From the project description:

For the laboratory-typed testing, we have placed emphasis on (1) information retrieval (IR) with Japanese or other Asian languages and (2) cross-lingual information retrieval. For the challenging issues, (3) shift from document retrieval to “information” retrieval and technologies to utilizing information in the documents, and (4) investigation for realistic evaluation, including evaluation methods for summarization, multigrade relevance judgments and single-numbered averageable measures for such judgments, evaluation methods suitable for retrieval and processing of particular document-genre and its usage of the user group of the genre and so on.

I know there are active topic map communities in both Japan and Korea. Perhaps this is a place to meet researchers working on issues closely similar to those in topic maps and to discuss the contribution that topic maps have to offer.

Forum for Information Retrieval Evaluation (FIRE)

Filed under: Conferences,Heterogeneous Data,Information Retrieval,Search Engines,Software — Patrick Durusau @ 6:44 am

Forum for Information Retrieval Evaluation (FIRE)  aims:

  • to encourage research in South Asian language Information Access technologies by providing reusable large-scale test collections for ILIR experiments
  • to explore new Information Retrieval / Access tasks that arise as our information needs evolve, and new needs emerge
  • to provide a common evaluation infrastructure for comparing the performance of different IR systems
  • to investigate evaluation methods for Information Access techniques and methods for constructing a reusable large-scale data set for ILIR experiments.

I know there is a lot of topic map development in South Asia and this looks like a great place to meet current researchers and to interest others in topic maps.

INEX: Initiative for Evaluation of XML Retrieval

Filed under: Conferences,Heterogeneous Data,Information Retrieval,Search Engines,Software — Patrick Durusau @ 6:30 am

INEX: Initiative for Evaluation of XML Retrieval is another must-see for serious topic map researchers.

No surprise that my first stop was the iNEX Publications page with proceedings from 2002-date.

However, INEX offers an opportunity for evaluation of topic maps in the context of other solutions, providing that one or more of us participate in the initiative.

If you or your institution decided to participate, please let others in the community know. I for one would like to join such an effort.

UCI ISG Lecture Series on Scalable Data Management

Filed under: Information Retrieval,MapReduce,Searching,Semantics,SQL — Patrick Durusau @ 5:39 am

UCI ISG Lecture Series on Scalable Data Management is simply awesome! Slides and videos you will find:

  • Teradata Past, Present and Future Todd Walter, CTO, R&D, Teradata
  • Hadoop: Origins and Applications Chris Smith, Xavier Stevens and John Carnahan, FOX Audience Network
  • Pig: Building High-Level Dataflows over Map-Reduce Utkarsh Srivastava, Senior Research Scientist, Yahoo!
  • Database Scalability and Indexes Goetz Graefe, HP Fellow, Hewlett-Packard Laboratories
  • Cloud Data Serving: Key-Value Stores to DBMSs Raghu Ramakrishnan, Chief Scientist for Audience & Cloud Computing, Yahoo!
  • Scalable Data Management at Facebook Srinvas Narayanan, Software Engineer, Facebook
  • SCOPE: Parallel Data Processing of Massive Data Sets Jingren Zhou, Researcher, Microsoft
  • What We Got Right, What We Got Wrong: The Lessons I Learned Building a Large-Scale DBMS for XML. Mary Holstege, Principal Engineer, Mark Logic
  • Scalable Data Management with DB2 Matthias Nicola, DB2 pureXML Architect, IBM
  • SQL Server: A Data Platform for Large-Scale Applications José Blakeley, Partner Architect, Microsoft
  • Data in the Cloud: New Challenges or More of the Same? Divy Agrawal, Professor of Computer Science, UC Santa Barbara

Subject identity is as important in the realm of big data/table/etc. as it is anywhere.

It is our choice if topic maps are going to step up to the challenge.

That is going to require reaching out and across communities and becoming pro-active with regard to new opportunities and possibilities.

This resource was brought to my notice by Jack Park. Jack delights in sending these highly relevant and often quite large resource listings my way (and to be honest, I return the favor).

July 10, 2010

JISC and OCLC profile the digital information seeker – Post

Filed under: Marketing,Searching,Usability — Patrick Durusau @ 9:31 am

JISC and OCLC profile the digital information seeker, a post from federatedsearchblog.com has a great summary of a report that summarizes how the way people look for information is changing.

Read this post and then watch the podcast What does the digital information seeker look like?

Full details at: Digital information seekers: How academic libraries can support the use of digital resources.

A reply I got to suggesting asking users about their needs:

I have never heard of an inventor making surveys to test things out. That is nonsense. At most what that can tell you is little details, ways to fine tune a system. It will never let you see the big changes coming.

The average user has at least as much imagination as would be tyrants of the WWW have arrogance, if not more.

I am going to ignore that advice and think you should as well.

Knowledge-Based Systems – Journal

Filed under: Information Retrieval,Software — Patrick Durusau @ 7:51 am

Knowledge-Based Systems is described on its homepage:

Knowledge-Based Systems is the international, interdisciplinary and applications-oriented journal on KBS.

Knowledge-Based Systems focuses on systems that use knowledge-based techniques to support human decision-making, learning and action. Such systems are capable of cooperating with human users and so the quality of support given and the manner of its presentation are important issues. The emphasis of the journal is on the practical significance of such systems in modern computer development and usage.

As well as being concerned with the implementation of knowledge-based systems, the journal covers the design process, the matching of requirements and needs to delivered systems and the organisational implications of introducing such technology into the workplace and public life, expert systems, application of knowledge-based methods, integration with conventional technologies, software tools for KBS construction, decision-support mechanisms, user interactions, organisational issues, knowledge acquisition, knowledge representation, languages and programming environments, knowledge-based implementation techniques and system architectures. Also included are publication reviews.

Forthcoming articles include:

  • Grammar-Based Geodesics in Semantic Networks
  • Hy-SN: Hyper-graph based Semantic Network
  • A Semantic Backend for Content Management Systems
  • Research on the Model of Rough Set over Dual-universes

Definitely should be on every topic map researcher’s current awareness list.

July 9, 2010

“I say toh-mah-toh, you say toh-may-toh”

Filed under: Citation Indexing,Fuzzy Sets,Topic Maps — Patrick Durusau @ 8:21 pm

Rough Fuzzies, and Beyond? I thought was a cute title.

But just scratching the surface in the area of rough sets and I find:

  • generalized fuzzy belief functions
  • generalized fuzzy rough approximation operators
  • fuzzy coverings
  • granular computing
  • training fuzzy systems
  • fuzzy generalization of rough sets
  • generalized fuzzy rough sets
  • fuzzy concept lattices
  • fuzzy implication operators
  • intuitionistic fuzzy implicators

How many of those would you think to search for?

Same semantic issues topic maps are designed to help resolve. But, resolving them means someone (err, that would be those of us interested in the area) have to undertake the real work to resolve those semantic issues.

The obvious answer is some robust system that allows tweets, instant messages, email (properly formatted), as well as updating protocols to update a topic map in real time. That is also an unlikely solution.

Suggestion:

What about an easier to reach solution? Lutz Maicher’s bibmap is a likely starting point.

We would have to ask Lutz about merging in additional data but I suspect he would be amenable to the suggestion.

Building a robust bibliography of topic map relevant materials would occupy us while waiting on more futuristic solutions.

July 8, 2010

Keeping Up With The “Competition”

Filed under: RDF,Semantic Web — Patrick Durusau @ 8:29 pm

New opportunities for linked data nose-following is a blog post from the W3C about three (3) new IETF RFCs.

Well, or at least two of them. As of my 8:55 PM local, 2010-07-08, “Defining Well-Known URIs” has the following URI, http://www.ietf.org/html/draft-nottingham-site-meta-05. Err, that doesn’t look right.

When it didn’t resolve I thought perhaps it was a redirect.

Nothing that complicated, just a bad URI. I got the IETF “404: Page Not Found” page.

Oh, the correct URI? Defining Well-Known URIs, http://www.rfc-editor.org/rfc/rfc5785.txt.

So, what is a well-known URI?

A well-known URI is a URI [RFC3986] whose path component begins with
the characters “/.well-known/”, and whose scheme is “HTTP”, “HTTPS”,
or another scheme that has explicitly been specified to use well-
known URIs.

Applications that wish to mint new well-known URIs MUST register
them, following the procedures in Section 5.1.

Wait for it….

5.1. The Well-Known URI Registry

This document establishes the well-known URI registry.

Well-known URIs are registered on the advice of one or more
Designated Experts (appointed by the IESG or their delegate), with a
Specification Required (using terminology from [RFC5226]). However,
to allow for the allocation of values prior to publication, the
Designated Expert(s) may approve registration once they are satisfied
that such a specification will be published.

Well, that’s a relief! We are going to have Designated Expert(s) sitting in judgment over “well-known” URIs.

We just narrowly escaped being able to judge for ourselves what are URIs worth treating as “well-known” or not.

Good thing we have TBL, the W3C and Designated Experts to keep us safe.

*******
Update: 2010-07-09

I was worried that since the “Defining Well-known URIs” RFC was dated in April that this was some complicated spoof or joke. I even check the cross linking in the RFC but finally erred on saying it was real.

I had that judgment confirmed this morning by learning that the page “went dark” briefly last night and when I checked it this morning, the incorrect URL that I reported above has been corrected, silently.

W3C blog, goes dark, comes back with correct information, all signs that this must be genuine. Or at least it is being reported as such.

Taking Your Tool Kit to the Next Level

Filed under: Data Mining,Information Retrieval,Search Engines — Patrick Durusau @ 7:53 pm

Online Mathematics Textbooks is a good stop if you want to take your tool kit to the next level.

Plug-n-play indexing and search engines will do a lot out of the box but aren’t going to distinguish you from the competition.

Understanding the underlying algorithms will help make the data mining you do to populate your topic map qualitatively different.

Here’s your chance to brush up on your math skills without monetary investment.

***
PS: At some point, maybe at TMRA, a group of us need to draft an outline for a topic maps curriculum. Would have to include topic maps, obviously, but would also need to include courses in Information Retrieval, User Interfaces, Natural Language Processing, Classification, Math, what else? Would need to have “minors” in some particular subject area.

July 7, 2010

Second Verse, Same As The First

Filed under: Marketing,RDF,Semantic Diversity,Semantic Web,Semantics — Patrick Durusau @ 2:44 pm

Unraveling Algol: US, Europe, and the Creation of a Programming Language by David Nofre, University of Amsterdam, is an interesting account of the early history of Algol.

The convention wisdom that what evolved was Algol vs. Fortran is deeply questionable.

The underlying difficulty, a familiar one in semantic integration circles, was a universal programming language versus a diversity of programming languages.

Can you guess who won?

Can you guess where I would put my money in a repeat of a universal solution vs. diverse solutions?

Where is your money riding?

July 6, 2010

Pragmatic Topic Map Streaming – From Semantic Headache

Filed under: Uncategorized — Patrick Durusau @ 8:45 am

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.

July 5, 2010

Closed World vs. Open World: the First Semantic Web Battle – From Stefano’s Linotype

Filed under: OWL,RDF,Semantic Web — Patrick Durusau @ 7:20 pm

Closed World vs. Open World: the First Semantic Web Battle from Stefano’s Linotype is well worth your time.

See also Stack or Two Towers. Seems like one universal world view may not be a robust as previously thought.

Interesting that non-universal treatment of “doubt” may split the Semantic Web into incompatible parts. Can you say fragile?.

Data-Intensive Text Processing with MapReduce – Book

Filed under: Authoring Topic Maps,MapReduce,Software — Patrick Durusau @ 5:30 am

Data-Intensive Text Processing with MapReduce will help answer the question: What subjects are available in a given torrent of information?

Or, perhaps the more interesting question, What subjects did you find in a given torrent of information?

Not exactly the same question is it?

The first presumes that we are going to find the same subjects and the second does not.

Download the Final Manuscript Support the authors by buying a copy as well: publisher’s site.

Authored by Jimmy Lin and Chris Dyer.

Very interested in hearing from anyone using MapReduce to mine texts for use in topic map construction.

*****
Updated to insert the authors. Opps! 20 April 2011

July 4, 2010

iPhone Opportunity for Topic Maps

Filed under: Marketing,Topic Maps,Uncategorized — Patrick Durusau @ 8:00 pm

The You Say God Is Dead? There’s an App for That story in the New York Times, July 2, 2010, looks like an opportunity for topic maps.

For publishers, it would be possible to map responses on the basis of topics and let the topic map handle the details of where that is the appropriate response to an “opposing” app. It should shorten the update/production cycle as new material is added to counter new arguments or variations of old ones.

On the product side, publishers could use topic maps to enable users to respond to a variety of ways of naming or phrasing particular issues. In debates over religion, as in all other areas, differences in terminology can make it difficult to come to grips with the opposing side.

Depending on how it was implemented, a topic map app could integrate other resources, ranging from study materials to personal contacts as they relate to this application. Think of a topic map as being able to bridge between data held in mini-silos on an iPhone. So users could add in information into the app that was useful to them in such debates.

Any other critical points I should make as I contact publishers of these apps to recommend topic maps?

*****
PS: Did anyone with an iPhone try out tmjs from Jan Schreiber? I really don’t want to have to buy an iPhone just for that. Help me out here.

July 3, 2010

Computation, Information, Cognition: The Nexus and the Liminal – Book

Filed under: Computation,Researchers,Semantics — Patrick Durusau @ 8:58 pm

Computation, Information, Cognition: The Nexus and the Liminal by Gordana Dodig-Crnkovic and Susan Stuart, is a deeply delightful collection of essays from European Computing and Philosophy Conference (E-CAP), 2005.

I originally ordered it because of Graeme Hirst’s essay, “Views of Text Meaning in Computational Linguistics: Past, Present, and Future.” More on that in a future post but suffice it to say that he sees computational linguistics returning to a realization that “meaning” isn’t nearly as flat as some people would like to believe.

I could not help perusing some of the other essays and ran across Werner Ceusters and Barry Smith, in “Ontology as the Core Discipline of Biomedical Informatics – Legacies of the Past and Recommendations for the Future Directions of Research,” bashing the work of ISO/IEC TC 37, and its founder, Eugen Wüster, as International Standard Bad Philosophy. Not that I care for “realist ontologies” all that much but it is a very amusing essay.

Not to mention Patrick Allo’s “Formalizing Semantic Information: Lessons from Logical Pluralism.” If I say “informational pluralism” does anyone need more of a hint as to why I would like this essay?

I feel bad that I can’t mention in a reasonable sized posts all the other essays in this volume, or do more to give the flavor of those I mention above. This isn’t a scripting source book but the ideas you will find in it are going to shape the future of computation and our little corner of it for some time to come.

July 2, 2010

Rough Fuzzies, and Beyond?

Filed under: Fuzzy Sets,Rough Sets,Semantic Diversity,Subject Identity — Patrick Durusau @ 8:18 pm

Reading Rought Sets: Theoretical Aspects of Reasoning about Data by Zdzislaw Pawlak, when I ran across this comparison of rough versus fuzzy sets:

Rough sets has often been compared to fuzzy sets, sometimes with a view to introduce them as competing models of imperfect knowledge. Such a comparison is unfounded. Indiscernibility and vagueness are distinct facets of imperfect knowledge. Indiscernibility refers to the granularity of knowledge, that affects the definition of universes of discourse. Vagueness is due to the fact that categories of natural language are often gradual notions, and refer to sets with smooth boundaries. Borrowing an example from image processing, rough set theory is about the size of pixels, fuzzy set theory is about the existence of more than two levels of grey. (pp. ix-x)

It occurred to me that the precision of our identifications or perhaps better, the fixed precision of our identifications is a real barrier to semantic integration. Because the precision I need for semantic integration is going to vary from subject to subject, depending upon what I already know, what I need to know and for what purpose. Very coarse identification may be acceptable for some purposes but not others.

I don’t know what it would look like to have varying degrees of precision to subject identification or even how that would be represented. But, I suspect solving those problems will be involved in any successful approach to semantic integration.

July 1, 2010

Catching Users With Honey

Filed under: Marketing — Patrick Durusau @ 5:19 am

The recent browser plugin for automatic generation of topic maps by Lars Heuer, ANN: Finally! DBpedia and Wikipedia switched to Topic Maps! – News is one step towards catching users for topic maps with honey.

But it is only one step. True, it has reduced creation of topic maps to a drop down menu for DBpedia and Wikipedia resources, but still falls short of offering users a full-featured topic map experience.

There are a number of topic map engines, bare topic map engines. If all the reported 8.5 million developers in the world starting playing with those engines tomorrow, that is less than 1/10 of 1 percent of the 1 billion computer users in the world. My marketing department (my wife), thinks targeting promotional efforts at less than 1/10 of 1 percent of the potential audience is crazy (a technical marketing term for not good judgment).

The Mappify web service is an enormous step in the right direction.

But, the honey we need for users is demonstrating the immediate payoff without any effort on their part from this thing we call topic maps.

What to do once we have “caught” them is open to your imagination and ingenuity.

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