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

December 9, 2010

Developing High Quality Data Models – Book

Filed under: Data Models,Data Structures,Ontology — Patrick Durusau @ 11:39 am

Developing High Quality Data Models by Dr. Matthew West is due out in January of 2011. (Pre-order: Elsevier, Amazon)

From the website:

Anyone charged with developing a data model knows that there is a wide variety of potential problems likely to arise before achieving a high quality data model. With dozens of attributes and millions of rows, data modelers are in always danger of inconsistency and inaccuracy. The development of the data model itself could result in difficulties presenting accurate data. The need to improve data models begins in getting it right in the first place.

Developing High Quality Data Models uses real-world examples to show you how to identify a number of data modeling principles and analysis techniques that will enable you to develop data models that consistently meet business requirements. A variety of generic data model patterns that exemplify the principles and techniques discussed build upon one another to give a powerful and integrated generic data model with wide applicability across many disciplines. The principles and techniques outlined in this book are applicable in government and industry, including but not limited to energy exploration, healthcare, telecommunications, transportation, military defense, transportation and so on.

Table of Contents:

Preface
Chapter 1- Introduction
Chapter 2- Entity Relationship Model Basics
Chapter 3- Some types and uses of data models
Chapter 4- Data models and enterprise architecture
Chapter 5- Some observations on data models and data modeling
Chapter 6- Some General Principles for Conceptual, Integration and Enterprise Data Models
Chapter 7- Applying the principles for attributes
Chapter 8- General principles for relationships
Chapter 9- General principles for entity types
Chapter 10- Motivation and overview for an ontological framework
Chapter 12- Classes
Chapter 13- Intentionally constructed objects
Chapter 14- Systems and system components
Chapter 15- Requirements specifications
Chapter 16- Concluding Remarks
Chapter 17- The HQDM Framework Schema

I first became familiar with the work of Dr. West from Ontolog. You can visit his publications page to see why I am looking forward to this book.

Citation of and comments on this work will follow as soon as access and time allow.

December 7, 2010

Open Provenance Model *
Ontology – RDF – Semantic Web

Filed under: Ontology,RDF,Semantic Web — Patrick Durusau @ 11:37 am

A spate of provenance ontology materials landed in my inbox today:

  1. Open Provenance Model Ontology (OPMO)
  2. Open Provenance Model Vocabulary (OPMV)
  3. Open Provenance Model (OPM)
  4. Provenance Vocabulary Mappings

We should could ourselves fortunate that the W3C working group did not title their document: Open Provenance Model Vocabulary Mappings.

The community would be better served with less clever and more descriptive naming.

No doubt the Open Provenance Model Vocabulary (#2 above) has some range of materials in mind.

I don’t know the presumed target but some candidates come to mind:

  • Art Museum Open Provenance Model (including looting/acquisition terms)
  • Library Open Provenance Model
  • Natural History Open Provenance Model
  • ….

I am, of course, giving the author’s the benefit of the doubt in presuming their intent was not to create a universal model of provenance.

For topic map purposes, the Provenance Vocabulary Mappings document (#4 above) is the most interesting. Read through it and then answer the questions below.

Questions:

  1. Assume you have yet another provenance vocabulary. On what basis would you map it to any of the other vocabularies discussed in #4?
  2. Most of the mappings in #4 give a rationale. How is that (if it is) different from properties and merging rules for topic maps?
  3. What should we do with mappings in #4 or elsewhere that don’t give a rationale?
  4. How should we represent rationales for mappings? Is there some alternative not considered by topic maps?

Summarize your thoughts in 3-5 pages for all four questions. They are too interrelated to answer separately. You can use citations if you like but these aren’t questions answered in the literature. Or, well, at least I don’t find any of the answers in the literature convincing. 😉 Your experience may vary.

December 5, 2010

idk (I Don’t Know) – Ontology, Semantic Web – Cablegate

Filed under: Associations,Ontology,Roles,Semantic Web,Subject Identity,Topic Maps — Patrick Durusau @ 4:45 pm

While researching the idk (I Don’t Know) post I ran across the suggestion unknown was not appropriate for an ontology:

Good principles of ontological design state that terms should represent biological entities that actually exist, e.g., functional activities that are catalyzed by enzymes, biological processes that are carried out in cells, specific locations or complexes in cells, etc. To adhere to these principles the Gene Ontology Consortium has removed the terms, biological process unknown ; GO:0000004, molecular function unknown ; GO:0005554 and cellular component unknown ; GO:0008372 from the ontology.

The “unknown” terms violated this principle of sound ontological design because they did not represent actual biological entities but instead represented annotation status. Annotations to “unknown” terms distinguished between genes that were curated when no information was available and genes that were not yet curated (i.e., not annotated). Annotation status is now indicated by annotating to the root nodes, i.e. biological_process ; GO:0008150, molecular_function ; GO:0003674, or cellular_component ; GO:0005575. These annotations continue to signify that a given gene product is expected to have a molecular function, biological process, or cellular component, but that no information was available as of the date of annotation.

Adhering to principles of correct ontology design should allow GO users to take advantage of existing tools and reasoning methods developed by the ontological community. (http://www.geneontology.org/newsletter/archive/200705.shtml, 5 December 2010)

I wonder about the restriction, “…entities that actually exist.” means?

If a leak of documents occurs, a leaker exists, but in a topic map, I would say that was a role, not an individual.

If the unknown person is represented as an annotation to a role, how do I annotate such an annotation with information about the unknown/unidentified leaker?

Being unknown, I don’t think we can get that with an ontology, at least not directly.

Suggestions?

PS: A topic map can represent unknown functions, etc., as first class subjects (using topics) for an appropriate use case.

November 28, 2010

Common Logic, ISO/IEC 24707

Filed under: Logic,Ontology — Patrick Durusau @ 11:12 am

Common Logic, ISO/IEC 24707

Every time I have a question about ISO/IEC 24707 I have to either find it on my hard drive or search for the publicly available text.

Don’t know that it will help to write it down here, but it might. 😉

Questions (of course):

  1. Annotated bibliography of citation/use of ISO/IEC 24707. (within last year)
  2. Not a logic course but do you have a preference among the syntaxes? (discussion)
  3. Are ontologies necessarily logical?

Ontologies, Semantic Data Integration, Mono-ontological (or not?)

Filed under: Marketing,Medical Informatics,Ontology,Semantic Web,Topic Maps — Patrick Durusau @ 10:21 am

Ontologies and Semantic Data Integration

Somewhat dated, 2005, but still interesting.

I was particularly taken with:

First, semantics are used to ensure that two concepts, which might appear in different databases in different forms with different names, can be described as truly equivalent (i.e. they describe the same object). This can be obscured in large databases when two records that might have the same name actually describe two different concepts in two different contexts (e.g. ‘COLD’ could mean ‘lack of heat’, ‘chronic obstructive lung disorder’ or the common cold). More frequently in biology, a concept has many different names during the course of its existence, of which some might be synonymous (e.g. ‘hypertension’ and ‘high blood pressure’) and others might be only closely related (e.g. ‘Viagra’, ‘UK92480’ and ‘sildenafil citrate’).

In my view you could substitute “topic map” everywhere he says ontology, well, except one.

With a topic map, you and I can have the same binding points for information about particular subjects and yet not share the same ontological structure.

Let me repeat that: With a topic map we can share (and update) information about subjects, even though we don’t share a common ontology.

You may have a topic map that reflects a political history of the United States over the last 20 years and in part it exhibits an ontology that reflects elected offices and their office holders.

For the same topic map, to which I contribute information concerning those office holders, I might have a very different ontology, involving offices in Hague.

The important fact is that we could both contribute information about the same subjects and benefit from the information entered by others.

To put it another way is the different being mono-ontological or not?

Questions:

  1. Is “mono-ontological” another way of saying “ontologically/logically” consistent? (3-5 pages, citations if you like)
  2. What are the advantages of mono-ontological systems? (3-5 pages, citations)
  3. What are the disadvantages of mono-ontological systems? (3-5 pages, citations)

November 21, 2010

Ontology Based Graphical Query Language Supporting Recursion

Filed under: Ontology,Query Language,Semantic Web,Visual Query Language — Patrick Durusau @ 7:55 am

Ontology Based Graphical Query Language Supporting Recursion Author(s): Arun Anand Sadanandan, Kow Weng Onn and Dickson Lukose Keywords: Visual Query Languages, Visual Query Systems, Visual Semantic Query, Graphical Recursion, Semantic Web, Ontologies

Abstract:

Text based queries often lead tend to be complex, and may result in non user friendly query structures. However, querying information systems using visual means, even for complex queries has proven to be more efficient and effective as compared to text based queries. This is owing to the fact that visual systems make way for better human-computer communication. This paper introduces an improved query system using a Visual Query Language. The system allows the users to construct query graphs by interacting with the ontology in a user friendly manner. The main purpose of the system is to enable efficient querying on ontologies even by novice users who do not have an in-depth knowledge of internal query structures. The system also supports graphical recursive queries and methods to interpret recursive programs from these visual query graphs. Additionally, we have performed some preliminary usability experiments to test the efficiency and effectiveness of the system.

From the abstract I was expecting visual representation of the subjects that form the query. The interface remains abstract but is a good step in the direction of a more useful query interface for the non-expert. (Which we all are in some domain.)

Questions:

  1. Compare to your experience with query language interfaces. (3-5 pages, no citations)
  2. Are recursive queries important for library catalogs? (3-5 pages, no citations, but use examples to make your case, pro or con)
  3. Suggestions for a visual query language for the current TMQL draft? (research project)

November 20, 2010

From Documents To Targets: Geographic References

Filed under: Associations,Geographic Information Retrieval,Ontology,Spatial Index — Patrick Durusau @ 9:18 pm

Exploiting geographic references of documents in a geographical information retrieval system using an ontology-based index Author(s): Nieves R. Brisaboa, Miguel R. Luaces, Ángeles S. Places and Diego Seco Keywords: Geographic information retrieval, Spatial index, Textual index, Ontology, System architecture

Abstract:

Both Geographic Information Systems and Information Retrieval have been very active research fields in the last decades. Lately, a new research field called Geographic Information Retrieval has appeared from the intersection of these two fields. The main goal of this field is to define index structures and techniques to efficiently store and retrieve documents using both the text and the geographic references contained within the text. We present in this paper two contributions to this research field. First, we propose a new index structure that combines an inverted index and a spatial index based on an ontology of geographic space. This structure improves the query capabilities of other proposals. Then, we describe the architecture of a system for geographic information retrieval that defines a workflow for the extraction of the geographic references in documents. The architecture also uses the index structure that we propose to solve pure spatial and textual queries as well as hybrid queries that combine both a textual and a spatial component. Furthermore, query expansion can be performed on geographic references because the index structure is based in an ontology.

Obviously relevant to the Afghan War Diary materials.

The authors observe:

…concepts such as the hierarchical nature of geographic space and the topological relationships between the
geographic objects must be considered….

Interesting but topic maps would help with “What defensive or offensive assets I have in a geographic area?”

November 9, 2010

ONTOLOGIES AND SOCIAL SEMANTIC WEB FOR INTELLIGENT EDUCATIONAL SYSTEMS (SWEL)

Filed under: Conferences,Ontology,Semantic Web — Patrick Durusau @ 8:04 pm

ONTOLOGIES AND SOCIAL SEMANTIC WEB FOR INTELLIGENT EDUCATIONAL SYSTEMS (SWEL)

Paper deadline: 22 November 2010

Announcement:

Ontologies, the Semantic Web, and the Social Semantic Web offer a new perspective on intelligent educational systems by providing intelligent access to and management of Web information and semantically richer modeling of the applications and their users. This allows for supporting more adequate and accurate representations of learners, their learning goals, learning material and contexts of its use, as well as more efficient access and navigation through learning resources. The goal is to advance intelligent educational systems, so as to achieve improved e-learning efficiency, flexibility and adaptation for single users and communities of users (learners, instructors, courseware authors, etc). This special track follows the workshop series “Ontologies and Semantic Web for e-Learning”- SWEL which was conducted successfully from 2002-2009 at different hosting conferences (http://compsci.wssu.edu/iis/swel/).

BTW, I stole this from a post by Darina Dicheva to the topicmapmail list. CFP: SWEL Special Track at FLAIRS-24 – two weeks to the deadline!

October 30, 2010

Copyright and Taxonomies

Filed under: Ontology,Topic Maps — Patrick Durusau @ 12:24 pm

A post to the Ontolog forum brought AMERICAN DENT. ASSN. v. DELTA DEN. PLANS ASSN., 126 F.3d 977 (7th Cir. 1997) to my attention.

Posted to alert you to potential copyright issues.

For licenses, consider Creative Commons.

October 26, 2010

The Neighborhood Auditing Tool – Update

Filed under: Bioinformatics,Biomedical,Interface Research/Design,Ontology,SNOMED,UMLS — Patrick Durusau @ 7:22 am

The Neighborhood Auditing Tool for the UMLS and its Source Terminologies is a presentation mentioned here several days ago.

If you missed it, go to: http://bioontology.org/neighborhood-audiiting-tool for the slides and WEBEX recording.

Pay close attention to:

The clear emphasis on getting user feedback during the design of the auditing interface.

The “neighborhood” concept he introduces has direct application to XML editing.

Find the “right” way to present parent/child/sibling controls to users and you would have a killer XML application.

Questions:

  1. Slides 8 – 9. Other than saying this is an error (true enough), on what basis is that judgment made?
  2. Slides 18 – 20. Read the references (slide 20) on neighborhoods. Pick another domain, what aspects of neighborhoods are relevant? (3-5 pages, with citations)
  3. Slides 21 – 22. How do your neighborhood graphs compare to those here?
  4. Slides 23 – 46. Short summary of the features of NAT and no citation evaluation. Or, use NAT as basis for development of interface for another domain. (project)
  5. Slides 49 – 55. Visualizations for use and checking. Compare to current literature on visualization of vocabularies/ontologies. (project)
  6. Slides 56 – 58. Snomed browsing. Report on current status. (3-5 pages, citations)
  7. Slices 57 – 73. Work on neighborhoods and extents. To what extent is a “small intersection type” a sub-graph and research on sub-graphs applicable? Any number of issues and questions can be gleaned from this section. (project)

October 24, 2010

Introduction to Biomedical Ontologies

Filed under: Biomedical,Ontology — Patrick Durusau @ 9:58 am

A very good introduction to ontologies: Introduction to Biomedical Ontologies.

This introduction neatly frames the issue addressed by both controlled vocabularies (ontologies) and topic maps.

When faced with multiple terms for a single subject, a controlled vocabulary (ontology), solves the problem by using a single term.

Other terms that mean the same subject are “near synonyms.”

Watch the video and then check back here for a post called: Near Synonyms.

I will discuss how the treatment of “near synonyms” differs between topic maps and controlled vocabularies (ontologies).

Indexing Nature: Carl Linnaeus (1707-1778) and His Fact-Gathering Strategies

Filed under: Indexing,Information Retrieval,Interface Research/Design,Ontology — Patrick Durusau @ 9:52 am

Indexing Nature: Carl Linnaeus (1707-1778) and His Fact-Gathering Strategies Authors: Staffan Müller-Wille & Sara Scharf (Working Papers on The Nature of Evidence: How Well Do ‘Facts’ Travel? No. 36/08)

Interesting article that traces the strategies used by Linnaeus when confronted with the “first bio-information crisis” as the authors term it.

Questions:

  1. In what ways do ontologies resemble the bound library catalogs of the early 18th century?
  2. Do computers make ontologies any less like those bound library catalogs?
  3. Short report (3-5 pages, with citations) on transition of libraries from bound catalogs to index cards.
  4. Linnaeus’s colleagues weren’t idle. What other strategies, successful or otherwise, were in use? (project)

October 22, 2010

National Center for Biomedical Ontology

Filed under: Biomedical,Health care,Ontology — Patrick Durusau @ 6:00 am

National Center for Biomedical Ontology

I feel like a kid in a candy store at this site.

I suppose it is being an academic researcher at heart.

Reports on specific resources to follow.

October 20, 2010

8th Extended Semantic Web Conference: May 29 – June 2 2011 Heraklion, Greece

Filed under: Conferences,Ontology,OWL,Semantic Web,Semantics,SPARQL — Patrick Durusau @ 3:15 am

8th Extended Semantic Web Conference: May 29 – June 2 2011 Heraklion, Greece

Important Dates

See ESWC 2010 for range of content.

September 12, 2010

LNCS Volume 6304: Artificial Intelligence: Methodology, Systems, and Applications

Filed under: Classification,Ontology,Searching,Subject Identity — Patrick Durusau @ 6:48 pm

LNCS Volume 6304: Artificial Intelligence: Methodology, Systems, and Applications edited by Darina Dicheva, Danail Dochev, has, among other interesting titles, the following:

September 6, 2010

The Sixth Australasian Ontology Workshop

Filed under: Conferences,Mapping,Ontology — Patrick Durusau @ 6:56 am

The Sixth Australasian Ontology Workshop will be held in conjunction with 23rd Australasian Joint Conference on Artificial Intelligence in Adelaide, South Australia.

Important dates:

  • Submission of papers: 24 September 2010
  • Notification of acceptance/rejection: 22 October 2010
  • Final camera ready copies: 12 November 2010
  • Workshop date: 7 December 2010

Ontologies are used in topic maps just like in other knowledge management technologies. An area of special interest for topic maps is mapping between ontologies (some of which don’t admit the existence of other ontologies, 😉 ).

September 1, 2010

OpenOntologyRepository IPR/Discussion

Filed under: Ontology — Patrick Durusau @ 7:19 pm

OpenOntologyRepository IPR/Discussion Ontolog series promises to be an interesting discussion of IPR issues in the context of ontology development.

The wiki-page offers a variety of resources on IPR issues.

ONTOLOG (a.k.a. “Ontolog Forum”) is an open, international, virtual community of practice devoted to advancing the field of ontology, ontological engineering and semantic devoted to advancing the field of ontology, ontological engineering and semantic technology, and advocating their adoption into mainstream applications and international standards.

July 26, 2010

OneSource

Filed under: Heterogeneous Data,Mapping,Ontology,Semantic Diversity — Patrick Durusau @ 7:23 am

OneSource describes itself as:

OneSource is an evolving data analysis and exploration tool used internally by the USAF Air Force Command and Control Integration Center (AFC2IC) Vocabulary Services Team, and provided at no additional cost to the greater Department of Defense (DoD) community. It empowers its users with a consistent view of syntactical, lexical, and semantic data vocabularies through a community-driven web environment, directly supporting the DoD Net-Centric Data Strategy of visible, understandable, and accessible data assets.

Video guides to the site:

OneSource includes 158 vocabularies of interest to the greater U.S. Department of Defense (DoD) community. (My first post to answer Lars Heuer’s question “…where is the money?”)

Following posts will explore OneSource and what we can learn from each other.

June 4, 2010

representing scientific discourse, or: why triples are not enough

Filed under: Classification,Indexing,Information Retrieval,Ontology,RDF,Semantic Web — Patrick Durusau @ 4:15 pm

representing scientific discourse, or: why triples are not enough by Anita de Waard, Disruptive Technologies Director (how is that for a cool title?), Elsevier Labs, merits a long look.

I won’t spoil the effect by trying to summarize the presentation.  It is only 23 slides long.

Read those slides carefully and then get yourself to: Rhetorical Document Structure Group HCLS IG W3C. Read, discuss, contribute.

PS: Based on this slide pack I am seriously thinking of getting a Twitter account so I can follow Anita. Not saying I will but am as tempted as I have ever been. This looks very interesting. Fertile ground for discussion of topic maps.

I do not think it means what you think it means

Filed under: Ontology,OWL,RDF,Semantic Web,Software — Patrick Durusau @ 4:30 am

I do not think it means what you think it means by Taylor Cowan is a deeply amusing take on Pellet, an OWL 2 Reasoner for Java.

I particularly liked the line:

I believe the semantic web community is falling into the same trap that the AI community fell into, which is to grossly underestimate the meaning of “reason”. As Inigo Montoya says in the Princess Bride, “You keep using that word. I do not think it means what you think it means.”

(For an extra 5 points, what is the word?)

Taylor’s point that Pellet will underscore unstated assumptions in an ontology and make sure that your ontology is consistent is a good one. If you are writing an ontology to support inferences that is a good thing.

Topic maps can support “consistent” ontologies but I find encouragement in their support for how people actually view the world as well. That some people “logically” infer from Boeing 767 -> “means of transportation” should not prevent me from capturing that some people “logically” infer -> “air-to-ground weapon.”

A formal reasoning system could be extended to include that case, but can that be done as soon as an analyst has that insight or must it be carefully crafted and tested to fit into a reasoning system when “the lights are blinking red?”

May 31, 2010

Semantic Web Challenge

Filed under: Conferences,Data Integration,Linked Data,Ontology,Semantic Web,Subject Identity,Topic Maps — Patrick Durusau @ 10:40 am

The Semantic Web Challenge 2010 details landed in my inbox this morning. My first reaction was to refine my spam filter. 😉 Just teasing. My second and more considered reaction was to think about the “challenge” in terms of topic maps.

Particularly because a posting from the Ontology Alignment Evaluation Initiative arrived the same day, in response to a posting from sameas.org.

I freely grant that URIs that cannot distinguish between identifiers and resources without 303 overhead are poor design. But the fact remains that there are many data sets, representing large numbers of subjects that have even poorer subject identification practices. And there are no known approaches that are going to result in the conversion of those data sets.

Personally I am unwilling to wait until some new “perfect” language for data sweeps the planet and results in all data being converted into the “perfect” format. Anyone who thinks that is going to happen needs to stand with the end-of-the-world-in-2012 crowd. They have a lot in common. Magical thinking being one common trait.

The question for topic mappers to answer is how do we attribute to whatever data language we are confronting, characteristics that will enable us to reliably merge information about subjects in that format either with other information in the same or another data language? Understanding that the necessary characteristics may vary from data language to data language.

Take the lack of a distinction between identifier and resource in the Semantic Web for instance. One easy step towards making use of such data would be to attribute to each URI the status of either being an identifier or a resource. I suspect, but cannot say, that the authors/users of those URIs know the answer to that question. It seems even possible that some sets of such URIs are all identifiers and if so marked/indicated in some fashion, they automatically become useful as just that, identifiers (without 303 overhead).

As identifiers they may lack the resolution that topic maps provide to the human user, which enables them to better understand what subject is being identified. But, since topic maps can map additional identifiers together, when you encounter a deficient identifier, simply create another one for the same subject and map them together.

I think we need to view the Semantic Web data sets as opportunities to demonstrate how understanding subject identity, however that is indicated, is the linchpin to meaningful integration of data about subjects.

Bearing in mind that all our identifications, Semantic Web, topic map or otherwise, are always local, provisional and subject to improvement, in the eye of another.

May 27, 2010

Blast From The Past

Filed under: Humor,Ontology,Semantic Web — Patrick Durusau @ 7:27 pm

Can you place the following quote?

…my invention uses reason in its entirety and it, in addition, a judge of controversies, an interpreter of notions, a balance of probabilities, a compass which will guide us over the ocean of experiences, an inventory of things, a table of thoughts, a microscope for scrutinizing present things, a telescope for predicting distant things, a general calculus, an innocent magic, a non-chimerical cabal, a script which all will read in their own language; and even a language which one will be able to learn in a few weeks, and which will be soon accepted amidst the world. And which will lead the way for the true religion everywhere it goes.

I have to admit when I first read the part about “…one will be able to learn it in a few week,…” I was thinking about John Sowa and one of his various proposals (some say perversions) of natural language.

Then I got to the part about “…the way for the true religion…” and realized that this was probably either a fundamentalist quote (you pick the tradition) or from an earlier time.

Curious? It was Leibniz, Letter to Duke of Hanover, 1679. Quoted in The Search For The Perfect Language by Umberto Eco. More on the book in later posts.

May 23, 2010

The Clio Project

Filed under: Mapping,Ontology — Patrick Durusau @ 8:38 am

The Clio Project is a collaboration between University of Toronto and the IBM Almaden Research Center to build tools to simplify conversion from one data format to another, or in the words of the project “…Clio can automatically generate either a view, to reformulate queries against one schema into queries on another for data integration, or code, to transform data from one representation to the other for data exchange.”

The text with some screen shots at IBM says:

The Schema Viewer allows users to draw arrows between source and target schema elements. Such arrows may cross nesting levels, combine multiple elements, split and merge tables, etc. Clio incrementally interprets these arrows as mappings and generates a query accordingly.

Is that the same as Bernard Vatant’s ontological emptiness? That is all we know is that the user drew an arrow from one schema element to another?

The project has produced a number of papers but no software that is openly available. I will request a copy and report back.

May 12, 2010

Gonorrhea and Weapons of Mass Destruction

Filed under: Classification,Concept Hierarchies,Humor,Ontology — Patrick Durusau @ 7:43 am

The Weapons of Mass Destruction (WMD) ontology at the Suggested Upper Merged Ontology (SUMO) website includes Gonorrhea.

Imagine a WMD debate over a Gonorrhea test for all airline passengers, blue ink for their thumbs (positive), along with penicillin shots.

The transmission mechanisms of Gonorrhea make it an unlikely weapon of mass destruction.

The monological nature of WMD ontology prevents contrary views from being registered. It must have, after all, a determinate result.

Topic map authors can make equally foolish statements. The difference is that contrary views can be registered as well.

April 2, 2010

Re-Inventing Natural Language

Filed under: Heterogeneous Data,Ontology,Semantic Diversity — Patrick Durusau @ 8:29 pm

What happens when users use ontologies? That is when ontologies leave the rarefied air of campuses, turgid dissertations and the clutches of arm chair ontologists?

Would you believe that users simply take terms from ontologies and use them as they wish? In other words, after decades of research, ontologists have re-invented natural language! With all of its inconsistent usage, etc.

I would send a fruit basket if I had their address.

For the full details, take a look at: The perceived utility of standard ontologies in document management for specialized domains. From the conclusion:

…rather than being locked into conforming to the standard, users will be free to use all or small fragments of the ontology as best suits their purpose; that is, these communities will be able to very flexibly import ontologies and make selective use of ontology resources. Their selective use and the extra terms they add will provide useful feedback on how the external ontologies could be evolved. A new ontology will emerge as the result and this itself may become a new standard ontology.

I would amend the final two sentences to read: “Their selective use and the extra terms they add will provide useful feedback on how their language is evolving. A new language will emerge as the result and this may itself become a new standard language.

Imagine, all that effort and we are back where we started. Users using language (terms from an ontology) to mean what they want it to mean and not what was meant by the ontology.

The arm chair ontologists have written down what they mean. Why don’t we ask ordinary users the same thing, and write that down?

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