2007 Semantic Technology Conference: Spectrum of Reasoning and Applications

1. 2007 Semantic Technology Conference:Spectrum of Reasoning and Applications Tuesday, May 22, 2-3 p.m. Brand Niemann, US EPA and SICoP, Co-chair Google: ColabWiki: SICoP

2. Session Abstract • As adoption of semantic technology grows, many people lack a basic frame of reference to understand: (a) different kinds of reasoning, (b) different kinds of knowledge representation, and (c) the fit of these with different categories of application. The goal of this session is to address this need.

3. Author’s Abstract • The author adopts the (1) philosophy of Gruber (2007) that "ontologies should be designed and evaluated with respect to how well they achieve their purposes", the (2) approach of Baclawski and Niu (2006) that ontologies can be expressed in a wide range of languages, and the (3) Semantics for Information, Knowledge, and Reasoning Spectrum of Obrst-Davis (2006), to characterize recipients of SICoP Special Recognitions and presentations at this conference, and to illustrate different approaches to SICoP's work on National Information Sharing Standards (NISS) for Person using controlled vocabulary, glossaries, database and XML schema, OWL, and First Order Logic, etc. in a knowledgebase. The background for this work is presented in the session "Advanced Intelligence Community R&D Meets the Semantic Web?" • The Semantic Interoperability Community of Practice (SICoP) is a Community of Practice of the Federal Chief Information Officer's Council and its co-chairs are Mills Davis, Project 10x, and Brand Niemann, US EPA.

4. Overview • 1. Background • 2. SICoP Special Recognitions • 3. Top Five Ontology-Driven Applications • 4. W3C’s Semantic Web Education and Outreach (SWEO) Interest Group • 5. 2007 Semantic Technology Conference Knowledgebase • 6. Person Knowledgebase • 7. Ontolog Forum and 2007 Ontology Summit Knowledgebases • 8. Questions & Answers

5. 1. Background • Gruber (2007): • Grande Challenges for Ontology Design, Ontolog Forum, March 1, 2007. Slide 6. • Baclawski and Niu (2006): • Selecting an Ontology Language, pages 285-288, in Ontologies for Bioinformatics, 2006, The MIT Press. • Obrst-Davis (2006): • Semantic Wave 2006 - Executive Guide to the Business Value of Semantic Technologies, Figure 10.

6. 1. Background • Gruber (2007) , Grande Challenges for Ontology Design, Ontolog Forum, March 1, 2007. Slide 6: • What Makes a Good Ontology? • Claim: Ontologies should be designed and evaluated with respect to how well they achieve their purposes. • Observation: Ontologies are agreements, made in a social context, to accomplish shared objectives. • Question: Which objectives? • Approach: Follow the process of collaborative engineering design.

7. 1. Background • Gruber (2007) , Grande Challenges for Ontology Design, Ontolog Forum, March 1, 2007. Slide 15: • Semantic Web, Meet the Social Web: • Social Web: • Architecture of participation – user data • Emergent, bottom-up value creation • Vital ecosystem of software and data reuse • Semantic Web: • Architecture of computation – structured data • Value from integration • Ecosystem of service composition • The Killer Apps of Social + Semantic Web: • Collective Knowledge Systems

8. 1. Background • Baclawski and Niu (2006) , Selecting an Ontology Language, pages 285-288, in Ontologies for Bioinformatics, 2006, The MIT Press: • Three Categories: • Logical Languages (e.g. KIF) • Frame-based Languages (e.g. KL-ONE) • Graph-based Languages (e.g. XML and Semantic Web languages)

9. 1. Background • Baclawski and Niu (2006) , Selecting an Ontology Language, pages 285-288, in Ontologies for Bioinformatics, 2006, The MIT Press (continued): • The Major Ontology Languages Used Today: • Basic XML • XML DTD • XSD • XML Topic Maps • Semantic Web • RDF • OWL • OWL Lite • OWL-DL • OWL Full • It is possible to use an approach that is compatible with XML, DTD, XSD, RDF, and OWL languages.

10. 1. Background Source: Figure 10 in SICoP White Paper Series Module 2: Semantic Wave 2006 - Executive Guide to the Business Value of Semantic Technologies, May 15, 2006, Principal Author Mills Davis, Project10X.

11. 1. Background • From bottom-to-top, the amount, kinds, and complexity of metadata, modeling, context, and knowledge representation increases. • From left-to-right, reasoning capabilities advance from (a) information recovery based on linguistic and statistical methods, to (b) discovery of unexpected relevant information and associations through mining, to (c) intelligence based on correlation of data sources, connecting the dots, and putting information into context; to (d) question answering ranging from simple factoids to complex decision-support, and (e) smart behaviors including robust adaptive and autonomous action.

12. 1. Background • Moving from lower right to upper left, the diagram depicts a spectrum of progressively more capable categories of knowledge representation together with standards and formalisms used to express metadata, associations, models, contexts, and modes of reasoning. • As the amount and expressive power of the semantics and knowledge increases, so does the value of the reasoning capacity it enables.

13. 1. Background • More expressive forms of metadata and semantic modeling encompass simpler forms, and extend their capabilities. For example, the semantic web standard OWL encompasses the ability to represent glossaries, taxonomies, thesauri, subject ontologies, and the semantics of specific XML schemas, database schemas, entity-relationship and UML models. On the other hand the OWL standard is not capable of representing the full spectrum of logical theory, nor higher order modes of reasoning. But, other formalisms exist that can express these capabilities.

14. 2. SICoP Special Recognitions • SICoP Has Three White Papers: • Introducing Semantic Technologies and the Vision of the Semantic Web: • W3C Semantic Web and DARPA DAML Program/SICoP Semantic Web Applications for National Security (SWANS) Conference April 2005 (40 exhibits) • Semantic Wave 2006 - Executive Guide to the Business Value of Semantic Technologies: • 2006 Semantic Technology Conference. Updated at 2007 Conference. • Operationalizing the Semantic Web/Semantic Technologies: A roadmap for agencies on how they can take advantage of semantic technologies and begin to develop Semantic Web implementations (recently released for public review): • Advanced Intelligence Community R&D Meets the Semantic Web (ARDA AQUAINT Program).

15. 2. SICoP Special Recognitions • SICoP Has Given Special Recognitions That Document the Progress Along the Spectrum of Reasoning and Applications: • First Semantic Technology for E-Government Conference, September 8, 2003. • Second Semantic Technology for E-Government Conference, September 8-9, 2004. • Semantic Web Applications for National Security, April 7-8, 2005 (also counted as Third Semantic Technology for E-Government Conference). • SICoP Public Meeting, September 14, 2005.

16. 2. SICoP Special Recognitions • SICoP Has Given Special Recognitions That Document the Progress Along the Spectrum of Reasoning and Applications (continued): • Fourth Semantic Technology for E-Government Conference, February 9-10, 2006. • Joint Open Group, Federal Semantic Interoperability Community of Practice (SICoP), and Federal Metadata Management Consortium Conference, April 27-28, 2006. • Fifth Semantic Technology for E-Government Conference, October 10-11, 2006. • Second SOA for E-Government Conference, October 30-31, 2006. • First SICoP Special Conference, February 6, 2007. • Second SICoP Special Conference, April 25, 2007.

17. John Prange By SICoP Co-Chairs, Mills Davis, Project 10x, and Brand Niemann, U.S. EPA Best Practices Committee of the Federal Chief Information Officers Council Produced in Collaboration With Federal CIO Council’s Semantic Interoperability Community of Practice (SICoP) Special Recognition Director, Advanced Question and Answering for Intelligence Program (AQUAINT) and President of Language Computer Corporation Special SICoP Conference, February 6, 2007, at Computer Science Corporation, Falls Church, VA.

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23. 2. SICoP Special Recognitions *Note: No use of OWL or RDF whatsoever!

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27. 2. SICoP Special Recognitions * Is my child safe from environmental toxins? Introducing Semantic Technologies and the Vision of the Semantic Web, February 16, 2005.

28. 3. Top Five Ontology-Driven Applications • 1. Using ontologies to align / translate among standards (terminology remediations) [20 points ] • 2. Building ontologies from standards (translating standards into ontological representations that can be used by ontology-aware tools, e.g., inference engines) [14 points] • 3. An ontology of existing standards [10 points] • 4. Standards in health informatics and emergency management [10 points] • 5. Using ontology to improve data quality [9 points] • Source: Compilation from Mills Davis, SICoP Co-chair, for the 5th Semantic Interoperability for E-Government Conference, October 10-11, 2006. See http://ontolog.cim3.net/cgi-bin/wiki.pl?TopFive

29. 3. Top Five Ontology-Driven Applications • The number in brackets is the grand score. You might find the actual patterns of interest in that the first place received attention from over half of the raters but was NOT their first choice. No application received more than 1 first choice vote. Thus, as you might well expect, there is not a lot of clear agreement about the future....but a concensus is observed about the importance of ontologies in standards development. • Source: Bob Smith, on behalf of the Ontolog Forum, October 6, 2006.

30. 3. Top Five Ontology-Driven Applications • 1. Terminology Remediations: • NIEM in Knoodl/MatchIT (Brooke Stevenson/Charles Mosher) • 2. Translating Standards: • Federal Enterprise Architecture Reference Models (TopQuadrant) and Privacy Act (Rick Tucker) • 3. Existing Standards: • NIST Sensor Standards Harmonization Working Group (Brand Niemann and Team) • 4. Health Informatics and Emergency Management: • Terminology Service Bureau (Mike Cummens) • Improving Rapid First Response (Rex Brooks) • 5. Improve Data Quality: • See next slides from this conference.

31. 3. Top Five Ontology-Driven Applications • Improve Data Quality: • Data Modeling and OWL: Two Ways to Structure Data, David Hay, Essential Strategies, Inc.: • Objectives of a Data Model: • Capture the semantics of an organization. • Communicate these to the business without requiring technical skills. • Provide an architecture to use as the basis for database design and system design. • Now: Provides the basis for designing Service Oriented Architectures.

32. 3. Top Five Ontology-Driven Applications • Improve Data Quality: • Data Modeling and OWL: Two Ways to Structure Data, David Hay, Essential Strategies, Inc. (continued): • Synopsis: • Both data modeling and ontology languages represent the structure of business data (ontologies). • Data modeling represent data being collected, and filters according to the rules. • Ontology languages represent data being used, with ability to have computer make inferences. • Comment from Lucian Russell: • So ontology can improve data quality in legacy systems! David Hay agreed.

33. 3. Top Five Ontology-Driven Applications • Ontolog Forum Survey Questions*: • What 'value' does 'ontology' or 'ontological engineering' bring to your constituency (or sub-constituency)? • Value of ontology is: • 1. Capability to deal with problems of interoperability, scale, complexity, security, personalization, change & versioning, performance, and cost of net-centric solutions. • 2. New modes of user experience that are ubiquitous, intelligent, adaptive, anticipatory, and context-aware as boundaries between cyber and real worlds converge and interpenetrate. • 3. New categories for adaptive, autonomic, and autonomous systems that know, learn, and reason as people do. * Responses by SICoP Co-chair, Mills Davis, March 27, 2007.

34. 3. Top Five Ontology-Driven Applications • Ontolog Forum Survey Questions* (continued): • What are the 'issues' being encountered in bringing 'ontology' or 'ontological engineering' into your community? • "Ontology for Dummies." We are working with organizations and communities of interest. Within these groups there is plenty of expertise and experience expressing thoughts, meanings and values in language of one form or another (written, formal, visual, gestural, etc.) We need semantic user interface(s) that empower groups with diverse skills to easily collaborate (exploiting the knowledge representation they already know how to do...e.g. write, model, design, program, etc.) to create, manage, share knowledge, and put it to work. * Responses by SICoP Co-chair, Mills Davis, March 27, 2007.

35. 3. Top Five Ontology-Driven Applications • Ontolog Forum Survey Questions* (continued): • Can you state 'specific problem(s)' that help is needed on? • 1. Semantic user experience & semantic social computing — most researchers, ontologists and semantic web types are clueless. The Web 2.0 crowd has a clue, but lacks the right technology. Focusing here will jump-start multi-billion dollar markets. • 2. Semantic processing — Three key challenges are: • (a) reasoning more than logical truth or falsity e.g., dealing with causality, conflict, uncertainty, and diverse value systems, • (b) hi-performance (& easily programmed) semantic processing across multi-core, multi-thread, grid, mesh, and parallel environments, and • (c) semantic processing at web scale. * Responses by SICoP Co-chair, Mills Davis, March 27, 2007.

36. 3. Top Five Ontology-Driven Applications • January 2007: National Academy’s Transportation Research Board Annual Meeting and the Sustainable Water Resources Roundtable: • Based on WordNet and Professor Barry Smith’s Tutorial (Video): How to Build An Ontology. • February 2007: NSF Policy and Guidance Documents and Sensor Standards Harmonization Working Group: • NSF: Used Index as initial Taxonomy/Ontology and discovered Dr. John Prange already working on Semantic Decision Support System for NSF Proposals and Reviewers. • SSHWG: Repurpose 7 Standards into a Knowledgebase and Use Six Approaches to Build an Ontology/Sensor Data Network. • March 2007: IC Data Management Committee’s Terrorist Watchlist Person Data Exchange Standard (TWPDES): • See Section 6 for Knowledgebase Progress. • April 2007: Ontology Summit 2007 and SICoP Special Conference 2: • In process. • May 2007: 2007 Semantic Technology Conference: • See Section 5 for Knowledgebase Progress.

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38. 3. Top Five Ontology-Driven Applications Sustainable Water Resources Roundtable

39. 3. Top Five Ontology-Driven Applications NSF Policy and Guidance Documents Concepts Instances at a Specific Location

40. 3. Top Five Ontology-Driven Applications • NSF has agreed to continue funding for PANALYST and will use the system (with some modifications) this summer for processing for selected set of proposals (believe it is the next round of SBIRs) over the summer. • LCC will send 1-2 people from their Richardson, TX office to work in NSF spaces so that they can have access to an internal database concerning past NSF proposal reviewers (their technical backgrounds and previous review results) as well as other internal NSF data that NSF does not want to release outside of their spaces. Source: Dr. John Prange, President, LLC, April 4, 2007.

41. 3. Top Five Ontology-Driven Applications • See February 6, 2007, SICoP Special Conference 1: Building DRM 3.0 and Web 3.0 for Managing Context Across Multiple Documents and Organizations: • Dr. John Prange, LCC, Presentation: • What can be done today to extract logical relationships from text sources LCC is there as the company that specializes in extracting logical representations from language, which can generate new knowledge from well crafted Data Descriptions. Google: DRM 3.0 and Web 3.0

42. 3. Top Five Ontology-Driven Applications

43. 4. W3C’s Semantic Web Education and Outreach (SWEO) Interest Group • September 12, 2006, Group Launch. • November 14-15, 2006, First Face-to-Face Meeting. • February 8, 2007, Survey on the Semantic Web. • Most common advice was to document real case studies. • Requested that SICoP provide (limited to two sides of page). • See example at http://www.oracle.com/customers/snapshots/wyeth-siebel-casestudy.pdf • February 26, 2007, Community Semantic Web Projects. • http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects • March 6, 2007, Community Project Support: • Support the following four projects: FOAF based White Listing for Fighting Spam, Interlinking Open Data on the Semantic Web, Knowee/Contact Organizer, and Powder Browser Extensions. • See http://www.w3.org/2001/sw/sweo/

44. 4. W3C’s Semantic Web Education and Outreach (SWEO) Interest Group • February 22, 2007, SWEO Chair Provided Case Study Example: • Wyeth Streamlines Customer Relations and Sales Execution to Reduce Costs (June 2006). • Wing Yung, IBM, for Susie Stephens, SWEO Chair, SWEO's Recent Survey Results: Documenting Real Case Studies

45. 5. 2007 Semantic Technology Conference • DRM 3.0 and Web 3.0 Knowledgebase: • Metadata: • Full text of standards, meeting notes, etc. • Harmonization • Different ways in which the same words are used. • Enhanced Search: • Across all standards and showing context (e.g. words around the term or concepts) • Mashups: • A website or application that combines content from more than one source into an integrated experience (repurposing).

46. 5. 2007 Semantic Technology Conference

47. 5. 2007 Semantic Technology Conference • Leo Obrst (as part of Michael Eschold’s presentation) • When is a Taxonomy enough? • In general, you are using weak term relations because the nodes are not really meant to be concepts, but only words or phrases that will be significant to the user or you as a classification devise. • Taxonomy not enough if you need to either: • Using narrower than relation: Define term synonyms and cross-references to other associated terms, or; • Using subclass relation: Define properties, attributes and values, relations, constraints, rules, on concepts. • When is a Thesaurus enough? • You need more than a thesaurus if you need to define properties, attributes and values, relations, constraints, rules, on concepts. • You need either a conceptual model (weak ontology) or a logical theory (strong ontology). • Appropriate Applications (see next slide).

48. Appropriate Applications Concept- based Ontology strong Logical Theory weak Data & Object Models Term- based Source: Michael Eschold’s Presentation at SemTech 2007 Based on Leo Obrst. Thesaurus Expressivity Taxonomy Enterprise Modeling (system, service, data), Question-Answering (Improved Precision), Querying, SW Services Synonyms, Enhanced Search (Improved Recall) & Navigation, Cross Indexing Real World Domain Modeling, Semantic Search (using concepts, properties, relations, rules), Machine Interpretability (M2M, M2H semantic interoperability), Automated Reasoning, SW Services Categorization, Simple Search & Navigation, Simple Indexing Application

49. 5. 2007 Semantic Technology Conference • Taxonomy: • Categorization, Simple Search & Navigation, Simple Indexing: • Example: Best Breakout Session Presentation - Facilitating Semantic Data Integration and Interoperability between Collaboration Tools and Digital Object Repositories, Los Alamos National Laboratory Research Library, Linn Marks Collins, et al • Thesaurus: • Synonyms, Enhanced Search (Improved Recall) & Navigation, Cross Indexing: • Example: Best Organization Semantic Interoperability Application: Semantic Interoperability at the World Bank, World Bank, Denise Bedford • Data and Object Models: • Enterprise Modeling (system, service, data), Question-Answering (Improved Precision), Querying, SW Services: • Example: Best Paper: Semantic Service Oriented Architecture, US Navy, Sam Chance, Concurrent Technologies, Michael Seebold • Logical Theory: • Real World Domain Modeling, Semantic Search (using concepts, properties, relations, rules), Machine Interpretability (M2M, M2H semantic interoperability), Automated Reasoning, SW Services: • Example: Ontology and First Order Logic, Cutter Consortium/ VivoMind Intelligence, Arun Majumdar Source: Classification by Leo Obrst based on previous slide.

50. 5. 2007 Semantic Technology Conference • Advanced Intelligence Community R&D Meets the Semantic Web!, Lucian Russell, Expert Reasoning & Decisions LLC, Thursday, 8:30-9:30 a.m.: • Prior to 2006 Semantic Interoperability was stalled • The principles of Computer Science to do the job right were not present • As usual people did the best that they could with the tools at hand • Many low-level computer processes were incorrectly named as “Semantic” when they were not. They were “gilded farthings” • On 2006 there were four new developments • IKRIS was specified by a Multi-University/Industry team • TimeML was developed by James Pustejovsky team at Brandeis • WordNet 3.0 was completed by Christiane Fellbaum’s team at Princeton • The AQUAINT Phase II projects to understand language were completed • What could only be wished for was now possible. • The Computer Science breakthroughs were paid for by your tax dollars. There IS a role for government funding!