Foundations IV: Ontology Evolution and Knowledge Management Class Session 8

1. Foundations IV: Ontology Evolution and Knowledge ManagementClass Session 8 Deborah McGuinness and Joanne Luciano With Peter Fox and Li Ding CSCI-6962-01 October 25, 2010

2. Review • Ontology Evolution Environment (Chimaera) Reading • Assignment • Any comments, questions?

3. Semantic Web Methodology and Technology Development Process Establish and improve a well-defined methodology vision for Semantic Technology based application development Leverage controlled vocabularies, et c. Adopt Technology Approach Leverage Technology Infrastructure Science/Expert Review & Iteration Rapid Prototype Open World: Evolve, Iterate, Redesign, Redeploy Use Tools Analysis Use Case Develop model/ ontology Small Team, mixed skills 3

4. Some Current Motivating Trends More applications are depending on background ontologies for : Site Organization, Query expansion, Integrity checking, … Systems are increasingly hybrid… thus requiring integration with many other systems There are an increasing number of existing vocabularies / taxonomies / ontologies that are official or defacto standards Applications are becoming more long lived, thus requiring evolution and maintenance …

5. Approach for today Introduce one early ontology evolution environment from 2000 (including historical motivation and capabilities) Discuss its strengths and weaknesses Followed by group discussion of where this area is evolving and should evolve today

6. Motivation: Ontology Integration Trends Integrated in most search applications General search from 2000: Yahoo, Lycos, Xift, …) General search today: Yahoo, keyword advertising and more Scientific search today: e.g. Noesis Core component of E-Commerce apps (Amazon, eBay, Virtual Vineyards, REI, etc.) Integrated in configuration applications (Dell, PROSE, …. Sun, SAP, Trilogy, …)

7. Motivation: Ontology Evolution Controlled vocabularies abound (SIC-codes, UN/SPSC, RosettaNet, OpenDirectory,…) Distributed ownership/maintenance Larger scale (Open Directory >23.5K editors, ~250K categories, 1.65M sites – true in 2000) (Open Directory (now arguably less mainstream has over 81K editors, and 590K directories, and >4.6M sites) Becoming more complicated - Moving to classes and slots (and value restrictions, enumerated sets, cardinality)

8. Motivation: Science Ontologies Today Growing awareness and consensus on science taxonomies/ontologies (SWEET, ChemML, …) Growing interest in community ownership/maintenance Editors are less typically trained in computer science…. thus tools need to be aimed at broader audiences Domain ontologies are growing more complicated – GO, BioPortal, … Domain specific environments are starting to emerge

9. Chimaera – A Merging and Diagnostic Ontology Environment Web-based tool utilizing the KSL Ontolingua platform that supports: merging multiple ontologies found in distributed environments analysis of single or multiple ontologies attention focus in problematic areas simple browsing and mixed initiative editing

10. Historical Setting Large government sponsored project Broad ontology needs – CIA World Fact Book, terrorist information, biological and chemical knowledge, weapon knowledge, … Number of ontologies approaching 100 Large sets of facts – mined from natural language text Complicated question set Distributed work force (many without much training in computer science) Time pressure

11. Historical KB Analysis Task Review KBs that: Were developed using differing standards May be syntactically but not semantically validated May use differing modeling representations May have different purposes Produce KB logs (in interactive environments) Identify provable problems Suggest possible problems in style and/or modeling Are extensible by being user programmable End user humans (but not with extensive training)

12. The (General) Need For KB Analysis Large-scale knowledge repositories will necessarily contain KBs produced by multiple authors in multiple settings KBs for applications will typically be built by assembling and extending multiple modular KBs from repositories that may not be consistent KBs developed by multiple authors will frequently Express overlapping knowledge in different, possibly contradictory ways Use differing assumptions and styles For such KBs to be used as building blocks - They must be reviewed for appropriateness and “correctness” That is, they must be analyzed

17. Historical KB Merging Needs One large reasoning task using many large scale KBs KBs for applications were required to extend existing ontologies (CYC and others) Overlapping ontologies and instance data For such KBs to be used together as building blocks - Their representational differences must be reconciled

18. The KB Merging Task Combine KBs that: Were developed independently (by multiple authors) Express overlapping knowledge in a common domain Use differing representations and vocabularies Produce merged KB with Non-redundant Coherent Unified vocabulary, content, and representation

19. How KB Merging Tools Can Help Combine input KBs with name clashes Treat each input KB as a separate name space Support merging of classes and relations Replace all occurrences by the merged class or relation Test for logical consistency of merge (e.g. instances/subclasses of multiple disjoint classes) Actively look for inconsistent extensions Match vocabulary Find name clashes, subsumed names, synonyms, ... Focus attention Portions of KB where new relationships are likely to be needed E.g., sibling subclasses from multiple input KBs Derive relationships among classes and relations Disjointness, equivalence, subsumption, inconsistency, ...

24. Merging Tools Merging can be arbitrarily difficult KBs can differ in basic representational design May require extensive negotiation among authors Tools can significantly accelerate major steps KB merging using conventional editing tools is Difficult Labor intensive Error prone Hypothesis: tools specifically designed to support KB merging can significantly Speed up the merging process Make broader user set productive Improve the quality of the resulting KB

26. Chimaera Usage HPKB program – analyze diverse KBs, support KR novices as well as experts Cleaning semi-automatically generated KBs Browsing and merging multiple controlled vocabularies (e.g., internal vocabularies and UN/SPSC (std products and services codes)) Reviewing internal vocabularies

27. OIE Intro and Demo

28. Jiao Tao, Li Ding, Deborah L. McGuinness Tetherless World Constellation Rensselaer Polytechnic Institute Troy, NY, USA Instance Data Evaluation on the Semantic Web

29. Data Evaluation on the SW: State of the Art On the semantic web, instance data often accounts for orders of magnitude more data than ontology (Ding & Finin 2006). However most data evaluation work (Rocha et al. 1998) focuses on ontology evaluation, i.e., checking whether the ontologies correctly describe the domain of interests. There is very little, if any, work on evaluating the conformance between ontologies and instance data.

30. Instance Data Evaluation on the Semantic Web Does D follow the semantic expectations in O ? 1. Create domain knowledge as ontologies O (semantic expectations) D O No syntax errors? Web 4. Publish D 3. Instantiate ontologies as instance data D O 2. Acquire ontologies Semantic expectation mismatches: (i) Logical inconsistencies (ii) Integrity issues

31. Generic Evaluation Process (GEP) Load instance data D Is loading failing? Parse instance data D Is D syntactically correct? Load referenced ontologies O = {O1,O2, …} Is Oi reachable? where Oi defines the terms used by D. Inspect logical inconsistencies in D Is Oi logically consistent? Merge all consistent referenced ontologies into O' Are D+O’ logically consistent? Inspect integrity issues in D Compute DC = INF(D,O') which includes all triples in D and O', and all inferred sub-class/sub-property relations Is there any integrity issue in D?

32. IntegrityIssues Unexpected Individual Type (UIT) Issue rdfs:domain rdfs:range owl:allValuesFrom Redundant Individual Type (RIT) Issue Non-specific Individual Type (NSIT) Issue Missing Property Value (MPV) Issue owl:cardinality owl:minCardinality Excessive Property Value (EPV) Issue owl:cardinality owl:maxCardinality

33. Example: MPV Make sure all instances of wine have a Maker

34. SPARQL Solutions forIntegrity Issue Detection

35. Implementation and Evaluation Demo: TW OIE Service http://onto.rpi.edu/demo/oie/ Comparative experiment results

36. References T. Berners-Lee, J. Hendler, and O. Lassila, The Semantic Web: A New Form of Web Content that Is Meaningful to Computers Will Unleash a Revolution of New Possibilities, Scientific American, pp. 34–43, 2001. J. Davies, M. Lytras, and A. Sheth, Semantic-Web-Based Knowledge Management, IEEE Internet Computing, Vol. 11, No. 5, pp. 14-6, 2007. L. Ding, and T. Finin, Characterizing the Semantic Web on the Web, ISWC, pp. 242-257, 2006. R. A. Rocha, S. M. Huff, P. J. Haug, D. A. Evans, and B. E. Bray, Evaluation of a Semantic Data Model for Chest Radiology: Application of a New Methodology, Methods of Information in Medicine, Vol. 37, No.4-5, pp. 477-490, 1998. D. L. McGuinness and P. Pinheiro da Silva. Explaining Answers from the Semantic Web: The Inference Web Approach. Journal of Web Semantics. Vol.1 No.4., pp 397-413, 2004.

37. Ontology Evaluation Presentation 37

38. Ontology Evaluation:Methods and Metrics

40. The Problem: Ontology Elephants

41. The Problem

42. Objective: Evolve to Science and Engineering Discipline for Ontology

43. Approach:

44. Research Plan: (1) Identify use cases

45. Research Plan (2) Develop Metrics

46. Examples

47. Example (1): BioPAX - Lack of Fluency

48. Example (2): Habitat Lite: Correctness and Completeness

49. Example 3: Enable Influenza Research(proposed construction and subsequent evaluation)

50. Example 3: Enable Influenza Research(proposed construction and subsequent evaluation)