1. An Environment for Merging and Testing Large Ontologies
Deborah McGuinness, Richard Fikes, James Rice*, Steve Wilder
Associate Director and Senior Research Scientist
Knowledge Systems Laboratory
Stanford University
Stanford, CA 94305
*CommerceOne, Mountain View, CA

2. Motivation: Ontology Integration Trends
Integrated in most search applications (Yahoo, Lycos, Xift, …)
Core component of E-Commerce applications (Amazon, eBay, Virtual Vineyards, REI, VerticalNet, CommerceOne, etc.)
Integrated in configuration applications (Dell, PROSE, etc.)

3. 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)
Becoming more complicated - Moving to classes and slots (and value restrictions, enumerated sets, cardinality)

4. 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

5. The Need For KB Merging
Large-scale knowledge repositories will contain KBs produced by multiple authors in multiple settings
KBs for applications will be built by assembling and extending multiple modular KBs from repositories
KBs developed by multiple authors will frequently
Express overlapping knowledge in a common domain
Use differing representations and vocabularies
For such KBs to be used together as building blocks -
Their representational differences must be reconciled

6. 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

7. 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, ...

12. 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

14. Our 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

19. 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

20. Discussion/Conclusion
Ontologies are becoming more central to applications, they are larger, more distributed, and longer-lived
Environmental support (in particular merging and diagnostic support) is more critical for the broader user base
Chimaera provides merging and diagnostic support for ontologies in many formats
It improves performance over existing tools
It has been used by people of various training backgrounds in government and commercial applications and is available for use.
-movie, tutorial, papers, link to live system, etc.

21. Extras

22. The Need For KB Analysis
Large-scale knowledge repositories will contain KBs produced by multiple authors in multiple settings
KBs for applications will 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
Have different purposes
KBs must be reviewed for appropriateness and “correctness”

23. Disjointness, Inverse, part-of…
What is an Ontology?
Thesauri
“narrower
term”
relation
Frames
(properties)
Formal
is-a
General
Logical
constraints
Catalog/ ID
Informal
is-a
Formal
instance
Disjointness, Inverse, part-of…
Terms/
glossary
Value Restrs.

24. Ontologies and importance to E-Commerce
Simple ontologies provide:
Controlled shared vocabulary (search engines, authors, users, databases, programs all speak same language)
Organization (and navigation support)
Expectation setting (left side of many web pages)
Browsing support (tagged structures such as Yahoo!)
Search support (query expansion approaches such as FindUR, e-Cyc)
Sense disambiguation

25. Ontologies and importance to E-Commerce II
Foundation for expansion and leverage
Conflict detection
Completion
Regression testing/validation/verification support foundation
Configuration support
Structured, comparative search
Generalization/ Specialization …

26. E-Commerce Search (starting point Forrester modified by McGuinness)
Ask Queries
- multiple search interfaces (surgical shoppers, advice seekers, window shoppers)
- set user expectations (interactive query refinement)
- anticipate anomalies
Get Answers
- basic information (multiple sorts, filtering, structuring)
- modify results (user defined parameters for refining, user profile info, narrow query, broaden query, disambiguate query)
- suggest alternatives (suggest other comparable products even from competitor’s sites)
Make Decisions
- manipulate results (enable side by side comparison)
- dive deeper (provide additional info, multimedia, other views)
- take action (buy)

27. A Few Observations about Ontologies
Simple ontologies can be built by non-experts
Consider Verity’s Topic Editor, Collaborative Topic Builder, GFP interface, Chimaera, etc.
Ontologies can be semi-automatically generated
from crawls of site such as yahoo!, amazon, excite, etc.
Semi-structured sites can provide starting points
Ontologies are exploding (business pull instead of technology push)
most e-commerce sites are using them - MySimon, Affinia, Amazon, Yahoo! Shopping,, etc.
Controlled vocabularies (for the web) abound - SIC codes, UMLS, UN/SPSC, Open Directory, Rosetta Net, …
Business ontologies are including roles
DTDs are making more ontology information available
Businesses have ontology directors
“Real” ontologies are becoming more central to applications

28. Implications and Needs
Ontology Language Syntax and Semantics
Environments for Creation and Maintenance of Ontologies
Training (Conceptual Modeling, reasoning implications, …)
Issues:
Collaboration among distributed teams
Diverse training levels
Interconnectivity with many systems/standards
Analysis and Diagnosis
Scale