Automating Ontology Building: Ontologies for the Semantic Web and Knowledge Management

1. Automating Ontology Building: Ontologies for the Semantic Web and Knowledge Management Christopher BREWSTER Department of Computer Science, University of Sheffield www.dcs.shef.ac.uk/~kiffer

2. Outline • The Need for Ontologies and Taxonomies • Problems with Knowledge Acquisition • Methodological Criteria • Coherence • Multiplicity • Ease of Computation • Labels • Data Sources • Linking/ associating terms • Constructing Hierarchies • Labelling Relations • Conclusions

3. The Need • Ontologies and Taxonomies are needed for: • Ontologies for the Semantic Web • Central component for ‘agent services’ over the Web • Knowledge acquisition for knowledge management • Minds of employees = Intangible assets • Ontologies act as “index to memory of an organisation” • Many organisations have built or are building their own ontologies/taxonomies (e.g. BBC, British Council, Clifford Chance, etc.)

4. The Need (2) • Navigational Aids e.g. Yahoo, Northern Lights, corporate intranets, … • Component in LT systems • etc. • BUT complex hand-built taxonomies/ ontologies such as Microkosmos, Cyc, WordNet, etc. are not used in applications!

5. Problems: Knowledge Representation • Widely-held Assumption: knowledge can be codified in an ontology • Ontology = “formal explicit specification of a shared conceptualisation” (Gruber) = “ a document or file that formally defines the relations among terms” (Berners-Lee)

6. Problems: Knowledge Representation (2) Continuum: Ontologies  Taxonomies  Other Semantic Networks Differences lie in degree of logical rigour, formality and the potential for reasoning over the data structure

7. Problems: Ontology/ Taxonomy Construction • Current focus: formal criteria e.g. “consistency, completeness & conciseness” (e.g. Gomez-Perez, Guarino) • Idealised aspirations similar to those in lexicography • Common assumption: users will willingly contribute to construction of a formal ontology (e.g. Stutt & Motta) • Reality: both librarian and companies know authors tag their texts inappropriately

8. Manual Labour • All current ontologies/ taxonomies are hand built • Yahoo, Northern Lights (browsable taxonomy) • Gene Ontology • Company internal (e.g. Arthur Andersen, DaimlerChrysler) • Computers cannot be relied on. • Some are mergers of existing taxonomies • Company merger  ontology merger (e.g. GlaxoSmithKline)

9. Specific Issues (1) • High cost of human labour in initial development / editorial task • Category construction • Content association • Knowledge is in continuous flux: ‘out of date on day of publication’ • Ontologies/Taxonomies need to be domain specific • General ontologies not very helpful without a lot of work

10. Specific Issues (2) • Ontologies/Taxonomies reflect a particular perspective on the world e.g. categories like “business opportunity” • Categories are abstractions, derived from an analytic frame work e.g. ‘nouns’ or ‘business opportunity’ • Ontologies = “shared conceptualisations” but often very difficult for human being to agree on categorising the world (e.g. problems with global ‘standards’)

11. Contradictions • Problems 1-3 imply need for automated construction • Problems 4-6 imply impossibility of such an approach. • Ontology construction involves judicious integration of of automated methods with manual validation

12. Data Sources • Traditionally and currently: • Protocol analysis • Introspection • Both slow • Both subjective • Both very costly • Future: • Automated Text/Corpus analysis • Information Extraction from texts • Automated ontology building must be based on texts, since we cannot enter people’s minds • Further in Future • Integration with generated dialogue ….

13. Methodological Criteria • Set of criteria to: • Guide choice and development of tools and algorithms • Contribute to evaluation of ontology construction methodologies by going beyond idealised abstract criteria

14. 1. Coherence • The algorithm(s) must produce output coherent for the user • Coherence = appears to user as reflecting common sense i.e. ‘shared conceptualisation’ of Gruber • Linguistic coherence  encyclopaedic coherence • Tennis problem in Wordnet • Very difficult to evaluate • No criteria for ‘degree of correctness’ • Easy to spot algorithms which produce rubbish • Help!

15. 2. Multiplicity • Algorithm(s) must allow for multiple placement of the same term in the ontology • Multiplicity  semantic ambiguity • ‘cat’ ISA ‘mammal’ • ‘cat’ ISA ‘ pet’ • Classic problem in librarianship

16. 3. Ease of Computation • Algorithms must not have excessive complexity and consequent computational processing cost. • Ontologies must be kept current • Feedback to editors must be acceptable • Certain algorithms have very high complexity (e.g. Brown et al.92 where it O(V5) where V = no. of types in the corpus.

17. 4. Lone Labels • The algorithm generates nodes with simple labels consisting of only one term. • Complex labels are not user friendly • Some approaches (e.g. Scatter/Gather) generate complex labels • This does not preclude synonyms acting as alternative labels • A bag of words is not acceptable

18. 5. Data Source • The algorithm(s) must use texts (corpora) as primary data sources, AND allow the extension of existing ontologies. • Written texts are the most appropriate data sources (quantity, quality, accessibility) • ‘Seed’ ontologies or existing complex data structures need to be taken into account, e.g. the company’s own ‘top-level’

19. Techniques • Linking terms • Methods to associate one word/term with another • Organising terms • Methods to organise terms into a structure e.g. a hierarchy • Labelling term relations • Methods to label the relationship between terms

20. Linking/associating terms • Objective: Given term a, list a set of associated terms {b1, b2, b3, ……bn} • Many, many techniques: correspondence analysis, distributional analysis, using MI in a window etc. ….

21. Linking/associating terms:Associated Words • Idea of Mike Scott (among others): • Input: corpusa + reference corpusb • Key words = unusually frequent words in corpusa in comparison with reference corpusb • Key-key words = words which are key in more than one text, the more text, the more key. • Associated words of wi = key words which co-occur in the same texts • 2 factors: i. comparison with reference corpus, and ii. cross-text frequency • Results can be very good (e.g. when using an encyclopaedia) but poor when using random texts.

22. Linking/associating terms: Colocational Similarity • Idea of Hays (among others): • Similarity between terms is measured by number of identical words in a window (citation), plus number of identical words in identical positions (distance) • He argues very effective in identifying similarity of meaning (95% + accuracy) • but it works only 30% of the time (i.e. only 30% of citations show similarity to another citation)

23. Linking/associating terms: Syntactic Similarity • Grefenstette (1994): • Texts are shallow parsed and for each term, the words in specific syntactic relations are collected as ‘attributes’. • The set of attributes for each term are compared with the set for each other term using the Jaccard measure. • Example result: tissuecell | growth cancer liver tumor | resistance disease lens but also this produces term: antonyms as an output.

24. Constructing Hierarchies • Objective: Construct trees or Directed Acyclic Graphs from the terms in the vocabulary of the texts. • Relations may or may not be specified between nodes • Major problem is obtaining (candidate) labels for a specific cluster

25. Constructing Hierarchies (2) • Again many methods exist: • Brown et al. (1992) merges classes based on MI • very high computational cost • No labels on nodes • No possiblity of integrating with an existing data structure

26. Constructing Hierarchies (3) • McMahon and Smith (1996) combine top-down with bottom up cluster formation. • lower computational cost, • still no labels • Scatter/Gather developed at Xerox • Strictly speaking only for documents not terms • Computationally tractable • Generates labels BUT consisting of many terms

27. Constructing Hierarchies (4) • Sanderson & Croft • Document-based lexical subsumption • Generates single term labels • Could allow use of existing hierarchy/ taxonomy • Problem of coherence

28. Labelling Relations • Most difficult challenge because: • There is no set of commonly accepted relations (cf. parts of speech) • There is no known correlation between a relation (e.g. ‘meronym’) and specific patterns in texts. • It is an open question whether there is sufficient lexico-syntactic encoding in texts to make the establishment of relations between concepts ‘extractable’ from texts. • Few methods exist ….

29. Labelling Relations:Synonyms & Substitutability • Identification of synonyms by ‘substitutability’ tests (Church et al. 1994) • Uses t-test to determine the significance of the overlap between the syntactic objects of different verbs • Result is a table of candidate substitutes of a given verb • BUT, the result is “not always one that fits nicely into a familiar category such as synonymy, antonymy, and hyponymy” (ibid.) • Hays (1997) similar work using collocational similarity

30. Labelling Relations: Hyponyms and ‘Lexico-syntactic patterns’ • Identification of hyponyms by ‘lexico-syntactic patterns’ (Hearst 1994), e.g. such NP as {NP, } 8 {(or| and)} NP e.g.: …works by such authors as Herrick, Goldsmith, and Shakespeare. • Considerable manual effort involved in identifying patterns (also language specific) • Developed by Morin (1999) but no evaluation

31. Labelling Relations:Adaptiva – Ontology building as IE

32. User & System Characteristics:A co-operative model of user/system interaction in the context of Knowledge Management • Characteristics of the user • Non-specialist • Can select a seed ontology • Can validate sentences as exemplars • Can label a relation exemplified • Characteristics of the system • Can process text at high speed • Can identify regularities • Can cluster patterns • Can establish that a relationship exists between term x and term y

33. Adaptiva • Input: • A corpus • A seed ontology, or some pairs of terms • A relation chosen or labelled by the user • Output: • A set of pairs of terms associated with labelled lexico-syntactic patterns • An extended ontology • Key concept is an effective User Interface to allow user validation/ training of the system

34. The Adaptiva System

35. Adaptiva Interface

36. Building on Labelled Relations • Most existing methods inadequate • Need to combine methods to compensate for different weaknesses • Effective pre-processing • Candidate associations from statistical methods • Term recognition • Use existing ontologies (e.g. Gene Ontology) to provide candidate data for machine learning

37. Background and Foreground Knowledge in Dynamic Ontology Construction

38. Overview • Texts as Knowledge Maintenance • Ontologies and Texts • Implicit and Explicit Knowledge • A Methodology • External resources: Potentials and Limitations • Conclusion

39. A shared conceptualisation • An ontology is “a formal, explicit specification of a shared conceptualisation, used to help programs and humans to share knowledge” (Gruber 1993) • Shared! i.e. concepts held in common by the participants/ community of practice • Therefore the ontology is the background knowledge assumed by the writer/reader of a text.

40. Texts Text and Knowledge • We want to generate ontologies from text • But if an ontology = shared/background knowledge, then a writer assumes the ontology to generate the text Ontology

41. Knowledge Maintenance • A text is an act of knowledge maintenance: • Re-enforcing assumptions of background knowledge • Altering links, associations and instantiations of existing concepts • Adding new concepts to the domain

42. Background Knowledge • If ontology = background knowledge, and background knowledge is implicit, then the text(s) will not express the domain ontology • Especially true of scientific papers • Less true of introductory textbooks, manuals, glossaries etc. • We expect to find specification of the ontological knowledge at the borders of a domain

43. Explicit vs. Implicit • Explicit ontological knowledge is found in lexico-syntactic phrases (Hearst 1992): • such NP as {NP, } 8 {(or| and)} NP e.g.: …works by such authors as Herrick, Goldsmith, and Shakespeare. • NP, a NP that e.g. isolation and characterisation of pbp, a protein that interacts …. • NP and other NPs e.g. … malignant melanomas and other cancer cell types … • Implicit is not machine-readable: e.g. ‘death is a biological process’ implied by Britannica article on death

44. An Approach • Assumption 1: No matter how large the corpus, a major part of the domain ontology will not be specified • Assumption 2: texts do specify explicitly ontological relations between terms • Therefore: Go beyond the corpus! Seek external sources to compensate the deficiencies of the corpus

45. Domain Corpus Using external sources External sources Mid and high level ontology Ontology Learner Low level ontology

46. External Sources • Encyclopaedias • Textbooks and manuals • Glossaries (manually identified) • Google glossaries (i.e. automatically identified) • The Internet • There are pros and cons for each of these potential sources

47. Some data • Using the Gene Ontology • Using the subset of Nature Corpus • 10 pairs of terms:

49. Example results

50. Overall results • Using the internet has the highest success rate – but still very poor