1 automating ontology building: ontologies for the semantic web and knowledge management christopher...

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Automating Ontology Building:

Ontologies for the Semantic Web and

Knowledge Management

Christopher BREWSTERDepartment of Computer Science,

University of Sheffield

www.dcs.shef.ac.uk/~kiffer

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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

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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.)

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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!

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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)

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Problems: Knowledge Representation (2)

Ontologies Taxonomies

Pathfinder Networks, Mindmaps

Degree of formality, potential for inference MORE LESS

Continuum:Ontologies Taxonomies Other Semantic Networks

Differences lie in degree of logical rigour, formality and the potential for reasoning over the data structure

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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

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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)

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Specific Issues (1)1. High cost of human labour in initial

development / editorial task– Category construction– Content association

2. Knowledge is in continuous flux: ‘out of date on day of publication’

3. Ontologies/Taxonomies need to be domain specific

– General ontologies not very helpful without a lot of work

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Specific Issues (2)

4. Ontologies/Taxonomies reflect a particular perspective on the world e.g. categories like “business opportunity”

5. Categories are abstractions, derived from an analytic frame work e.g. ‘nouns’ or ‘business opportunity’

6. Ontologies = “shared conceptualisations” but often very difficult for human being to agree on categorising the world (e.g. problems with global ‘standards’)

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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

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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 ….

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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

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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!

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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

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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.

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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

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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’

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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

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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. ….

a

b c

d e

α

γ δ

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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.

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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)

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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:

tissue cell | growth cancer liver tumor | resistance disease lens

but also this produces term: antonyms as an output.

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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

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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

day

year

week

month

quarter

half

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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

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Constructing Hierarchies (4)

• Sanderson & Croft– Document-based lexical subsumption– Generates single term labels– Could allow use of existing hierarchy/

taxonomy

• Problem of coherence rock

igneous rock

basalt

rock (800 files)

basalt (129 files)

igneous rock (29 files)

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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 ….

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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

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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

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Labelling Relations:Adaptiva – Ontology building as IE

Lexico-Syntactic Patterns

Pairs of Terms

Labelled Relations

User Validation

User Validation

Pattern Extraction using Amilcare

Pattern Extraction using Amilcare

Based on Ciravegna’s ‘Lazy-NLP’ concepts and the Amilcare adaptive IE engine.

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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

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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

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The Adaptiva System Ontology

Pair of Terms

Amilcare/ a rule learning system

1. Training using examples 2. Retrieval of f urther

unlabelled examples

Examples with proposed

classification

User’s Feedback

Labelled set of examples

Corpus

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Adaptiva Interface

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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

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Background and Foreground Knowledge in Dynamic Ontology

Construction

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Overview

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

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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.

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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

TextsOntology

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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

Transport factors in the karyopherin- (also called importin- ) family mediate the movement of macromolecules in nuclear–cytoplasmic transport pathways. Karyopherin- 2 (transportin) binds a cognate import substrate and targets it to the nuclear pore complex. …. Here we present the 3.0 structure of the karyopherin- 2–Ran GppNHp complex where GppNHp is a non-hydrolysable GTP analogue.

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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

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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

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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

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Using external sources

Domain Corpus

Ontology Learner

External sources

Low level ontology

Mid and high level

ontology

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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

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Some data

• Using the Gene Ontology

• Using the subset of Nature Corpus

• 10 pairs of terms:1. histolysis isa tissue death

2. flocculation isa cell communication

3. vasoconstriction isa circulation

4. holin isa autolysin

5. aminopeptidase isa peptidase

6. death isa biological process

7. metallochaperone isa chaperone

8. hydrolase isa enzyme

9. ligase isa enzyme

10. conotoxin isa neurotoxin

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Terms Frequency Common environments

histolysis isatissue death

0 0

0

flocculation isacell communication

0 0

8

vasoconstriction isacirculation

0 0

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holin isaautolysin

0 0

8

aminopeptidase isapeptidase

3 0

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death isabiological process

654 0

12

metallochaperone isachaperone

0 0

50

hydrolase isaenzyme

9 2

672

ligase isaenzyme

92 2

672

conotoxin isaneurotoxin

1 0

3

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Example results

Terms: histolysis isa tissue death

Textual Source

Original corpus 0

Google citations 3, all from the ‘Gene Ontology’

Google glossary 0

Encyclopaedia (Britannica) 1, under ‘lepidopteran’ “tissues of the larva undergo considerable histolysis (breaking down)”

Dictionary (Britannica) histolysis: “the breakdown of bodily tissues”

Terms: ligase isa enzyme

Textual Source

Original corpus 2

Google citations 31k, many contexts where this is derivable:

“DNA ligase: Enzyme involved in the replication and repair”

but also:“ligase is a single polypeptide”“ligase is a 600 kDa

multisubunit protein”

Google glossary 9

Encyclopaedia (Britannica) 1 article with definition: “also called Synthetase any one of a class of about 50 enzymes that ….”

Dictionary (Britannica) ligase: “an enzyme that catalyzes the linking together of two molecules”

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Textual Source Number of contexts/ articles found

Clear specification of ontological relation

Original corpus 0 – 2 0/10

Google citations 3 - 31,000 6/10

Encyclopaedia 7/10 2/10

Dictionary 7/10 3/10

Overall results

• Using the internet has the highest success rate – but still very poor

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Problems

• Ambiguity: Many terms are defined for entirely different domains or contexts.

• Perspective: If the ontology has a particular perspective on the world, then the internet may not reflect that, i.e. the internet citations may ‘dumb down’.

• Data sparsity: Zipf law implies certain limits• Ontological coarseness: “vasoconstriction

IS-A circulation” cannot be found

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Conclusions

• A domain ontology reflects background knowledge

• This implicit i.e. never explicitly stated

• No corpus will ever provide sufficient citations to construct the corresponding ontology successfully

• External sources need to accessed

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Research Issues

• What external sources exist? What specialised sources exist?

• How does one (automatically ?) identify them? A web services application?

• How can we determine what ‘knowledge’ is absent from the corpus and decide to search elsewhere?

• Can we use external sources to vote on the ontological statement to be derived?

• What do we trust? The domain corpus, the internet, our human intuition? Why?