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Ontology learning from

Swedish textJan Bothma

Supervisor: Eva Blomqvist

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What is the problem?

Computer support implemented manually

Problem Goals State of the art Contribution Recap

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Problem Goals State of the art Contribution Recap

Berners-Lee, Tim et al (May 1, 2001)."The Semantic Web". Scientific American

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Problem Goals State of the art Contribution Recap

Berners-Lee, Tim et al (May 1, 2001)."The Semantic Web". Scientific American

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What is the problem?

Computer support implemented manually Encode semantics

Problem Goals State of the art Contribution Recap

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What is the problem?

Computer support implemented manually Encode semantics

Problem Goals State of the art Contribution Recap

Encoding semantics is time consuming

Domain Expert knowledge Encoding knowledge a challenge in itself Keeping current

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What is the problem?

Computer support implemented manually Encode semantics

Encoding semantics is time consuming

Problem Goals State of the art Contribution Recap

One approach: Ontology learning Ontology learning from text

NLP, Computational Linguistics Information Extraction, Data mining, etc

Ontology learning from Swedish text

Problem is to encode domain semantics

from Swedish text

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What is the problem?

Problem Goals State of the art Contribution Recap

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Motivation and goals

Prototype system for OL from Swedish Much research in each subtask How does it fit together?

Problem Goals State of the art Contribution Recap

Investigate OL from Swedish text Identify relevant tools Target further research

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Motivation and goals

Specifically Preprocess natural language Swedish corpus Extract evidence for important

Concepts Subclass relations

Non-taxonomic relations Open domain Semi-supervised

Problem Goals State of the art Contribution Recap

Investigate OL from Swedish text Prototype system for OL from Swedish

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Relevant state of the art

Problem Goals State of the art

Preprocess Concepts

Taxonomy Relations OL Systems

Contribution Recap

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Relevant state of the art

Problem Goals State of the art

Wilson Wong, Wei Liu, and Mohammed Bennamoun. 2012. Ontology learning from

text: A look back and into the future.ACM Comput. Surv. 44, 4, Article 20(September 2012), 36 pages.

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Relevant state of the art

Part of Speech tagging

Compound splitting

Stemming/Lemmatisation

Syntactic analysis Chunking

Syntactic Dependencies

Word sense disambiguation Named entity recognition

Coreference resolution

Problem Goals State of the art

Preprocess Concepts

Taxonomy Relations OL Systems

Contribution Recap

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Relevant state of the art

Parsers Stanford TnT HunPOS MaltParser

Saldo Systems

GATE NLTK Korp

Corpus Workbench

Problem Goals State of the art

Preprocess Concepts

Taxonomy Relations OL Systems

Contribution Recap

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Relevant state of the art

Lexo-syntactic Patterns

TF-IDF

C-Value/NC-Value

PageRank, graph-based

Markov Logic Network-based syntactic parse

Problem Goals State of the art

Preprocess Concepts

Taxonomy Relations OL Systems

Contribution Recap

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Relevant state of the art

General taxonomies (e.g. WordNet)

Lexico-syntactic Patterns

Agglomerative Clustering

Distributional similarity

Formal Concept Analysis

Markov Logic Network-based syntactic parse

Problem Goals State of the art

Preprocess Concepts

Taxonomy Relations OL Systems

Contribution Recap

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Relevant state of the art

Association rule mining

Lexico-syntactic patterns

Graph theory

Markov Logic Network-based syntactic parse

Compound splitting/paraphrasing

Problem Goals State of the art

Preprocess Concepts

Taxonomy Relations OL Systems

Contribution Recap

P bl

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Relevant state of the art

Text2Onto

OntoUSP

OntoCMaps

OntoGain

Cross-language (Hjelm, Volk)

Problem Goals State of the art

Preprocess Concepts

Taxonomy Relations OL Systems

Contribution Recap

P bl

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Key idea and contribution

Problem Goals State of the art Contribution Recap

Prototype ontology learning from Swedish text

P bl

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Key idea and contribution

Problem Goals State of the art Contribution

P bl

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Preprocessing

CRP

tillhr

en

grupp

proteiner

,

s

k

pentraxiner

, och

utgr

en

del

av

det

akuta

inflammationssvaret

vid

t

ex

akuta

infektionssjukdomar

.

Problem Goals State of the art Contribution

Preprocess

Recap

Justkidding!

P bl

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Key idea and contribution

Korp POS Lemmatisation Semantic dependencies

GATE Traverse annotations programmatically Pattern match over annotations (JAPE)

Problem Goals State of the art Contribution

Preprocess

Concepts Taxonomy Relations Evaluation

Recap

Problem

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Key idea and contribution

Noun Phrase Linguistic Filter Adj* Noun+

C-value Reward log(|candidate|) Reward frequency of candidate Penalise occurrence as substring Reward independence from containing candidates

Problem Goals State of the art Contribution

Preprocess

Concepts Taxonomy Relations Evaluation

Recap

Term Confidence

kardiovaskulr sjukdom 1

lkartidningen nr 0.8797681

mm hg 0.6492587

et al 0.6396211

fysisk aktivitet 0.4494908

typ 2-diabetes 0.413715

kardiovaskulra hndelse 0.32784185

hg blodtryck 0.32001647

potentiell bindning 0.31713346

Problem

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Key idea and contribution

Hierarchical Agglomerative Clustering Similarity Measure

Reward common phrase head Reward common words Penalise disjoint words

Head = last noun in NP

Problem Goals State of the art Contribution

Preprocess

Concepts Taxonomy Relations Evaluation

Recap

Blodtryck

DiastoliskBlodtryck SystoliskBlodtryck HgBlodtryck

Problem

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Key idea and contribution

Hierarchical Agglomerative Clustering Lexico-syntactic pattern

Supersuch assub1,sub2,and/orsub3

Superssomsub1,sub2,och/ellersub3

Problem Goals State of the art Contribution

Preprocess

Concepts Taxonomy Relations Evaluation

Recap

Breda ochjourtunga specialiteter, ssomallmnkirurgiochfamiljemedicin,

JourtungaSpecialitet

FamiljemedicinAlmnkirurgi

Problem

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Key idea and contribution

Problem Goals State of the art Contribution

Preprocess

Concepts Taxonomy Relations Ontology Evaluation

Recap

Syntactic dependencies

Ultraljud visade trombos av vena portae

root

subject object

Problem

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Evaluation

Problem Goals State of the art Contribution

Preprocess

Concepts Taxonomy Relations Evaluation

Recap

Broad medical domain 1381 articles 12 + 8 hours

MeSH C14 - Hjrt-krlsjukdomar 312 Articles 3 + 1 hours

Candidates thousands of terms, hundreds of relations

Useful evidence Hundreds of terms, dozens of relations Ranking helps

Problem

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Future work

Ranking relations Thorough evaluation Provenance

User interaction, e.g. Corpus and rationale Ontology

Extensibility Evidence combination

Methods Cross-language

Ontology Consistency and Reasoning

Problem Goals State of the art Contribution

Preprocess Concepts Taxonomy Relations Evaluation

Recap

Problem

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Recap

Implementing computer support is manual

Encode semantics

Semi-supervised Ontology Learning

Problem Goals State of the art Contribution Result Recap