© Paul Buitelaar: KnowledgeWeb Summer School, Spain - July 2004 Human Language Technology in Ontology Engineering Ontology Learning from Text Paul Buitelaar

© Paul Buitelaar: KnowledgeWeb Summer School, Spain - July 2004

Human Language Technology in Ontology

Engineering

Ontology Learning from Text

Paul Buitelaar DFKI GmbH

Language Techology LabDFKI Competence Center Semantic Web

Saarbrücken, Germany


Overview

HLT and Ontology Engineering

Automated Linguistic Analysis

Ontology Learning from Text

Further Issues: Evaluation

Conclusions


Ontology Lifecycle

Creating

Populating

Validating

Evolving

Maintaining

Deploying


HLT in the Ontology Lifecycle

Ontology(Knowledge)

Ontology Learning

Development & EvolutionLinguistic Analysis

to Extract Classes / Relations

Ontology Population

Knowledge Base GenerationLinguistic Analysis

to Extract Instances

Instances

Documents(Text)

HLT for Ontology Learning and Population from Text

Human Language Technology = Automated Linguistic Analysis

Classes,Relations/Properties


Automated Linguistic Analysis


Linguistic Analysis: Example

The Dell computer with a flat screen had to be rejected because of a failure in the motherboard.

Dell computerflat screen

motherboard

has-a

has-a

reject

failurelocation-of

animate-entity


Levels of Linguistic Analysis

Lexical Analysis Word Class: Part-of-Speech (also Semantic Class) Word Structure: Morphology

Phrase Analysis Sentence Structure: Phrases (if ‘shallow’: Chunks) Semantic Units

Dependency Structure Analysis Sentence Meaning: Predicate Argument Structure (Clause) Semantic Structure


Part-of-Speech, Morphology

Part-of-Speech e.g.: noun, verb, adjective, preposition, … PoS tag sets may have between 10 and 50 (or more) tags

Morphology Most languages have inflection and declination, e.g.:

Singular/Plural computer, computers Present/Past reject, rejected

Many languages have also complex (de)composition, e.g.:

Flachbildschirm (flat screen) > flach + Bildschirm> flach + Bild + Schirm


Phrases, Terms, Named Entities

Semantic Units Phrases (e.g. nominal - NP, prepositional - PP)

NP a flat screenPP with a flat screenNP (recursive) the Dell computer with a flat

screen a failure in the motherboard

Terms (domain-specific phrases)Dell computer

Dell computer with a flat screen

Named Entities (phrases corresponding to dates, names, …)

COMPANY Dell COMPANY Dell Computer Corporation PERSON Michael Dell


Dependency Structure (I)

Semantic Structure Dependencies between Predicates and Arguments

the Dell computer with a flat screen had to be rejected

PRED: rejectARG1: ENTITYARG2: ‘the Dell computer with a flat screen’

‘Logical Form’ : reject(x,y) & animate-entity(x) & computer(y) & …

Dependency Structure Analysis is based on:

Sub-categorization Frames

reject :: Subj:NP, Obj:NP

Selection Restrictions

reject :: Subj:NP:ANIMATE-ENTITY, Obj:NP:ENTITY


Dependency Structure (II)The Dell computer that has been rejected was claimed to have suffered from handling.

reject(e1,x1,y1) & animate-entity(x1) & Dell_computer(y1) & claim(e2,x2,e3) & animate-entity(x2) & suffer_from(e3,y1,y2) & handling (y2)

PRED claim < NULL, XCOMP >

SUBJ y1

XCOMP

PRED computer

MOD Dell

ADJUNCTPRED reject < NULL, SUBJ >

PRED suffer < SUBJ, OBL-from >

SUBJ y1

SUBJ y1

OBL-from handling

claim

y1

Dell reject

suffer

y1

y1handling

SUBJ

SUBJ

XCOMP

MOD ADJUNCT OBL-from

SUBJ

y1 : computer

Lexical Functional Grammar (LFG)


Ontology Learningfrom Text


Some History

Lexical Knowledge Extraction Extraction of lexical semantic representations (word meaning) from Machine Readable Dictionaries – 70‘s/80‘s Extraction of semantic lexicons from corpora for Information Extraction systems - 80‘s/90‘s, e.g. CRYSTAL (Soderland) Answer extraction in Question Answering, e.g. Webclopedia (Hovy)Thesaurus Extraction Similar work, (complex, multilingual) term extraction e.g. Sextant (Grefenstette); DR-Link (Liddy)

Ontology Learning from Text Similar work, (domain-specific) term / relation extraction e.g. TextToOnto (Maedche & Staab), OntoLearn (Velardi et al.) Discussed here: OntoLT (Buitelaar, Olejnik & Sintek)


TextToOntoAssociation

Rules


OntoLearn

Domain-Specific WordNet Tuning and Extension


OntoLT: Some Background

Ontology Learning from Text Taxonomy Extraction, Document Clustering

String-based, Document Level

“Unnamed” Relation Extraction, Word ClusteringStemming & Part-of-Speech, Token Level

Extraction of Terms, “Named” RelationsPred-Arg & Head-Mod Structure, Term Level

TextToOnto

OntoLearn

Text in Ontology Engineering Textual Grounding of Concepts

Retain Linguistic Contexts and Realizations

Text-based Ontology MonitoringCompare Language Use over Time


OntoLT: Some Background

Ontology Learning from Text Taxonomy Extraction, Document Clustering

String-based, Document Level

“Unnamed” Relation Extraction, Word ClusteringStemming & Part-of-Speech, Token Level

Extraction of Terms, “Named” RelationsPred-Arg & Head-Mod Structure, Term Level

Text in Ontology Engineering Textual Grounding of Concepts

Retain Linguistic Contexts and Realizations

Text-based Ontology MonitoringCompare Language Use over Time

OntoLT


OntoLT

What is it?OntoLT provides a middleware solution in ontology development that enables the ontology engineer to bootstrap or extend a domain-specific ontology from a relevant text collection

How does it work?1. automatic linguistic annotation2. automatic statistical preprocessing 3. interactive definition of mapping rules4. interactive user validation of candidates5. automatic integration into an ontology


OntoLT: Architecture

AnnotatedCorpus(XML)

Mappings

XML (Linguistic Structure) <=>

Protégé (Classes, Slots)

Extraction

Protégé

Edit Extracted Ontology

Corpus

Definitionof Mappings

LinguisticAnnotation

ExtractedOntology

OntoLT


<sentence … >…

<text> … <text>

<phrases> … <phrases>

<clauses> … </clauses>

</sentence>

<text> … <token id="t5" pos="ADJA" str="mittlere"> <lemma id="t5.l1">mittler</lemma> </token> <token id="t6" pos="NN" str="Patellarsehnendrittel"> <lemma id="t6.l1">patellar</lemma> <lemma id="t6.l2">Sehne</lemma> <lemma id="t6.l3">Drittel</lemma> </token> …

Linguistic Annotation

<phrases> … <phrase id="p2" from="t5" to="t6" type="NP"> <mod from="t5" to="t5" /> <head from="t6" to="t6" /> </phrase> … </phrases>

<clauses> <clause id="cl1" from="p1" to="p5" pred="p5" type="pass"> <arg id="a1" type="SUBJ" phrase="none" /> <arg id="a2" type="IOBJ" phrase="p1"/> <arg id="a3" type="DOBJ" phrase="p2" /> </clause> </clauses>

mittlere Patellarsehnendrittel(mid patellar ligament third)

An 40 Kniegelenkpräparaten wurden mittlere Patellarsehnendrittel mit einer neuen Knochenverblockungstechnik in einem zweistufigen Bohrkanal femoral fixiert.


Mapping Rules

Precondition LanguageVar (Y, XPath (Y)) Get all occurrences of element Y,

e.g. HeadNoun, Modifier, Subject, …ConcatConcatList

combined through AND, OR, NOT, EQUAL

OperatorsCreateCls create a new class with super-classAddSlot add a slot with range to a new or existing classCreateInst introduce an instance for a new or existing classFillSlotset the value of a slot of an instance


Mapping Rules

Precondition LanguageVar (Y, XPath (Y)) Get all occurrences of element Y,

e.g. HeadNoun, Modifier, Subject, …ConcatConcatList

combined through AND, OR, NOT, EQUAL

OperatorsCreateCls create a new class with super-classAddSlot add a slot with range to a new or existing classCreateInst introduce an instance for a new or existing classFillSlotset the value of a slot of an instance


Example Experiment

Ontology Extraction for Neurology Neurology Section of a Medical Corpus Medical Scientific Journal Abstracts – MuchMore Project

XML-based Linguistic Annotation PoS, Lemmatization, Phrases, Pred-Arg Structure

Statistical Preprocessing (chi-square) Select Domain-Relevant Linguistic Entities

Definition of Mapping Rules Define Operators for Selected Linguistic Entities

Generate & Validate Class/Slot Candidates Select Candidates for Integration in Neurology Ontology

Generate “Ontology Fragments” for Neurology














Further Issues

Future Development Organization of Class/Slot Candidate List

Inference & Clustering - “Graph Restructuring” Extend Statistical Preprocessing

Multiple Reference CorporaExtended Frequency Information

Include Machine Learning ApproachSemi-Automatic Definition of Mapping

RulesPerformance Evaluation Guidelines

ECAI04 Workshop on OLP Benchmark

Challenge within PASCAL NoE


Evaluation: What? -- Subtasks Classes

(Multilingual) Term ExtractionNamed-Entity RecognitionSimilarity ThesaurusTerm,Document Clustering

Class-Hierarchy (Taxonomy) Thesaurus ExtractionTerm,Document Clustering

Class-Properties (Relations)Relation Extraction? Formal Properties of Relations (Properties)

Class-Instances (Individuals)(Multilingual) Term ExtractionNamed-Entity RecognitionTerm,Document Classification


Evaluation: How?

By Sub-Task – Evaluation of: Classes – Term,NE Extraction,Clustering

Class-Hierarchy – Thesaurus Extraction

Class-Properties – Relation Extraction

Class-Instances – Term,NE Extraction,Classification

By Application – Evaluation of: Ontology Learning and Population – Gold Standard

IR,QA – Precision /Recall Increase with Ontology?

Interactive QA – Increased User Satisfaction?

Information Access – Increased User Performance?


Conclusions

Stay Tuned

OntoLT Release

To be Announced on Protégé-Discussion List

http://protege.stanford.edu/mailing-lists

Evaluation

Ontology Learning & Population (OLP) Challenge

Within PASCAL NoE - First Task Spring 2005

ECAI04 Workshop: Evaluation of Text-based OLP

http://olp.dfki.de/ECAI04/cfp.htm