Slide1 : EACL-2006
11th Conference
of the European Chapter of the
Association for Computational Linguistics
Tutorial Notes
Ontology Learning from Text
Paul Buitelaar
DFKI GmbH, Saarbrücken
Philipp Cimiano
AIFB, Univ. of Karlsruhe
April 2006
Trento, Italy
Aims of the Tutorial : Aims of the Tutorial Present an Overview of Ontology Learning Methods in the Context of NLP Systems
Analyze Ontology Learning (from Text) as a Layered Set of Sub-Tasks
Discuss Methods, Evaluation and Available Tools for each Layer
Provide Pointers to Relevant Literature
Structure of the Tutorial : Structure of the Tutorial Introduction
Ontologies: Origin and Purpose
Ontologies for NLP
Applications: IR, IE, MT, QA
Ontologies and Lexical Semantics
Layers in Ontology Learning from Text
Terms
(Multilingual) Synonyms
Concept Formation – Intension & Extension
Relations
Concept & Relation Hierarchies
Axioms Schemata & General Axioms - Rules
Wrap Up
What have we Learned in the Tutorial?
Where are we Today?
Where are we Heading?
Part I : Part I Ontologies: Origin and Purpose
Ontologies in Philosophy : Ontologies in Philosophy A Branch of Philosophy that Deals with the Nature and Organization of Reality
Science of Being (Aristotle, Metaphysics)
What Characterizes Being?
Eventually, what is Being?
Ontologies in Computer Science : Ontologies in Computer Science Ontology refers to an engineering artifact
a specific vocabulary used to describe a certain reality
a set of explicit assumptions regarding the intended meaning of the vocabulary
An Ontology is
an explicit specification of a conceptualization [Gruber 93]
a shared understanding of a domain of interest [Uschold and Gruninger 96]
Why Develop an Ontology? : Why Develop an Ontology? Make domain assumptions explicit
Easier to exchange domain assumptions
Easier to understand and update legacy data
Separate domain knowledge from operational knowledge
Re-use domain and operational knowledge separately
A community reference for applications
Shared understanding of what information means
Applications of Ontologies : Applications of Ontologies NLP
Information Extraction, e.g. [Buitelaar et al. 06], [Stevenson et al. 05], [Mädche et al. 02]
Information Retrieval (Semantic Search), e.g. WebKB [Martin et al. 00], SHOE [Hendler et al. 00], OntoSeek [Guarino et al. 99]
Question Answering, e.g. [Sinha and Narayanan 05], [Schlobach et al. 04], Aqualog [Lopez and Motta 04], [Pasca and Harabagiu 01]
Machine Translation, e.g. [Nirenburg et al. 04], [Beale et al. 95], [Hovy and Nirenburg 92], [Knight 93]
Other
Business Process Modeling, e.g. [Uschold et al. 98]
Information Integration, e.g. [Kashyap 99], [Wiederhold 92]
Knowledge Management (incl. Semantic Web), e.g. [Fensel 01], [Mulholland et al. 2001], [Staab and Schnurr 00], [Sure et al. 00], [Abecker et al. 97]
Software Agents, e.g. [Gluschko et al. 99], [Smith and Poulter 99]
User Interfaces, e.g. [Kesseler 96]
Types of Ontologies [Guarino 98] : Types of Ontologies [Guarino 98] Describe very general concepts like space, time, event, which are independent of a particular problem or domain. Describe the vocabulary related to a generic domain by specializing the concepts introduced in the top-level ontology. Describe the vocabulary related to a generic task or activity by specializing the top-level ontologies. Concepts in application ontologies often correspond to roles played by domain entities while performing a certain activity.
Ontologies and Their Relatives : Ontologies and Their Relatives
Ontologies and Their Relatives (continued) : Ontologies and Their Relatives (continued)
Thesauri - Examples : Thesauri - Examples
Semantic Networks - Examples : Semantic Networks - Examples
Ontologies – Example (Geographical) : Ontologies – Example (Geographical) capital city Neckar Zugspitze GE Natural GE Inhabited GE country river mountain instance_of Germany Berlin Stuttgart is-a flow_through located_in capital_of flow_through flow_through located_in capital_of 367 length (km) 2962 height (m)
A Mathematical Definition [Stumme et al. 2003] : A Mathematical Definition [Stumme et al. 2003] Structure:
C: set of concept identifiers
R: set of relation identifiers
Part II : Part II Ontologies for NLP
Overview : Overview Ontologies in NLP Applications
Information Retrieval Query Expansion
Information Extraction Template Definition, Semantic Integration
Question Answering Question Analysis, Answer Selection
Machine Translation Interlingua
Summarization Semantic Graphs
Lexical Semantic / Ontological Knowledge
Inference and Reasoning for Semantic Interpretation
Compound Analysis, Coercion, Bridging, WSD, Semantic Roles
Lexical Semantic Theory
Qualia Structure, Meaning-Text Theory, Case Grammar, …
A Lexicon Model for Ontologies
LingInfo
Information Retrieval : Information Retrieval Query Expansion
Works for Short Queries [Voorhees 94]
Conceptual Indexing
Indexing with Respect to WordNet can Improve Retrieval - Even at Considerable WSD Noise Levels [Gonzalo 98]
Combination of Traditional Indexing with Semantic Features Improves Results in Cross-Lingual IR (e.g. [Volk et al. 02], [Vossen et al. 2006])
Document Clustering/Classification
Extended Bag-of-Words Model with WordNet or MeSH leads to 2-7% Improvement [Bloehdorn et al. 05]
Information Extraction : Information Extraction Class-based Template Definition
Allows for Reasoning over Extracted Templates with Respect to the Ontology (see e.g. [Nedellec and Nazarenko 05] for discussion)
Rule Induction
Discovering Semantically Similar Patterns (e.g. Unsupervised Approach w.r.t. WordNet [Stevenson and Greenwood 05])
Discourse Analysis
Event Co-Reference Resolution (e.g. LaSIE [Gaizauskas et al. 95])
Semantic Integration (“Template Merging”)
Extraction from Heterogeneous Sources (Text, Tables and other Semi-Structured Data, Image Captions) – SmartWeb [Buitelaar et al. 06a/b]
Multi-Document Information Extraction – ArtEquAKT [Alani et al. 03]
Question Answering : Question Answering Question Analysis
Ontology/WordNet-based Semantic Question Interpretation
e.g. [Pasca and Harabagiu 01]
Answer Selection
Ontology/WordNet-based Reasoning for Answer Type-Checking
Ontology of Events [Sinha and Narayanan 05]
Geographical Ontology, WordNet [Schlobach et al. 04]
WordNet [Pasca and Harabagiu 01]
Ontology-based Question Answering
Derive Answers from a Knowledge Base
e.g. Aqualog [Lopez and Motta 04]
Machine Translation & Summarization : Machine Translation & Summarization Conceptual Model for Interlingua in MT
Not much current work, but see e.g. [Hovy and Nirenburg 92], [Knight 93]
Background Knowledge from Relevant Ontologies in Concept-based Summarization
Not common, but see e.g. [Lee et al. 04], [Lenci et al. 02]
Multi-Document Concept-based Summarization
e.g. ArtEquAKT [Alani et al. 03]
Semantic Interpretation : Semantic Interpretation Ontology-based Inference and Reasoning for
Compound Analysis
headache medicine (medicine cure headache)
Metonymy and Coercion
The Boston office called (office > person, person part_of office)
I began the book (book > event, read telic book)
Bridging and Discourse
Peter bought a car. The engine runs well (engine part_of car)
Word Sense Disambiguation
in the corner (> location) / before the corner is taken (> event)
Beckham kicked the ball (kick > shot) / the referee (kick > foul)
Sense Disambiguation / Assignment : Sense Disambiguation / Assignment … with Wordnets
Domain independent, with high ambiguity rate
Sense Disambiguation based on semantic distance that exploit taxonomic and non-hierarchical structure, e.g. [Navigli and Velardi 04], [Resnik 98], [Agirre and Rigau 96]
… with Domain Ontology
Domain-specific, with low ambiguity rate
Primarily non-ambiguous Sense Assignment
In case of ambiguity, Sense Disambiguation based on:
Semantic Distance (Statistical) – as above
Formal Inference (Reasoning) and Hybrid Approaches – like Sense Resolution work of 70s/80s/early 90s (also: Qualia Structure), but with large scale domain-specific ontologies/corpora
Semantic Role Assignment : Semantic Role Assignment … with FrameNet and Similar
Domain independent
High ambiguity rate
Classifier Induction on the basis of training data
e.g. CoNLL Task on Semantic Role Labeling [Carreras and Marquez 04], [Baldewein et al. 04], [Gildea and Jurafsky 02]
… with Domain Ontology
Domain-specific
Less ambiguity
No availability of training data
Relation extraction/discovery
Lexical Semantics : Lexical Semantics Lexical Semantic Theory
Link Morphological/Syntactic Structure to Semantic Structure
Theories, e.g.
Generative Lexicon / Qualia Structure [Pustejovsky 95]
Lexical Functions / Meaning-Text Theory [Mel‘cuk and Polguere 87]
Case Grammar / FrameNet [Fillmore 68]
Ontology-Driven (Lexical) Semantic Interpretation
Link Ontological Knowledge - Classes, Relations, Properties - to Lexical/Terminological Realizations
Semantics is in the Ontology, not in the Lexicon!
LingInfo - A Model for Integrating Linguistic (Lexical) Information in Ontologies [Buitelaar, Sintek and Kiesel 05]
Ontology Meta-Classes for LingInfo : Ontology Meta-Classes for LingInfo
LingInfo Model : LingInfo Model
Example Instance: “Fußballspielers” (of the football player) : Example Instance: “Fußballspielers” (of the football player) Fußballspielers term morphSynDecomp de lang inst0 : LingInfo wordForm … singular number Fußballspielers ortographicForm Noun partOfSpeech male gender genitive case inst1 : InflectedWordForm isComposedOf singular number Fußballspieler ortographicForm Noun partOfSpeech male gender nominative case inst2 : Stem root Fußball orthographicForm modifier function isComposedOf semantics ... 1 analysisIndex inst3 : Stem … Spieler orthographicForm root 2 analysisIndex inst8 : Stem Spieler orthographicForm … inst1 : Root inst7 : Stem (Ball) inst5 : Stem (Fuß) inst4 : Root (Ball) inst6 : Root (Fuß)
o:BallObject
Part III : Part III Methods in Ontology Learning from Text
Motivation for Ontology Learning from Text : Motivation for Ontology Learning from Text Problem:
Knowledge Acquisition Bottleneck
Possible solution:
Data-driven Knowledge Acquisition
As text is massively available on the Web, ontology learning from text is an attractive option
OL from Text as Reverse Engineering : OL from Text as Reverse Engineering
OL from Text - Some “pre-History” : OL from Text - Some “pre-History” AI - Knowledge Acquisition
Since 60s/70s: Semantic Network Extraction and similar for Story Understanding
e.g. MARGIE (Schank et al. 73), LUNAR (Woods 73)
NLP - Lexical Knowledge Extraction
70s/80s/early 90s: Extraction of Lexical Semantic Representations from Machine Readable Dictionaries
e.g. ACQUILEX LKB (Copestake et al. 92)
80s/90s: Extraction of Semantic Lexicons from Corpora for Information Extraction Systems
e.g. AutoSlog (Riloff 93), CRYSTAL (Soderland et al. 95)
IR - Thesaurus Extraction
Since 60s: Extraction of Keywords, Thesauri and Controlled Vocabularies
e.g. (Sparck-Jones 66/86, 71), Sextant (Grefenstette 92), DR-Link (Liddy 94)
Some Current Work on OL from Text : Some Current Work on OL from Text Terms, Synonyms & Classes
Statistical Analysis
Patterns
(Shallow) Linguistic Parsing
Term Disambiguation & Compositional Interpretation
Taxonomies
Statistical Analysis & Clustering (e.g. FCA)
Patterns
(Shallow) Linguistic Parsing
WordNet
Relations
Anonymous Relations (e.g. with Association Rules)
Named Relations (Linguistic Parsing)
(Linguistic) Compound Analysis
Web Mining, Social Network Analysis
Definitions
(Linguistic) Compound Analysis (incl. WordNet) Overview of Current Work: Paul Buitelaar, Philipp Cimiano, Bernardo Magnini Ontology Learning from Text: Methods, Evaluation and Applications Frontiers in Artificial Intelligence and Applications Series, Vol. 123, IOS Press, July 2005.
Slide34 : Ontology Learning Layer Cake
Evaluation : Evaluation Gold Standard
Human Evaluation
Task-based
Other
Tools : Tools
Slide37 : Ontology Learning Layer Cake Terms (Multilingual) Synonyms Concept Formation Concept Hierarchy Relations Axiom Schemata General Axioms Relation Hierarchy
Terms : Terms Terms are at the basis of the ontology learning process
Terms express more or less complex semantic units
But what is a term?
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Extracted term candidates (phrases)
computer
terminal
computer terminal
? high-quality computer terminal
? top brand computer terminal
? HP terminal, DEC terminal, …
Term Extraction : Term Extraction Determine most relevant phrases as terms
Linguistic Methods
Rules over linguistically analyzed text
Linguistic analysis – Part-of-Speech Tagging, Morphological Analysis, …
Extract patterns – Adjective-Noun, Noun-Noun, Adj-Noun-Noun, …
Ignore Names (DEC, HP, …), Certain Adjectives (quality, top, …), etc.
Statistical Methods
Co-occurrence (collocation) analysis for term extraction within the corpus
Comparison of frequencies between domain and general corpora
Computer Terminal will be specific to the Computer domain
Dining Table will be less specific to the Computer domain
Hybrid Methods
Linguistic rules to extract term candidates
Statistical (pre- or post-) filtering
Statistical Analysis : Statistical Analysis Scores used in Term Extraction:
MI (Mutual Information) – Cooccurrence Analysis
TFIDF – Term Weighting
2 (Chi-square) – Cooccurrence Analysis & Term Weighting
Other
c-value/nc-value (Frantzi & Ananiadou, 1999)
Considers length (c-value) and context (nc-value) of terms
Domain Relevance & Domain Consensus (Navigli and Velardi, 2004)
Considers term distribution within (DC) and between (DR) corpora
TFIDF : TFIDF most popular weighting schema
(normalized word frequency) tf(w) term frequency (number of word occurrences in a document)
df(w) document frequency (number of documents containing the word)
N number of all documents
tfIdf(w) relative importance of the word in the document The word is more important if it appears
several times in a target document The word is more important if it appears in less documents
C- / NC-value : C- / NC-value Combination of:
C-value (indicator for termhood)
NC-value (contextual indicators for termhood)
C-value (frequency-based method sensitive to multi-word terms)
C- / NC-value : C- / NC-value NC-value (incorporation of information from context words indicating termhood)
C-/NC-value
Terms - Evaluation : Terms - Evaluation Gold Standard
handcrafted term lists (e.g. [Frantzi and Ananiadou 1999])
domain-specific ontology (vocabulary overlap) (e.g.
[Mädche 2002])
Human Evaluation
assessment of relevance of terms a posteriori
Task-based
Indirect evaluation of coverage in IE, IR, ...
Terms – Tools : Terms – Tools
Slide46 : Ontology Learning Layer Cake Terms (Multilingual) Synonyms Concept Formation Concept Hierarchy Relations Axiom Schemata General Axioms Relation Hierarchy
(Multilingual) Synonyms : (Multilingual) Synonyms Next step in ontology learning is to identify terms that share (some) semantics, i.e., potentially refer to the same concept
Synonyms (Within Languages)
‘100% synonyms’ don’t exist – only term pairs with similar meanings
Examples from http://thesaurus.com
terminal – video display – input device
graphics terminal - video display unit - screen
Translations (Between Languages)
‘100% translations’ don’t exist - only multilingual term pairs with similar meanings
Examples from http://dict.leo.org
input device (English) – Eingabegerät (German)
Back to English: input device, input unit, signal conditioning device
video display unit (English) – Videosichtgerät (German)
Extraction of Synonyms : Extraction of Synonyms Term Classification and Clustering
Classification
Classifying terms to existing class systems, e.g., by extending WordNet (with SynSets corresponding to classes)
Clustering
Clusters according to similar distributions, e.g., by measuring co-occurrence between terms
Extraction of Translations : Extraction of Translations Multilingual Term Classification and Clustering - see e.g. [Grefenstette, 1998]
Similar as with monolingual terms, but depending on translated contexts (i.e., document collections):
Parallel Corpora: Pairs of translated documents
Comparable Corpora: Pairs of documents in different languages on the same topic
In both cases ‘need to cross the language barrier’
Parallel Corpora: Term alignment according to document structure (layout, linguistic, semantic)
Comparable Corpora: Term alignment according to similar contexts, e.g. by translating context words (dictionary lookup)
Synoyms - Evaluation : Synoyms - Evaluation Gold Standard
TOEFL (Landauer – LSA: 64.45%, Turney – PMI-IR: 48-74%)
WordNet (problematic due to domain-independence, e.g. [Pantel and Lin 03])
WordNet „tuning“, e.g. [Cucchiarelli and Velardi 98], [Turcato 00], [Buitelaar and Sacaleanu 01]
Human Evaluation
Task-based
(Cross-lingual ) IR/QA - e.g. Query Expansion
Other
Artificial Evaluation (see [Grefenstette 94])
e.g. transform cell -> CELL in some contexts
Synonyms – Tools : Synonyms – Tools
Slide52 : Ontology Learning Layer Cake Terms (Multilingual) Synonyms Concept Formation Concept Hierarchy Relations Axiom Schemata General Axioms Relation Hierarchy
The Semiotic Triangle : The Semiotic Triangle Ogden & Richards, 1923
based on Structural Linguistics studies (de Saussure, 1916)
adopted in Knowledge Representation (e.g. Sowa, 1984)
Concepts: Intension, Extension, Lexicon : Concepts: Intension, Extension, Lexicon A term may indicate a concept, if we can define its
Intension
(in)formal definition of the set of objects that this concept describes
a disease is an impairment of health or a condition of abnormal functioning
Extension
a set of objects (instances) that the definition of this concept describes
influenza, cancer, heart disease, …
Discussion: what is an instance? - ‘heart disease’ or ‘my uncle’s heart disease’
Lexical Realizations
the term itself and its multilingual synonyms
disease, illness, Krankheit, maladie, …
Discussion: synonyms vs. instances – ‘disease’, ‘heart disease’, ‘cancer’, …
Concepts – Intension : Concepts – Intension Extraction of a Definition for a Concept from Text
Informal Definition
e.g., a gloss for the concept as used in WordNet
OntoLearn (Navigli and Velardi 04; Velardi et al. 05) uses natural language generation to compositionally build up a WordNet gloss for automatically extracted concepts
‘Integration Strategy’ : “strategy for the integration of …”
Formal Definition
e.g., a logical form that defines all formal constraints on class membership
Inductive Logic Programming, Formal Concept Analysis, …
Concepts – Extension : Concepts – Extension Extraction of Instances for a Concept from Text
Commonly referred to as Ontology Population
Relates to Knowledge Markup (Semantic Metadata)
Uses Named-Entity Recognition and Information Extraction
Instances can be:
Names for objects, e.g.
Person, Organization, Country, City, …
Event instances (with participant and property instances), e.g.
Football Match (with Teams, Players, Officials, ...)
Disease (with Patient-Name, Symptoms, Date, …)
Concepts – Lexicon: LingInfo : Concepts – Lexicon: LingInfo
Concept Formation - Evaluation : Concept Formation - Evaluation Concept Extension
Gold Standard
overlap on clusters, e.g. OntoBasis
overlap on set of instances w.r.t. KB (difficult)
Human Evaluation (e.g. OntoBasis)
Task Based
QA from KBs
Concept Intension (in/formal definitions)
Gold Standard (e.g. WordNet glosses, WikiPedia)
Human Evaluation (e.g. WordNet glosses [Velardi et al. 05])
Task Based
Ontology Engineering
Understanding
Consistency
Concept Formation – Tools : Concept Formation – Tools
Slide60 : Ontology Learning Layer Cake Terms (Multilingual) Synonyms Concept Formation Concept Hierarchy Relations Axiom Schemata General Axioms Relation Hierarchy
Taxonomy Extraction - Overview : Taxonomy Extraction - Overview Lexico-syntactic patterns
Distributional Similarity & Clustering
Linguistic Approaches
Taxonomy Extension/Refinement
Combination of Methods
Evaluation
Tools Matrix
Hearst Patterns [Hearst 1992] : Hearst Patterns [Hearst 1992] Patterns to extract a relation of interest fullfilling the following requirements:
They should occur frequently and in many text genres.
They should accurately indicate the relation of interest.
They should be recognizable with little or no pre-encoded knowledge.
Acquiring Hearst Patterns : Acquiring Hearst Patterns Hearst also suggests a procedure in order to acquire
such patterns from a corpus:
Decide on a lexical relation R of interest, e.g. hyponymy/hypernymy.
Gather a list of terms for which this relation is known to hold, e.g. hyponym(car, vehicle). This list can be found automatically using the Hearst patterns or by bootstrapping from an existing lexicon or knowledge base.
Find places in the corpus where these expressions occur syntactically near one another.
Find the commonalities and generalize the expressions in 3. to yield patterns that indicate the relation of interest.
Once a new pattern has been identified, gather more instances of the target relation and go to step 3.
Hearst Patterns - Examples : Hearst Patterns - Examples Examples for hyponymy patterns:
Vehicles such as cars, trucks and bikes
Such fruits as oranges, nectarines or apples
Swimming, running and other activities
Publications, especially papers and books
A seabass is a fish.
Hearst Patterns (Continued) : Hearst Patterns (Continued) Use regular expression defined over syntactic categories:
NP such as NP, NP, ... and NP
Such NP as NP, NP, ... or NP
NP, NP, ... and other NP
NP, especially NP, NP ,... and NP
NP is a NP.
...
Precision wrt. Wordnet: 55,46% (66/119) on the basis of New York Times corpus
[Cederberg and Widdows 03] report lower results: 40%
Extensions of Hearst’s approach : Extensions of Hearst’s approach Using Hearst Patterns for Anaphora Resolution
Poesio et al. 02 / Markert et al. 03
Additional Patterns
[Iwanska et al. 00]
Using Questions
[Sundblad 02]
Application to collateral texts
[Ahmad et al. 03]
Matching patterns on the Web
KnowItAll [Etzioni et al. 04-05], PANKOW [Cimiano et al. 04-05]
Improving Accuracy (LSA) & Coverage (Conjunctions)
[Cederberg and Widdows 03 ]
Learning Patterns
Snowball [Agichtein et al. 00], [Downey et al. 04], [Ravichandran and Hovy 02], [Snow et al. 04])
Improving Precision and Recall of Hearst patterns [Cederberg and Widdows 03] : Improving Precision and Recall of Hearst patterns [Cederberg and Widdows 03] Main Idea:
Improve precision by filtering hyponym pairs using their similarity in WordSpace (error reduction by 30%, P=58%)
Improve recall by using coordination information, i.e. A < B and coordinated(A,C) -> C < B
This yields a five-fold increase in recall while mantaining precision at P=54% using the WordSpace filtering technique.
Generalizing Patterns : Generalizing Patterns Pantel, Ravichandran, Hovy
using edit distance as a basis to generalize patterns
Snowball [Agichtein et al. 00]
patterns as triples of bag-of-words represented as vectors, i.e. (left,arg1,middle,arg2,right)
use dot product to calculate similarity
calculating centroid as a generalization of the pattern
Other
[Downey et al. 04]
[Ravichandran and Hovy 02]
[Snow et al. 04]
Taxonomy Extraction - Overview : Taxonomy Extraction - Overview Lexico-syntactic patterns
Distributional Similarity & Clustering
Linguistic Approaches
Taxonomy Extension/Refinement
Combination of Methods
Evaluation
Tools Matrix
Distributional Hypothesis & Vector Space Model : Distributional Hypothesis & Vector Space Model Harris, 1986
„Words are (semantically) similar to the extent to which they share similar words“
Firth, 1957
„You shall know a word by the company it keeps“
Idea: collect context information and represent it as a vector:
compute similarity among vectors wrt. a measure
Context Features : Context Features Four-grams [Schuetze 93]
Word-windows [Grefenstette 92]
Predicate-Argument relations (SUBJ/OBJ/COMPLEMENT)
Modifier Relations (fast car, the hood of the car)
[Grefenstette 92, Cimiano 04b, Gasperin et al. 03]
Appositions (Ferrari, the fastest car in the world)
[Caraballo 99]
Coordination (ladies and gentlemen)
[Caraballo 99, Dorow and Widdows 03]
Extracting contextual features : Extracting contextual features The museum houses an impressive collection of medieval and modern art. The building combines geometric abstraction with classical references that allude to the Roman influence on the region.
house_subj(museum)
house_obj(collection)
combine_subj(museum)
combine_obj(abstraction)
combine_with(reference)
allude_to(influence)
Clustering Concept Hierarchies from Text : Clustering Concept Hierarchies from Text Similarity-based
Set-theoretical
Soft clustering
Similarity-based Clustering : Similarity-based Clustering Similarity Measures:
Binary (Jaccard, Dine)
Geometric (Cosine, Euclidean/Manhattan distance)
Information-theoretic (Relative Entropy, Mutual Information)
(…)
Linkage Strategies:
Complete linkage
Average linkage
Single linkage
(…)
Methods:
Hierarchical agglomerative clustering
Hierarchical top-down clustering, e.g. Bi-Section KMeans
(…)
Hierarchical Agglomerative Clustering : Hierarchical Agglomerative Clustering car bus trip excursion
Bi-Section-KMeans : Bi-Section-KMeans
Clustering Concept Hierarchies : Clustering Concept Hierarchies Similarity-based
Set Theoretical
Soft clustering
Formal Concept Analysis [Ganter, Wille 1999] : Formal Concept Analysis [Ganter, Wille 1999] finds ‚closed‘ sets of attributes and
objects (Formal Concepts)
yields a hierarchy with a formal
interpretation in terms of subsumption
of attributes
Clustering – Comparison [Cimiano 04] : Clustering – Comparison [Cimiano 04]
Clustering Concept Hierarchies from Text : Clustering Concept Hierarchies from Text Similarity-based
Set-theoretical & Probabilistic
Soft clustering
What About Multiple Word Meanings? : What About Multiple Word Meanings? bank: financial institute or natural object?
At least two clusters!
So we need soft clustering algorithms:
Clustering By Committee (CBC) [Lin et al. 2002]
Gaussian Mixtures (EM)
PoBOC (Pole-Based Overlapping Clustering)
FCA
(...)
Challenge: recognize multiple word meanings!
Soft clustering aglorithms : Soft clustering aglorithms Principle underlying POBOC and CBC:
Construct first `poles‘ or ´committees´ corresponding to very homogeneous groups of words, e.g. monosemous words
At a second step, assign words which do not form poles or committes to one or more committees; these are the ambiguos words
Additional trick in CBC: once you assign a word to a committe, remove the overlapping features, i.e. substract the `meaning of the committee´
Approach by [Widdows and Dorow 2002] : Approach by [Widdows and Dorow 2002] Extract shallow grammatical
relations for words -> build a
context vector.
Apply LSA/LSI to reduce
dimension of co-occurrence
matrix.
Calculate similarity as the
cosine between the angle of
the corresponding vectors.
Senses of a word = disjoint
subgraphs
Scalability : Scalability Problem with clustering algorithms:
Compute at least pairwise similarity between words, i.e. O(n2k)
Idea of [Ravichandran, Pantel and Hovy]
Apply locality sensitive hash functions
i.e. approximate cosine measure by a randomized procedure
Randomly approximating the cosine measure : Randomly approximating the cosine measure where d is the number of random vectors!
Taxonomy Extraction - Overview : Taxonomy Extraction - Overview Lexico-syntactic patterns
Distributional Similarity & Clustering
Linguistic Approaches
Taxonomy Extension/Refinement
Combination of Methods
Evaluation
Tools Matrix
Linguistic Approaches : Linguistic Approaches Modifiers:
Modifiers (adjectives/nouns) typically restrict or narrow down the meaning of the modified noun, i.e.
e.g. isa(international credit card, credit card)
Yields a very accurate heuristic for learning taxonomic relations, e.g. OntoLearn [Velardi & Navigli], OntoLT [Buitelaar et al., 2004], TextToOnto [Cimiano et al.], [Sanchez et al., 2005]
Compositional interpretation of compounds [OntoLearn]
e.g. long-term debt
Disambiguate long-term and debt with respect to WordNet
Generate a gloss out of the glosses of the respective synsets:
long-term debt := „a kind of debt, the state of owing something (especially money), relating to or extending over a relatively long time“
Taxonomy Extraction - Overview : Taxonomy Extraction - Overview Lexico-syntactic patterns
Distributional Similarity & Clustering
Linguistic Approaches
Taxonomy Extension/Refinement
Combination of Methods
Evaluation
Tools Matrix
General Problem : General Problem
Hearst & Schuetze 1993 : Hearst & Schuetze 1993 For each word w in WordSpace:
collect the 20 nearest neighbors in space using the cosine measure,
compute the score si of category i for w as the number of nearest neighbors that are in i, and
assign w to the highest scoring category.
Widdows 2003 : Widdows 2003 For a target word w, find words from the corpus which are similar to those of w. Consider these corpus-derived neighbors N(w)
Map the target word w to the place in the taxonomy where the neighbors N(w) are most concentrated.
Crucial question: What does most concentrated mean?
Determine where they are `most concentrated´ : Determine where they are `most concentrated´ Maximization problem:
Taxonomy Extension/Refinement : Taxonomy Extension/Refinement Conclusions:
difficult problem
approaches not comparable (datasets,
measures, ontologies, number of concepts,...)
Taxonomy Extraction - Overview : Taxonomy Extraction - Overview Lexico-syntactic patterns
Distributional Similarity & Clustering
Linguistic Approaches
Taxonomy Extension/Refinement
Combination of Methods
Evaluation
Tools Matrix
Initial Blueprints for Combination : Initial Blueprints for Combination Ontology learning is error-prone, combination of techniques can be
expected to make results more accurate:
[Caraballo 99]
Label tree produced with hierarchical agglomerative clustering using lexico-syntactic patterns
[Cimiano 05b/c]
Guided Clustering
Integrate a hypernym oracle with agglomerative clustering
Classification-based approach
use features derived from several learning paradigms
[Cederberg & Widdows 03]
Increase accuracy and coverage of lexico-syntactic patterns by using LSA and coordination patterns
Hierarchical Agglomerative Clustering with Postprocessing : Hierarchical Agglomerative Clustering with Postprocessing Caraballo’s Method [Caraballo 1999]:
Agglomerative Clustering
Labeling Clusters with hypernyms derived from Hearst patterns
Removing unlabeled concepts thus compacting the hierarchy
Evaluation: select 20 nouns with at least 20 hypernyms and present them to human judges with the 3 best hypernyms for each
Results:
Best Hypernym: 33% (Majority) / 39% (Any)
Any Hypernym: 47.5% (Majority) / 60.5% (Any)
Classification-based approach [Cimiano et al. 2005b] : Classification-based approach [Cimiano et al. 2005b] isa(t1,t2)=p isaWN(t1,t2) isaHearst(t1,t2) isaWWW(t1,t2) isahead(t1,t2) Idea: Use as input features derived by applying different techniques, resources, etc. and find optimal combination in a supervised manner!
Results for Combination : Results for Combination
Concept Hierarchy - Evaluation : Concept Hierarchy - Evaluation Taxonomy Induction
Gold Standard - comparison with hand-crafted taxonomy (e.g. [Mädche 01], [Cimiano 05a])
Human Evaluation of is-a triples (e.g. [Hearst 92] [Caraballo 99], [Cimiano 05b], [Cimiano 05c])
Taxonomy Extension/Refinement
Gold Standard – leave-one-out method (e.g. [Mädche, Pekar and Staab 02])
Human Evaluation – a posteriori (e.g. [Hearst and Schütze 93])
Task-based
WSD (e.g. [Agirre and Rigau 96])
IE (e.g. [Stevenson and Greenwood 05])
Text classification / clustering (e.g. [Bloehdorn et al. 05])
Concept Hierarchy – Tools : Concept Hierarchy – Tools
Slide101 : Ontology Learning Layer Cake Terms (Multilingual) Synonyms Concept Formation Concept Hierarchy Relations Axiom Schemata General Axioms Relation Hierarchy
Specific Relations / Attributes : Specific Relations / Attributes Part-of [Charniak et al. 98]
X consists of Y
Qualia [Yamada et al. 04, Cimiano & Wenderoth 05]
Formal: such X as Y
Purpose: X is used for Y
Agentive: a ADV Xed Y
Causation [Girju 02], [Sanchez 04]
X leads to Y
Attributes [Poesio and Almuhareb 05]
Attributes [Poesio et al. 2005] : Attributes [Poesio et al. 2005] Distinguish:
Qualities (e.g. color of a car)
Parts (e.g. hood of a car)
Related-Objects (e.g. the track of the deer)
Activities (e.g. the repairing of the car)
Related-Agents (e.g. the driver of the car)
Non-Attributes (e.g. the majority of the deer)
Train classifier with the following features:
Morphological Information
Clustering Attributes on the basis of their attributes
Issuing question patterns to Google (What is the color of ? vs. *When is the color of?)
Attributive Use (the size of the X vs. The X of the size.)
Results:
2-Way-Classifier: 89.2% (Attribute), 55.1% (Non-Attribute)
5-Way Classifier: 79.9 – 93% (Attributes), 60.2% (Non-Attribute)
Qualia Structures : Qualia Structures Match patterns on the web to discover qualia relations [Cimiano and Wenderoth,2005]
Formal: Y such as X
Telic: X is used for Y
Constitutive: X is made of Y
Evaluation: judge assigns credits from 0 (wrong) to 3 (totally correct)
General Relations: Exploiting Linguistic Structure : General Relations: Exploiting Linguistic Structure OntoLT: SubjToClass_PredToSlot_DObjToRange Heuristic
Maps a linguistic subject to a class, its predicate to a corresponding slot for this class and the direct object to the range of the slot
TextToOnto: Acquisition of Subcategorization Frames
love(man,woman)
love(kid,mother)
love(kid,grandfather)
Problem related to acquisition of subcategorization frames and selectional restrictions in Natural Language Processing
e.g. [Resnik 97], [Ribas 95], [Clark and Weir 02] love(person,person)
Which Relations are Actually the Same? : Which Relations are Actually the Same? Clustering of verbs semantically according to their alternation behavior [Schulte im Walde 00]
Use EM algorithm
Examples:
{advise, teach, instruct}
{fly, move, roll}
{start, finish, stop, begin}
{fight, play}
{meet, play}
{need, like, want , desire}
Finding the Right Level of Abstraction : Finding the Right Level of Abstraction [Ciramita et al. 05]
Genia Corpus. + Genia Ontology
Verb-based relations
X activates B
Use X2 to decide to generalize or not (significance level)
Results:
83.3% of relations correct according to human evaluation
53.1% correctly generalized
Relations - Evaluation : Relations - Evaluation Gold Standard
e.g. [Cimiano et al. 06], [Schutz and Buitelaar 05], Mädche and Staab 00]
Human Evaluation
A posteriori (e.g. [Schutz and Buitelaar 05])
Task-based evaluation
QA, e.g. `Who killed JFK?´ maps to KILL (X:person, Y:person) -> answer type is person
Relations – Tools : Relations – Tools
Slide110 : Ontology Learning Layer Cake Terms (Multilingual) Synonyms Concept Formation Concept Hierarchy Relations Axiom Schemata General Axioms Relation Hierarchy
Axiom Schemata & General Axioms : Axiom Schemata & General Axioms DIRT (Discovery of Inference Rules from Text [Lin et al. 01])
calculate significant collocations on dependency paths
Examples: „X solves Y“
Y is solved by X, X resolves Y, X finds a solution to Y, X tries to solve Y, Y deals with X, Y is resolved by X, X addresses Y, X seeks a solution to Y, X do something about Y, ...
AEON [Völker et al. 05]:
Rigidity, Identity, Unity, Dependence
[Haase and Völker 05]
Disjointness Axioms on the basis of coordination:
i.e. disjoint(man,woman)
Axioms & Rules - Evaluation : Axioms & Rules - Evaluation Gold Standard
Human-defined axioms ([Völker et al. 05])
Human Evaluation
A posteriori
Task-based evaluation
Consistency of Ontologies
Tools - Axioms : Tools - Axioms
Part IV : Part IV WrapUp
Overview : Overview What have we learned in the tutorial?
Role of Ontologies in NLP and Vice Versa
Definition of Tasks in Ontology Learning
Where are we today?
Variety of (incomparable) Methods
Orientation Towards Comparison, Evaluation and Integration
Where are we heading?
Combinations of Methods
Integration of (Combinations of ) Methods into Ontology Life-Cycle
Formal Criteria for evaluation
What have we learned? : What have we learned? Ontologies and NLP: a crucial symbiosis
Top-Down: Ontologies provide domain knowledge that can be employed in disambiguation, interpretation, reasoning, etc.
Bottom-Up: NLP provides methods for data-driven ontology development
Variety of tasks and techniques
OL reuses techniques from NLP and ML
Evaluation
Lots of different types of evaluation (gold standard, human, other)
Results often uncomparable (datasets, measures)
Task-based evaluation is important
Where are we today? : Where are we today? A lot of methods, little combination, quite spurious results
Similarity-based techniques lead to spurious results
Pattern-based approaches lead to low recall
Currently only initial blueprints for combination
Applications
No real application of automatically learned ontologies
OL in Ontology Engineering
How can ontology learning techniques be integrated into the process of ontology engineering?
How can users be involved in semi-automatic quality assurance of OL (results)?
Where are we heading? : Where are we heading? Large scale data sets
Web-based methods to reduce data sparseness
Combination of methods
Improve quality of results by compensating for drawbacks of different methods
Comparison of methods
Need for shared tasks, gold standards, and evaluation measures to move the field forward
Applications
Demonstrate benefit of automatically learned ontologies
Thanks for your attention! : Thanks for your attention! Any questions?
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