acquisition of axioms in ontology learningarios/docs/seminar... · acquisition of axioms in...

Acquisition of axioms in ontology learning

Ana B. Rios-Alvarado

Information Technology LaboratoryCINVESTAV - Tamaulipas, Mexico

September, 4th 2013

Supervisor:Dr. Ivan Lopez-Arevalo


Outline

1 Introduction

2 The research problem

3 Objectives

4 The proposed approach

5 Methodology

6 Schedule of activities

7 Conclusions

8 References

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Introduction

Motivation

• The extensive use of e-mail, word processing, digital presenta-tions and audio/video have shifted the balance to unstructureddata in many organizations (corporations, governments, coo-peratives, universities, etc)

• The need of efficient mechanisms to access, use, and exchangeinformation resources

• The emergence of Semantic Web and its goal for doing unders-table and functional the information by computers

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Introduction

Some definitions...

What is an ontology?

• “... an element that defines the basic terms and relationscontained into the vocabulary of a topic area as the rules forcombining terms and relations to define extensions of a con-ceptualization” [Neches et al., 1991]

What is ontology learning?

• “Ontology learning is the set of methods and techniques usedfor building an ontology from scratch, enriching, or adaptingan existing ontology in a semi-automatic fashion using severalknowledge and information sources” [Gomez-Perez, 2003]

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Introduction

Some definitions ...

What is ontology learning from text?

• Essentially, it is the process of deriving high-level concepts andrelations as well as the axioms from unstructured informationto form an ontology

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Introduction

Some definitions ...

What are the elements of an ontology?

Element DescriptionConcepts (classes) Ideas to formalizeTaxonomic relationships Relation is-a or subClassOfNon-taxonomic Interaction between elementsrelationshipsInstances “real-world” objectsAxioms Theorems on relations

to be satisfied by elements

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Introduction

Example: Travel ontology1

• Taxonomic relation: Hotel is a Accommodation

• Non-taxonomic relation: Activity isOfferedAt Destination

• Axiom: disjointWith(Museums, Safari)

• Instance: “National Museum of Art” is an instance of Museum

1http://www.owl-ontologies.com/travel.owl

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Introduction

Ontology learning from text

The aspects and tasks in ontology learning from text are structuredas a set of layers 2

2Philipp Cimiano, Ontology Learning and Population from Text: Algorithms, Evaluation and Applications,

Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006. pag. 23

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Introduction

Some techniques for obtaining the vocabulary

• Linguistic analysis

• Example: “Hiking is an outdoor activity”• Tokenization: [Hiking] [is] [an] [outdoor] [activity]...• Part of Speech Tagging: [Hiking/NNP] [is/VBZ] [an/DT] ...• Parsing:

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Introduction


• Linguistic analysis• Example: “Hiking is an outdoor activity”• Tokenization: [Hiking] [is] [an] [outdoor] [activity]...• Part of Speech Tagging: [Hiking/NNP] [is/VBZ] [an/DT] ...• Parsing:

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Introduction


• Statistical analysis• Term weightning TD-IDF• Mutual Information

• Based on WordNet• Latent Semantic Analysis• Text clustering

The use of clusteringtechniques relies on theassumption that similar termsshare similar syntactic contexts

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Introduction

Some techniques for obtaining taxonomic relations

• Lexical databases (for example, WordNet)

• Linguistic approaches

• Co-ocurrence analysis

• Lexico-syntactic patterns

The lexical patterns occur frequently in many text genders

• Use of web search and Wikipedia as knowledge source

The web search uses as knowledge base the whole Web

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Introduction

Some techniques for obtaining taxonomic relationsLexical patterns

• An expression A is a hyponym of an expression B if the meaningof B is part of the meaning of A and A is a subordinate of B.By contrast, an expresion B is a hyperonym of A if B includesthe meaning of A and B is a superior to A.

Hearst’s PatternsA, and other BA, or other B

A is a BB, such as AB, including AB, specially A

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Introduction

Some techniques for obtaining taxonomic relationsQuerying the Web

Given a query is possible to do the analysis of the number of hitsretrieved by a search engine.

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Introduction

Some the techniques for automatic axiom extraction

Kind of axioms Description ExampleClass expression Allow relationships subClassOf(Car,CompactCar)

to be establishedbetween class

Object property Characterise and establish subPropertyOf(hasMother,

relationships between hasParent)

object property expressions

Assertion Axioms about sameIndividualAs(President Bush,

individuals G. W. Bush)

• Lexical patterns [Del Vasto Terrientes, et al., 2010]

• Transforming rules [Volker, et al., 2007a] and statistical analysis [Volker,et al., 2007b][Volker and Rudolph, 2008]

• Inductive logic programming [Lisi and Straccia, 2011]

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Introduction

Related workOntology learning from text approaches

[Cimiano et al., [Jiang and Tan, [Ochoa et al.,2005] 2010] 2011]

Domain Tourism and Sport Event and Oncology andFinance Terrorism Finance

(Spanish texts)

Phase Technique

Concepts Formal concept Statistical Linguisticanalysis analysis patterns

Taxonomic Formal concepts are Rule based Semanticrelations partially ordered algortihm roles

Non-taxonomic - Rule mining Semanticrelations algorithm roles

Axioms - - -

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The research problem

The problem

• The most of learned ontologies are limited to non-taxonomicrelationships

• Limitations in previous works:

• Clustering techniques have been applied only to solve is-arelations

• Assignment a label to a group with a semantic context• Dependency on corpus or linguistic databases for a specific

domain• Limited contexts into corpus for the lexical pattern matching• Ontology learning process without the axiom layer

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The research problem

Research questions...

• Is it possible to build an ontology from textual resources withhigh level of expressiveness?

• Is it possible to extract the vocabulary and their relationshipsby text clustering and web search?

• Is it possible to extract axioms using natural language process-ing techniques?

Hypothesis

Considering that text clustering can get the vocabulary of a corpus,web search can obtain taxonomic relationships, and natural languageprocessing techniques allows discover axioms, it is possible adapt andintegrate these techniques for ontology learning.

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Objectives

Objectives

General objective

Define an approach for ontology learning getting the vocabulary,relationships, and key axioms from textual resources

Specific objectives

• Study, analyze, and obtain a method to get the vocabulary from textusing a clustering algorithm

• Study, analyze, and design a method to extract taxonomic relation-ships between concepts

• Study, analyze, and design an unsupervised method for axiom learning

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Objectives

Objectives

Phases Techniques Expressiveness Resources

1) Topic extraction, 1) Hybrid clustering, Axioms Text documents

2) build taxonomy, and 2) web search, and (pdf, doc, docx, html,

3) learning axioms 3) NLP techniques pps, odt)

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The proposed approach

The proposed approach: main phases

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The proposed approach: main tasks

1 Extract topics• pre-processing: collect corpus, POS tagging, lemmatization,

delete stopwords• obtain the representation model of input corpus’s content,

feature selection (nouns/verbs)

2 Discovering taxonomic relations• build querys, execute web search

3 Axiom learning• linguistic analysis, named entity recognition

4 Evaluation of the obtained ontology• improvement the obtained ontology, evaluation and

comparison with “gold standard” ontologies

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Methodology

Methodology

Phase 1: Topic extraction

• To collect text corpus

• To pre-process the corpus to obtain the representation model

• To implement and adapt the clustering algorithm for obtaining thedomain vocabulary

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Methodology

Methodology

Phase 2: Discovering taxonomic relationships

• To analyze the methods to get taxonomic relations

• To implement and adapt the method to extract taxonomic relations

• To compare the obtained method with others approaches

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Methodology

Methodology

Phase 3: Axiom learning

• To analyze the methods to get axioms from text

• To implement and adapt the method to extract axioms

• To integrate the methods in a model for ontology learning

• To evaluate the obtained ontologies23 / 31


Schedule of activities


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Conclusions

Conclusions

• An approach for ontology learning from textual resources inenglish language about a specific domain has been presented

• Considering a model inspired by layer cake model, it has beenproposed to work in three main layers:

• vocabulary• taxonomic relations• axioms

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Conclusions

Conclusions

• The text clustering combined with linguistic analysis seems tobe a good technique to obtain the representative vocabulary ina specific domain

• The lexical-patterns have shown a good evidence to identifytaxonomic relationships

• The natural language processing techniques are useful in theaxiom extraction task

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References

References I

[Neches et al., 1991] Neches, R., Fikes, R., Finin, T., Gruber, T., Patil R., Swartout W. R. “Enabling

technology for knowledge sharing”. AI Magazine, 12, September 1991. pp. 36-56.

[Gomez-Perez, 2003] Gomez-Perez, A., Manzano-Macho, D. “A survey of ontology learning methods and

techniques”. Deliverable 1.5 OntoWeb, Universidad Politecnica de Madrid. 2003.

[Cimiano et al., 2005] Cimiano, P., Hotho, A., Staab, S. “Learning concept hierarchies from text corpora

using formal concept analysis”. Journal Artificial Intelligence Research, 24(1). 2005. pp. 305-339.

[Jiang and Tan, 2010] Jiang, X., Tan, A. “CRCTOL: A semantic-based domain ontology learning system”.

Journal American Society Information Science Technologies, 61(1). 2010. pp. 150-168.http://dx.doi.org/10.1002/asi.v61:1

[Ochoa et al., 2011] Ochoa, J.L., Hernandez-Alcazar, M.L., Valencia-Garcıa, R., and Martınez-Bejar, R. “A

semantic role-based methodology for knowledge acquisition from Spanish documents”. International Journalof the Physical Sciences, 6(7). 2011. pp. 1755-1765.

[Lisi and Straccia, 2011] Lisi, F. A., Straccia, U. “An Inductive Logic Programming Approach to Learning

Inclusion Axioms in Fuzzy Description Logics”. In Proceedings of the 26th Italian Conference onComputational Logic, 810, 2011, 57-71 pp.

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References

References II

[Del Vasto Terrientes, et al., 2010] Del Vasto Terrientes, L., Moreno, A., Sanchez A. “Discovery of relation

axioms from the Web”. In Proceedings of the 4th International Conference on Knowledge Science,Engineering and Management, 2010, 222-233 pp.

[Volker, et al., 2007a] Volker, J., Hitzler, P., Cimiano, P. Acquisition of owl dl axioms from lexical resources,

The Semantic Web: Research and Applications, LNCS, 4519, 2007, 670-685 pp.

[Volker, et al., 2007b] Volker, J., Vrandedic, D., Sure, Y., Hotho, A. Learning disjointness. In Proceedings of

the 4th European Conference on the Semantic Web: Research and Applications, LNCS, Springer, 2007,175-189 pp.

[Volker and Rudolph, 2008] Volker, J., Rudolph, S. Lexico-logical acquisition of OWL DL axioms. In

Proceedings of the International Conference on Formal Concept Analysis, LNAI, Springer, 4933, 2008, 62-77pp.

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Thanks a lot for your kind attention!

Ana B. [email protected]

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