Evidence Sources, Methods and Use Cases for Learning Lightweight Domain Ontologies

By providing interoperability and shared meaning across actors and domains, lightweight domain ontologies are a cornerstone technology of the Semantic Web. This chapter investigates evidence sources for ontology learning and describes a generic and extensible approach to ontology learning that combines such evidence sources to extract domain concepts, identify relations between the ontology’s concepts, and detect relation labels automatically. An implementation illustrates the presented ontology learning and relation labeling framework and serves as the basis for discussing possible pitfalls in ontology learning. Afterwards, three use cases demonstrate the usefulness of the presented framework and its application to real-world problems.

[1]  Marti A. Hearst,et al.  A Method for Re ning Automatically-Discovered Lexical Relations: Combining Weak Techniques for Stronger Results , 1992 .

[2]  Marti A. Hearst Automatic Acquisition of Hyponyms from Large Text Corpora , 1992, COLING.

[3]  Elizabeth Chang,et al.  Semi-Automatic Ontology Extension Using Spreading Activation , 2005 .

[4]  Enrico Motta,et al.  Toward a New Generation of Semantic Web Applications , 2008, IEEE Intelligent Systems.

[5]  Harith Alani,et al.  Position paper: ontology construction from online ontologies , 2006, WWW '06.

[6]  Arno Scharl,et al.  Refining non-taxonomic relation labels with external structured data to support ontology learning , 2010, Data Knowl. Eng..

[7]  Albert Weichselbraun Applying Optimal Stopping for Optimizing Queries to External Semantic Web Resources , 2008, ICSOFT.

[8]  Jens Lehmann,et al.  DBpedia - A crystallization point for the Web of Data , 2009, J. Web Semant..

[9]  Cui Tao,et al.  Automatic hidden-web table interpretation, conceptualization, and semantic annotation , 2009, Data Knowl. Eng..

[10]  Mark Sanderson,et al.  A Study of User Interaction with a Concept-Based Interactive Query Expansion Support Tool , 2004, ECIR.

[11]  Philipp Cimiano,et al.  Ontology learning and population from text - algorithms, evaluation and applications , 2006 .

[12]  Marti A. Hearst,et al.  Refining Automatically-Discovered Lexical Relations: Combining Weak Techniques for Stronger Results , 1992 .

[13]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[14]  Pavel Blagoveston Bochev,et al.  A vector space model for information retrieval with generalized similarity measures. , 2012 .

[15]  P. Schmitz,et al.  Inducing Ontology from Flickr Tags , 2006 .

[16]  Arno Scharl,et al.  Multiple coordinated views for searching and navigating Web content repositories , 2009, Inf. Sci..

[17]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[18]  Marti A. Hearst,et al.  A Method for Refining Automatically-Discovered , 1992 .

[19]  Wendy Hall,et al.  The Semantic Web Revisited , 2006, IEEE Intelligent Systems.

[20]  James H. Martin,et al.  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .

[21]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[22]  Claudio Giuliano,et al.  Relation extraction and the influence of automatic named-entity recognition , 2007, TSLP.

[23]  Steffen Staab,et al.  Ontology Learning Part One - On Discoverying Taxonomic Relations from the Web , 2002 .

[24]  Hector Garcia-Molina,et al.  Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems , 2006 .

[25]  Enrico Motta,et al.  Bridging the gap between folksonomies and the semantic web: an experience report , 2007 .

[26]  A. Weichselbraun,et al.  Web Content Mining for Comparing Corporate and Third-Party Online Reporting : A Case Study on Solid Waste Management , 2009 .

[27]  Fabio Crestani,et al.  Application of Spreading Activation Techniques in Information Retrieval , 1997, Artificial Intelligence Review.

[28]  Zellig S. Harris,et al.  Mathematical structures of language , 1968, Interscience tracts in pure and applied mathematics.

[29]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .

[30]  Peter Mika Ontologies Are Us: A Unified Model of Social Networks and Semantics , 2005, International Semantic Web Conference.

[31]  Frank van Harmelen,et al.  Ontology matching using comprehensive ontology as background knowledge , 2006 .

[32]  Eduardo Mena,et al.  Querying the web: a multiontology disambiguation method , 2006, ICWE '06.

[33]  Oren Etzioni,et al.  Open Information Extraction from the Web , 2007, CACM.

[34]  F. Yates Contingency Tables Involving Small Numbers and the χ2 Test , 1934 .

[35]  Massimo Melucci,et al.  Vector Space Model , 2019, Syntactic n-grams in Computational Linguistics.

[36]  Shelley Powers,et al.  Practical RDF , 2003 .

[37]  Maria Ruiz-Casado,et al.  Automatising the learning of lexical patterns: An application to the enrichment of WordNet by extracting semantic relationships from Wikipedia , 2007, Data Knowl. Eng..

[38]  Enrico Motta,et al.  What Can be Done with the Semantic Web? An Overview Watson-based Applications , 2008, SWAP.

[39]  Qiong Luo,et al.  Towards Ontology Learning from Folksonomies , 2009, IJCAI.

[40]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[41]  W. Bruce Croft,et al.  Deriving concept hierarchies from text , 1999, SIGIR '99.

[42]  Elena García Barriocanal,et al.  Making use of upper ontologies to foster interoperability between SKOS concept schemes , 2006, Online Inf. Rev..

[43]  Óscar Corcho,et al.  Ontology based document annotation: trends and open research problems , 2006, Int. J. Metadata Semant. Ontologies.

[44]  Paul Buitelaar,et al.  Ontology Learning from Text: An Overview , 2005 .

[45]  Mohammed Bennamoun,et al.  Tree-Traversing Ant Algorithm for term clustering based on featureless similarities , 2007, Data Mining and Knowledge Discovery.

[46]  Enrico Motta,et al.  Integrating Folksonomies with the Semantic Web , 2007, ESWC.