SAM: A Tool for the Semi-Automatic Mapping and Enrichment of Ontologies

Ontologies are fundamental tools used with different purposes and with different modalities in different areas and communities. To guarantee the right level of quality, the most widely used ontologies are man-made. However, developing and maintaining them turns out to be extremely time-consuming. For this reason, there are approaches aiming at their automatic construction where ontologies are incrementally extended by extracting and integrating knowledge from existing sources. However, these approaches tend to reach an accuracy that, according to the application they need to serve, cannot be always considered satisfactory. Therefore, when a higher accuracy is necessary, manual or semi-automatic approaches are still preferable. In this paper we present a technique and a corresponding tool, that we called SAM (semi-automatic mapper), for the semi-automatic enrichment of an ontology through the mapping of an external source to the target ontology. As proved by our evaluation, the tool allows saving around 50% of the time required by purely manual approaches.

[1]  Fausto Giunchiglia,et al.  Encoding classifications into lightweight ontologies , 2007 .

[2]  Diane Vizine-Goetz,et al.  Vocabulary Mapping for Terminology Services , 2004, J. Digit. Inf..

[3]  Martin R. Gibbs,et al.  Mediating intimacy: designing technologies to support strong-tie relationships , 2005, CHI.

[4]  Fausto Giunchiglia,et al.  Mapping large-scale Knowledge Organization Systems , 2009 .

[5]  Geoff Sutcliffe,et al.  Large theory reasoning with SUMO at CASC , 2010, AI Commun..

[6]  Fausto Giunchiglia,et al.  Domains and context: First steps towards managing diversity in knowledge , 2012, J. Web Semant..

[7]  Fausto Giunchiglia,et al.  Discovering Missing Background Knowledge in Ontology Matching , 2006, ECAI.

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

[9]  Margaret-Anne D. Storey,et al.  A Cognitive Support Framework for Ontology Mapping , 2007, ISWC/ASWC.

[10]  Lois Mai Chan,et al.  FAST (Faceted Application of Subject Terminology): A Simplified LCSH-based Vocabulary , 2003 .

[11]  Dan Klein,et al.  Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network , 2003, NAACL.

[12]  Stefano Spaccapietra.  Journal on Data Semantics XV , 2011, Journal on Data Semantics XV.

[13]  I. McIlwaine.  Subject retrieval in a networked environment , 2002 .

[14]  Mary Czerwinski,et al.  Visualization of mappings between schemas , 2005, CHI.

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

[16]  Gerhard Weikum,et al.  YAGO: A Large Ontology from Wikipedia and WordNet , 2008, J. Web Semant..

[17]  Fausto Giunchiglia,et al.  Save Up to 99% of Your Time in Mapping Validation , 2010, OTM Conferences.

[18]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[19]  Heiner Stuckenschmidt,et al.  Ontology Alignment Evaluation Initiative: Six Years of Experience , 2011, J. Data Semant..

[20]  Johannes Keizer,et al.  Comparing Human and Automatic Thesaurus Mapping Approaches in the Agricultural Domain , 2008, Dublin Core Conference.

[21]  Ali Shiri,et al.  HILT: A Pilot Terminology Mapping Service with a DDC Spine , 2006 .

[22]  Heike Neuroth,et al.  Renardus: cross-browsing European subject gateways via a common classification system (DDC) , 2003 .

[23]  Natalya F. Noy,et al.  Semantic integration: a survey of ontology-based approaches , 2004, SGMD.

[24]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[25]  Alon Y. Halevy,et al.  Why Your Data Won’t Mix , 2005, ACM Queue.

[26]  Michael J. Witbrock,et al.  An Introduction to the Syntax and Content of Cyc , 2006, AAAI Spring Symposium: Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering.