An ontological framework for modeling the contents of definitions

Selja Seppälä. “An ontological framework for modeling the contents of definitions”. Terminology, 21(1):23–50, 2015.

[ pre-print ]

ABSTRACT
This paper addresses the troublesome question of feature selection and content prediction in definition writing. I present the basis of definition-authoring tools that can be used across a range of contexts, independently of the domain and language of the definitions. In addition to being domain- and language-independent, these tools should be easily tailorable to specific domains. Thus, my work seeks to contribute to developing generic definition-writing aids that can be tailored to a range of different contexts and domains. The objectives of this article are: (1) to show that it is possible to create implementable generic definition models; (2) to show how to constrain these models to produce definitions relevant to particular contexts; and (3) to propose an ontological analysis frameworkwith a fixed and well-motivated descriptive vocabulary that can be used in further content analysis studies in terminology and to enhance integration of textual definitions in ontologies.

Mapping WordNet to the Basic Formal Ontology

“Mapping WordNet to the Basic Formal Ontology”, in Buffalo Ontology Research Group Meeting, State University of New York at Buffalo, NY, June 8, 2015.

[ slides ]

ABSTRACT
We present preliminary work on the mapping of WordNet 3.0 to the Basic Formal Ontology (BFO 2.0). WordNet is a large semantic network linking sets of synonymous words (synsets) by means of semantic relations; it is widely used in natural language processing (NLP) tasks. BFO is a domain-neutral upper-level ontology that represents the types of things that exist in the world and relations between them. BFO serves as an integration hub for more specific ontologies, such as the Ontology for Biomedical Investigations (OBI) and the Cell Line Ontology (CLO). This work aims at creating a lexico‑semantic resource that can be used in NLP tools to perform ontology-related text manipulation tasks. Such tasks include semantic interpretation of natural language texts, word sense disambiguation, and information retrieval. The resource could, for example, be used to find terms in biomedical texts and link them to relevant BFO-based ontologies. Our semi-automatic mapping method consists in using existing mappings between WordNet and an upper-level ontology similar to BFO called KYOTO. The latter allows machines to reason over texts by providing interpretations of the words in ontological terms. Our working hypothesis is that a large portion of WordNet synsets can be semi-automatically mapped to BFO using simple mapping rules from KYOTO to BFO, e.g., ‘accomplishment > process’ and ‘#agentive-social-object > role’. The resulting mappings are to be read as ‘a WN synset X refers to something that is a subtype of BFO type Y’, e.g., the synset ‘immunity.n.02′ refers to a subtype of the BFO type ‘disposition’. We evaluate the method on medical synsets, examine preliminary results, and discuss issues related to the method. We conclude with suggestions for future work.

Mapping WordNet to the Basic Formal Ontology using the KYOTO ontology

Poster accepted to ICBO 2015

ABSTRACT
We present preliminary work on the mapping of WordNet 3.0 to the Basic Formal Ontology (BFO 2.0). WordNet is a large semantic network linking sets of synonymous words (synsets) by means of semantic relations; it is widely used in natural language processing (NLP) tasks. BFO is a domain-neutral upper-level ontology that represents the types of things that exist in the world and relations between them. BFO serves as an integration hub for more specific ontologies, such as the Ontology for Biomedical Investigations (OBI) and the Cell Line Ontology (CLO). This work aims at creating a lexico‑semantic resource that can be used in NLP tools to perform ontology-related text manipulation tasks. Such tasks include semantic interpretation of natural language texts, word sense disambiguation, and information retrieval. The resource could, for example, be used to find terms in biomedical texts and link them to relevant BFO-based ontologies. Our semi-automatic mapping method consists in using existing mappings between WordNet and an upper-level ontology similar to BFO called KYOTO. The latter allows machines to reason over texts by providing interpretations of the words in ontological terms. Our working hypothesis is that a large portion of WordNet synsets can be semi-automatically mapped to BFO using simple mapping rules from KYOTO to BFO, e.g., ‘accomplishment > process’ and ‘#agentive-social-object > role’. The resulting mappings are to be read as ‘a WN synset X refers to something that is a subtype of BFO type Y’, e.g., the synset ‘immunity.n.02′ refers to a subtype of the BFO type ‘disposition’. We evaluate the method on medical synsets, examine preliminary results, and discuss issues related to the method. We conclude with suggestions for future work.

Mapping WordNet to the Basic Formal Ontology using the KYOTO ontology

“Mapping WordNet to the Basic Formal Ontology using the KYOTO ontology”, in UB’s 7th Annual Postdoc Research Symposium, State University of New York at Buffalo, NY, June 1, 2015 (Poster).

ABSTRACT
We present preliminary work on the mapping of WordNet 3.0 to the Basic Formal Ontology (BFO 2.0). WordNet is a large semantic network linking sets of synonymous words (synsets) by means of semantic relations; it is widely used in natural language processing (NLP) tasks. BFO is a domain-neutral upper-level ontology that represents the types of things that exist in the world and relations between them. BFO serves as an integration hub for more specific ontologies, such as the Ontology for Biomedical Investigations (OBI) and the Cell Line Ontology (CLO). This work aims at creating a lexico‑semantic resource that can be used in NLP tools to perform ontology-related text manipulation tasks. Such tasks include semantic interpretation of natural language texts, word sense disambiguation, and information retrieval. The resource could, for example, be used to find terms in biomedical texts and link them to relevant BFO-based ontologies. Our semi-automatic mapping method consists in using existing mappings between WordNet and an upper-level ontology similar to BFO called KYOTO. The latter allows machines to reason over texts by providing interpretations of the words in ontological terms. Our working hypothesis is that a large portion of WordNet synsets can be semi-automatically mapped to BFO using simple mapping rules from KYOTO to BFO, e.g., ‘accomplishment > process’ and ‘#agentive-social-object > role’. The resulting mappings are to be read as ‘a WN synset X refers to something that is a subtype of BFO type Y’, e.g., the synset ‘immunity.n.02′ refers to a subtype of the BFO type ‘disposition’. We evaluate the method on medical synsets, examine preliminary results, and discuss issues related to the method. We conclude with suggestions for future work.

IWOOD 2014 Proceedings

2014 Boyce, Richard D. and Brochhausen, Mathias and Empey, Philip E. and Haendel, Melissa and Hogan, William R. and Malone, Daniel C. and Ray, Patrick and Ruttenberg, Alan and Seppälä, Selja and Stoecker, Christian J. and Zheng, Jie, Proceedings of The First International Workshop on Drug Interaction Knowledge Management (DIKR 2014), The Second International Workshop on Definitions in Ontologies (IWOOD 2014), and The Starting an OBI-based Biobank Ontology Workshop (OBIB 2014), CEUR Workshop Proceedings, Vol-1309, Houston, TX, USA, October 6-7, http://ceur-ws.org/Vol-1309/.

NCBO Webinar Series Talk

Modeling textual definition contents with BFO 2.0 and creating associated linguistic/NLP resources, NCBO Webinar Series, the National Center for Biomedical Ontology, October 29, 2014, http://www.bioontology.org/node/840.

[slides]

Talk at the UB Department of Biomedical Informatics

Modeling textual definition contents with BFO 2.0 and creating associated linguistic/NLP resources, Department of Biomedical Informatics, State University of New York at Buffalo, NY, USA, October 22, 2014.