The Functions of Definitions in Ontologies

Selja Seppälä, Alan Ruttenberg, and Barry Smith. “The Functions of Definitions in Ontologies”. In R. Ferrario and W. Kuhn, editors, Formal Ontology in Information Systems: Proceedings of the 9th International Conference (FOIS 2016), volume 283 of Frontiers in Artificial Intelligence and Applications, pages 37–50. IOS Press, Annecy, France, July 6-9 2016.

[ PDF | slides ]

ABSTRACT

To understand what ontologies do through their definitions, we propose a theoretical explanation of the functions of definitions in ontologies backed by empirical neuropsychological studies. Our goal is to show how these functions should motivate (i) the systematic inclusion of definitions in ontologies and (ii) the adaptation of definition content and form to the specific context of use of ontologies.

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 Basic Formal Ontology using the KYOTO ontology

Selja Seppälä, 2015, “Mapping WordNet to Basic Formal Ontology using the KYOTO ontology”, ICBO2015, International Conference on Biomedical Ontology 2015, Proceedings of the Main Conference, July, 27-30, Lisbon, Portugal.

BEST POSTER AWARD

[ extended abstract | flash presentationposter ]

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.

Semi-Automatic Mapping of WordNet to Basic Formal Ontology

Selja Seppälä, Amanda Hicks, and Alan Ruttenberg. “Semi-Automatic Mapping of WordNet to Basic Formal Ontology”. In V. B. Mititelu, C. Forăscu, C. Fellbaum, and P. Vossen, editors, Proceedings of the Eighth Global WordNet Conference, 369–376, Bucharest, Romania, January 27-30 2016.

[PDF | slides]

ABSTRACT

We present preliminary work on the map- ping of WordNet 3.0 to the Basic Formal Ontology (BFO 2.0). WordNet is a large, widely used semantic network. 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 Ontology for Biobanking (OBIB). This work aims at creating a lexico-semantic resource that can be used in NLP tools to perform ontology-related text manipu- lation tasks. Our semi-automatic mapping method consists in using existing map- pings between WordNet and the KYOTO Ontology. The latter allows machines to reason over texts by providing interpreta- tions 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 sim- ple mapping rules from KYOTO to BFO. We evaluate the method on a randomized subset of synsets, examine preliminary re- sults, and discuss challenges related to the method. We conclude with suggestions for future work.

Enhancing terminological knowledge with upper level ontologies

Selja Seppälä and Amanda Hicks, “Enhancing terminological knowledge with upper level ontologies”, in Proceedings of the 11th International Conference on Terminology and Artificial Intelligence (TIA 2015), Thierry Poibeau and Pamela Faber, Eds., CEUR Workshop Proceedings, Vol-1495, Granada, Spain, November 4-5, 2015, p. 179-182.

PDF | slides ]

ABSTRACT
In this communication, we advocate the use of upper level ontologies such as the Basic Formal Ontology (BFO) to enhance terminological resources and research. First, we present common issues in ontologized terminological work. Then, we review two projects that illustrate the potential advantages of integrating rigorous formal upper level ontologies. Finally, we discuss possible challenges and conclude with a sum- mary of the benefits that such ontologies can bring to both terminological theory and practice.

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

“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).

[ 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.