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Article type: Research Article
Authors: Baclawski, Kena; * | Bennett, Michaelb | Berg-Cross, Garyc | Dickerson, Leiad | Schneider, Todde | Seppälä, Seljaf | Sharma, Ravig | Sriram, Ram D.h | Westerinen, Andreai
Affiliations: [a] Northeastern University, Boston, MA, USA | [b] Hypercube Limited, London, UK | [c] ESIP Semantic Harmonization | [d] Government Accountability Office, Washington, DC, USA | [e] Engineering Semantics, Fairfax, VA, USA | [f] University College Cork, Ireland | [g] Elk Grove, CA, USA | [h] National Institute of Standards and Technology, Gaithersburg, MD, USA | [i] OntoInsights LLC, Elkton MD, USA
Correspondence: [*] Corresponding author. E-mail: kenbaclawski@gmail.com.
Note: [] Accepted by: Michael Gruninger
Abstract: Advances in machine learning and the development of very large knowledge graphs have accompanied a proliferation of ontologies of many types and for many purposes. These ontologies are commonly developed independently, and as a result, it can be difficult to communicate about and between them. To address this difficulty of communication, ontologies and the communities they serve must agree on how their respective terminologies and formalizations relate to each other. The process of coming into accord and agreement is called “harmonization.” The Ontology Summit 2021 examined the overall landscape of ontologies, the many kinds of ontology generation and harmonization, as well as the sustainability of ontologies. The Communiqué synthesizes and summarizes the findings of the summit as well as earlier summits on related issues. One of the major impediments to harmonization is the relatively poor quality of natural language definitions in many ontologies. The summit surveyed the state of the art in natural language definition development, based on lexicographic principles, as well as examples of ongoing projects that are explicitly dealing with harmonization and sustainability.
Keywords: Ontology, machine learning, definitions, sustainability, harmonization
DOI: 10.3233/AO-220266
Journal: Applied Ontology, vol. 17, no. 2, pp. 233-248, 2022
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