It is generally agreed that the interpretation of information, in any form, is context-dependent. The goal of the recent Ontology Summit 2018 was to explore the various relationships spanning ontol...

There are many connections among artificial intelligence, learning, reasoning and ontologies. The Ontology Summit 2017 explored, identified and articulated the relationships among these areas. As p...

There are many connections among artificial intelligence, learning, reasoning and ontologies. The Ontology Summit 2017 explored, identified and articulated the relationships among these areas. As p...

An increasing amount of data is now available from public and private sources. Furthermore, the types, formats, and number of sources of data are also increasing. Techniques for extracting, storing, processing, and analyzing such data have been developed in the last few years for managing this bewildering variety based on a structure called a knowledge graph. Industry has devoted a great deal of effort to the development of knowledge graphs, and knowledge graphs are now critical to the functions of intelligent (...) virtual assistants such as Siri, Alexa, and Google Assistant. The goal of the Ontology Summit 2020 was to understand not only what knowledge graphs are but also where they originated, why they are so popular, the current issues, and their future prospects. The summit sessions examined many examples of knowledge graphs and surveyed the relevant standards that exist and are in development for knowledge graphs. The purpose of this Communiqué is to summarize our understanding from the Summit in order to foster research and development of knowledge graphs. (shrink)

Ontologies and related reasoning systems are key to the facilitation of semantic integration and interoperability. But several key questions are involved. How do we define the tools, methodologies...

With the increasing amount of software devoted to industrial automation and process control, it is becoming more important than ever for systems to be able to explain their behavior. In some domain...

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. (shrink)