Upper ontology

About: Upper ontology is a research topic. Over the lifetime, 9767 publications have been published within this topic receiving 220721 citations. The topic is also known as: top-level ontology & foundation ontology.

Learning Expressive Ontologies

Abstract: The automatic extraction of ontologies from text and lexical resources has become more and more mature. Nowadays, the results of state-of-the-art ontology learning methods are already good enough for many practical applications. However, most of them aim at generating rather inexpressive ontologies, i.e. bare taxonomies and relationships, whereas many reasoning-based applications in domains such as bioinformatics or medicine rely on much more complex axiomatizations. Those are extremely expensive if built by purely manual efforts, and methods for the automatic or semi-automatic construction of expressive ontologies could help to overcome the knowledge acquisition bottleneck. At the same time, a tight integration with ontology evaluation and debugging approaches is required to reduce the amount of manual post-processing which becomes harder the more complex learned ontologies are. Particularly, the treatment of logical inconsistencies, mostly neglected by existing ontology learning frameworks, becomes a great challenge as soon as we start to learn huge and expressive axiomatizations. In this chapter we present several approaches for the automatic generation of expressive ontologies along with a detailed discussion of the key problems and challenges in learning complex OWL ontologies. We also suggest ways to handle different types of inconsistencies in learned ontologies, and conclude with a visionary outlook to future ontology learning and engineering environments.

Knowledge-driven multimedia information extraction and ontology evolution: bridging the semantic gap

Abstract: Bootstrapping Ontology Evolution with Multimedia Information Extraction.- Semantic Representation of Multimedia Content.- Semantics Extraction from Images.- Ontology Based Information Extraction from Text.- Logical Formalization of Multimedia Interpretation.- Ontology Population and Enrichment: State of the Art.- Ontology and Instance Matching.- A Survey of Semantic Image and Video Annotation Tools.

The use of foundational ontologies in ontology development: an empirical assessment

Abstract: There is an assumption that ontology developers will use a top-down approach by using a foundational ontology, because it purportedly speeds up ontology development and improves quality and interoperability of the domain ontology. Informal assessment of these assumptions reveals ambiguous results that are not only open to different interpretations but also such that foundational ontology usage is not foreseen in most methodologies. Therefore, we investigated these assumptions in a controlled experiment. After a lecture about DOLCE, BFO, and partwhole relations, one-third chose to start domain ontology development with an OWLized foundational ontology. On average, those who commenced with a foundational ontology added more new classes and class axioms, and significantly less object properties than those who started from scratch. No ontology contained errors regarding part-of vs. is-a. The comprehensive results show that the 'cost' incurred spending time getting acquainted with a foundational ontology compared to starting from scratch was more than made up for in size, understandability, and interoperability already within the limited time frame of the experiment.

Ontology and the Semantic Web

Abstract: In order for information systems supporting two different organizations to interoperate, there must be an agreement as to what the words mean. There are many such agreements in place, supporting information systems interoperation in many different application areas. Most of these agreements have been created as part of diverse systems development processes, but since the advent of the Semantic Web in the late 1990s, they have been studied as a kind of software artifact in their own right, called an ontology, or description of a shared world. This book brings together developments from philosophy, artificial intelligence and information systems to formulate a collection of functional requirements for ontology development. Once the functional requirements are established, the book looks at several ontology representation languages: RDFS, OWL, Common Logic and Topic Maps, to show how these languages support the functional requirements, what deficiencies there are, and how the languages relate to each other. Besides a collection of running examples used throughout the book, the entire treatment is supported by an extended example of a hypothetical ontology for the Olympic Games presented first as a set of chapter-end exercises and then as a set of solutions which illustrate the various points made in the text in the context of a single coherent development.

What Four Million Mappings Can Tell You about Two Hundred Ontologies

Abstract: The field of biomedicine has embraced the Semantic Web probably more than any other field. As a result, there is a large number of biomedical ontologies covering overlapping areas of the field. We have developed BioPortal--an open community-based repository of biomedical ontologies. We analyzed ontologies and terminologies in BioPortal and the Unified Medical Language System (UMLS), creating more than 4 million mappings between concepts in these ontologies and terminologies based on the lexical similarity of concept names and synonyms. We then analyzed the mappings and what they tell us about the ontologies themselves, the structure of the ontology repository, and the ways in which the mappings can help in the process of ontology design and evaluation. For example, we can use the mappings to guide users who are new to a field to the most pertinent ontologies in that field, to identify areas of the domain that are not covered sufficiently by the ontologies in the repository, and to identify which ontologies will serve well as background knowledge in domain-specific tools. While we used a specific (but large) ontology repository for the study, we believe that the lessons we learned about the value of a large-scale set of mappings to ontology users and developers are general and apply in many other domains.