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.

Discovery of ontologies from knowledge bases

Abstract: Current approaches to building knowledge-based systems propose the development of an ontology as a precursor to building the problem-solver. This paper outlines an attempt to do the reverse and discover interesting ontologies from systems built without the ontology being explicit. In particular the paper considers large classification knowledge bases used for the interpretation of medical chemical pathology results and built using Ripple-Down Rules (RDR). The rule conclusions in these knowledge bases provide free-text interpretations of the results rather than explicit classes. The goal is to discover implicit ontological relationships between these interpretations as the system evolves. RDR allows for incremental development and the goal is that the ontology emerges as the system evolves. The results suggest that approach has potential, but further investigation is required before strong claims can be made.

Ontology enrichment through automatic semantic annotation of on-line glossaries

Abstract: The contribution of this paper is to provide a methodology for automatic ontology enrichment and for document annotation with the concepts and properties of a domain core ontology. Natural language definitions of available glossaries in a given domain are parsed and converted into formal (OWL) definitions, compliant with the core ontology property specifications. To evaluate the methodology, we annotated and formalized a relevant fragment of the AAT glossary of art and architecture, using a subset of 10 properties defined in the CRM CIDOC cultural heritage core ontology, a recent W3C standard.

Cicero: tracking design rationale in collaborative ontology engineering

Abstract: Creating and designing an ontology is a complex task requiring discussions between domain and ontology engineering experts as well as the users of an ontology. We present the Cicero tool, that facilitates efficient discussions and accelerates the convergence to decisions. Furthermore, by integrating it with an ontology editor, it helps to improve the documentation of an ontology.

Ontorat: automatic generation of new ontology terms, annotations, and axioms based on ontology design patterns

Abstract: Background: It is time-consuming to build an ontology with many terms and axioms. Thus it is desired to automate the process of ontology development. Ontology Design Patterns (ODPs) provide a reusable solution to solve a recurrent modeling problem in the context of ontology engineering. Because ontology terms often follow specific ODPs, the Ontology for Biomedical Investigations (OBI) developers proposed a Quick Term Templates (QTTs) process targeted at generating new ontology classes following the same pattern, using term templates in a spreadsheet format. Results: Inspired by the ODPs and QTTs, the Ontorat web application is developed to automatically generate new ontology terms, annotations of terms, and logical axioms based on a specific ODP(s). The inputs of an Ontorat execution include axiom expression settings, an input data file, ID generation settings, and a target ontology (optional). The axiom expression settings can be saved as a predesigned Ontorat setting format text file for reuse. The input data file is generated based on a template file created by a specific ODP (text or Excel format). Ontorat is an efficient tool for ontology expansion. Different use cases are described. For example, Ontorat was applied to automatically generate over 1,000 Japan RIKEN cell line cell terms with both logical axioms and rich annotation axioms in the Cell Line Ontology (CLO). Approximately 800 licensed animal vaccines were represented and annotated in the Vaccine Ontology (VO) by Ontorat. The OBI team used Ontorat to add assay and device terms required by ENCODE project. Ontorat was also used to add missing annotations to all existing Biobank specific terms in the Biobank Ontology. A collection of ODPs and templates with examples are provided on the Ontorat website and can be reused to facilitate ontology development. Conclusions: With ever increasing ontology development and applications, Ontorat provides a timely platform for generating and annotating a large number of ontology terms by following design patterns. Availability: http://ontorat.hegroup.org/

Ontology Summit 2007 --Ontology, taxonomy, folksonomy: Understanding the distinctions

Abstract: Under the appellation of “ontology” are found many different types of artifacts created and used in different communities to represent entities and their relationships for purposes including annotating datasets, supporting natural language understanding, integrating information sources, semantic interoperability and to serve as a background knowledge in various applications. The Ontology Summit 2007 “Ontology, taxonomy, folksonomy: Understanding the distinctions”,1 was an attempt to bring together various communities (computer scientists, information scientists, philosophers, domain experts) having a different understanding of what is an ontology, and to foster dialog and cooperation among these communities. In practice, ontologies cover a spectrum of useful artifacts, from formal upper-level ontologies expressed in first order logic, such as Basic Formal Ontology (BFO), Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE), Suggested Upper Merged Ontology (SUMO), and Process Specification Language (PSL), to folksonomies (the simple lists of user-defined keywords to annotate resources on the Web). In between these two extremities of the ontology spectrum are taxonomies, conceptual models and controlled vocabularies such as Medical Subject Headings (MeSH), often used for information indexing and retrieval, and whose organization is mostly hierarchical. Finally, there are ontologies which represent not only subsumption, but also other kinds of relationships among entities (e.g., functional, physical), often based on formalisms such as frames or description logics. Examples of such ontologies in the biomedical domain include the Foundational Model of Anatomy, SNOMED CT and the NCI Thesaurus. The goal of the Ontology Summit was not to establish a definitive definition of the word “ontology”, which has proven to be extremely challenging due to the diversity of artifacts it can refer to. Rather, the results of the Summit identified a limited number of key dimensions along which ontologies can