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.

D1.8.1 Base upper-level ontology (BULO) Guidance 1

Abstract: An important practical approach to ontology generation is the use of background or pre-existing knowledge in the form of a basic upper-level ontology. Such an ontology can also be used for metadata generation and as a groundwork for the overall knowledge modelling and integration strategy of a KM environment. The essential contribution of this deliverable is a basic upper-level ontology called PROTON (PROTo ONtology), which is hereby introduced and documented. It contains about 300 classes and 100 properties, providing coverage of the general concepts necessary for a wide range of tasks, including semantic annotation, indexing, and retrieval of documents. The design principles can be summarized as follows (i) domain-independence; (ii) light-weight logical definitions; (iii) alignment with popular standards; (iv) good coverage of named entities and concrete domains (i.e. people, organizations, locations, numbers, dates, addresses). The ontology is originally encoded in a fragment of OWL Lite and split into four modules: System, Top, Upper, and KM (Knowledge Management).

Background and foreground knowledge in dynamic ontology construction

Abstract: Ontologies have become a key component in the Semantic Web and Knowledge management. One accepted goal is to construct ontologies from a domain specific set of texts. An ontology reflects the background knowledge used in writing and reading a text. However, a text is an act of knowledge maintenance, in that it re-enforces the background assumptions, alters links and associations in the ontology, and adds new concepts. This means that background knowledge is rarely expressed in a machine interpretable manner. When it is, it is usually in the conceptual boundaries of the domain, e.g. in textbooks or when ideas are borrowed into other domains. We argue that a partial solution to this lies in searching external resources such as specialized glossaries and the internet. We show that a random selection of concept pairs from the Gene Ontology do not occur in a relevant corpus of texts from the journal Nature. In contrast, a significant proportion can be found on the internet. Thus, we conclude that sources external to the domain corpus are necessary for the automatic construction of ontologies.

ONTOGENERATION: Reusing Domain and Linguistic Ontologies for Spanish Text Generation

Abstract: A significant problem facing the reuse of ontologies is to make their content more widely accessible to any potential user. Wording all the information represented in an ontology is the best way to ease the retrieval and understanding of its contents. This article proposes a general approach to reuse domain and linguistic ontologies with natural language generation technology, describing a practical system for the generation of Spanish texts in the domain of chemical substances. For this purpose the following steps have been taken: (a) an ontology in the chemicals domain developed under the METHONTOLOGY framework and the Ontology Design Environment (ODE) has been taken as knowledge source; (b) the linguistic ontology GUM (Generalized Upper Model) used in other languages has been extended and modified for Spanish; (c) a Spanish grammar has been built following the systemic-functional model by using the KPML (Komet-Penman Multilingual) environment. As result, the final system named Ontogeneration permits the user to consult and retrieve all the information of the ontology in Spanish.

Ontology Integration for Linked Data

Abstract: The Linked Open Data cloud contains tremendous amounts of interlinked instances with abundant knowledge for retrieval. However, because the ontologies are large and heterogeneous, it is time-consuming to learn all the ontologies manually and it is difficult to learn the properties important for describing instances of a specific class. To construct an ontology that helps users to easily access various data sets, we propose a semi-automatic system, called the Framework for InTegrating Ontologies, that can reduce the heterogeneity of the ontologies and retrieve frequently used core properties for each class. The framework consists of three main components: graph-based ontology integration, machine-learning-based approach for finding the core ontology classes and properties, and integrated ontology constructor. By analyzing the instances of linked data sets, this framework constructs a high-quality integrated ontology, which is easily understandable and effective in knowledge acquisition from various data sets using simple SPARQL queries.

Knowledge management applied to e-government services: the use of an ontology

Abstract: This paper is about the development and use of an ontology of e-government services. We identify the knowledge required to deliver e-government transaction services. Based on the SmartGov project, we describe the use of a domain map to assist in knowledge management and motivate the use of an ontology as a domain map. We describe the development of the e-government service ontology and give a few examples of its definitions. We explain why the SmartGov project has adopted taxonomies, derived from the ontology, as its domain map. We highlight issues in ontology development and maintenance.