Foundations for OWLS: Aligning OWLS to DOLCE

AAAI Conferences

Clarity in semantics and a rich formalization of this semantics are important requirements for ontologies designed to be deployed in large-scale, open, distributed systems such as the envisioned Semantic Web. This is especially true for the description of web services, which should enable complex tasks involving multiple agents. As one of the first initiatives of the Semantic Web community for describing web services, OWLS attracts a lot of interest and increases its user base even though it is still under development. Our contribution to the development of this ontology is to identify some of its problematic aspects and to suggest enhancements through alignment to a foundational ontology. However, the contribution of our work is not limited to the concrete results reported in this paper, but rather consists of examples of the benefits of alignment to foundational ontologies and a description of the method itself.


Towards an Ontology for Data-driven Discovery of New Materials

AAAI Conferences

Materials scientists and nano-technologists are struggling with the challenge of managing the large volumes of multivariate, multidimensional and mixed-media data sets being generated from the experimental, characterisation, testing and post-processing steps associated with their search for new materials. In addition, they need to access large publicly available databases containing: crystallographic structure data; thermodynamic data; phase stability data and ionic conduction data. Materials scientists are demanding data integration tools to enable them to search across these disparate databases and to correlate their experimental data with the public databases, in order to identify new fertile areas for searching. Systematic data integration and analysis tools are required to generate targeted experimental programs that reduce duplication of costly compound preparation, testing and characterisation. This paper presents MatOnto - an extensible ontology, based on the DOLCE upper ontology, that aims to represent structured knowledge about materials, their structure and properties and the processing steps involved in their composition and engineering. The primary aim of MatOnto is to provide a common, extensible model for the exchange, reuse and integration of materials science data and experimentation.


Sweetening WORDNET with DOLCE

AI Magazine

Example from the LOOM WORDNet Knowledge Base. At the beginning, we assumed that the hyponymy relation could simply be mapped onto the subsumption relation and that the synset notion could be mapped into the notion of concept. Both subsumption and concept have the usual description logic semantics (Woods and Schmolze 1992). LOOM WORDNET knowledge base are reported in table 1. Fig-ORDNET's noun top Under Territorial_-Dominion, we find Macao and Palestine together with Trust_Territory. The Trust_Territory synset, defined as "a dependent country, administered by a country under the supervision of United Nations," denotes a general kind of country rather than a specific country such as Macao or Palestine.


Sweetening WORDNET with DOLCE

AI Magazine

Despite its original intended use, which was very different, WORDNET is used more and more today as an ontology, where the hyponym relation between word senses is interpreted as a subsumption relation between concepts. In this article, we discuss the general problems related to the semantic interpretation of WORDNET taxonomy in light of rigorous ontological principles inspired by the philosophical tradition. Then we introduce the DOLCE upper-level ontology, which is inspired by such principles but with a clear orientation toward language and cognition. We report the results of an experimental effort to align WORDNET's upper level with DOLCE. We suggest that such alignment could lead to an "ontologically sweetened" WORDNET, meant to be conceptually more rigorous, cognitively transparent, and efficiently exploitable in several applications.


The African Wildlife Ontology tutorial ontologies: requirements, design, and content

arXiv.org Artificial Intelligence

Background. Most tutorial ontologies focus on illustrating one aspect of ontology development, notably language features and automated reasoners, but ignore ontology development factors, such as emergent modelling guidelines and ontological principles. Yet, novices replicate examples from the exercises they carry out. Not providing good examples holistically causes the propagation of sub-optimal ontology development, which may negatively affect the quality of a real domain ontology. Results. We identified 22 requirements that a good tutorial ontology should satisfy regarding subject domain, logics and reasoning, and engineering aspects. We developed a set of ontologies about African Wildlife to serve as tutorial ontologies. A majority of the requirements have been met with the set of African Wildlife Ontology tutorial ontologies, which are introduced in this paper. The African Wildlife Ontology is mature and has been used yearly in an ontology engineering course or tutorial since 2010 and is included in a recent ontology engineering textbook with relevant examples and exercises. Conclusion. The African Wildlife Ontology provides a wide range of options concerning examples and exercises for ontology engineering well beyond illustrating only language features and automated reasoning. It assists in demonstrating tasks about ontology quality, such as alignment to a foundational ontology and satisfying competency questions, versioning, and multilingual ontologies.