{"title":"概念建模的本体论基础","authors":"Giancarlo Guizzardi, Terry A. Halpin","doi":"10.3233/AO-2008-0046","DOIUrl":null,"url":null,"abstract":"What is needed to enable communication about observation and measurement results in information systems? Information system ontologies make a certain conceptualization explicit and partially account for the meanings of symbols associated with this conceptualization. Yet, the meaning of signs denoting measurement results such as “10 m”, “red” or “high” cannot be specified with currently available ontologies. They fail to separate the ontological nature of some observable quality from the specification of how to observe and name the measurement result. We employ the foundational ontology DOLCE for characterizing the ontological nature of observable magnitudes. This involves dealing with ontological questions like “What kinds of observable qualities exist, in which entity does the observed quality inhere and how are the magnitudes of the observed quality structured?”. Then, in order to capture the semantic aspects of an observation result, we introduce semantic reference spaces, which help deal with semantic questions like “Do the signs “10 m”, “33 feet” or “shallow” have the same meaning? Do these signs refer to the same entity, e.g. the depth magnitude of a lake? How to establish a unit of measure?\". We posit that the semantic questions can be approached efficiently only if agreement is reached on the ontological questions, and show that the specification of the meaning of signs denoting measurement results is enabled via the extension of the foundational ontology DOLCE with semantic reference spaces. This work was conducted while the author (Probst) was working at the Institute for Geoinformatics, University of Munster, Germany.","PeriodicalId":49238,"journal":{"name":"Applied Ontology","volume":"3 1","pages":"1-12"},"PeriodicalIF":2.5000,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AO-2008-0046","citationCount":"20","resultStr":"{\"title\":\"Ontological foundations for conceptual modelling\",\"authors\":\"Giancarlo Guizzardi, Terry A. Halpin\",\"doi\":\"10.3233/AO-2008-0046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"What is needed to enable communication about observation and measurement results in information systems? Information system ontologies make a certain conceptualization explicit and partially account for the meanings of symbols associated with this conceptualization. Yet, the meaning of signs denoting measurement results such as “10 m”, “red” or “high” cannot be specified with currently available ontologies. They fail to separate the ontological nature of some observable quality from the specification of how to observe and name the measurement result. We employ the foundational ontology DOLCE for characterizing the ontological nature of observable magnitudes. This involves dealing with ontological questions like “What kinds of observable qualities exist, in which entity does the observed quality inhere and how are the magnitudes of the observed quality structured?”. Then, in order to capture the semantic aspects of an observation result, we introduce semantic reference spaces, which help deal with semantic questions like “Do the signs “10 m”, “33 feet” or “shallow” have the same meaning? Do these signs refer to the same entity, e.g. the depth magnitude of a lake? How to establish a unit of measure?\\\". We posit that the semantic questions can be approached efficiently only if agreement is reached on the ontological questions, and show that the specification of the meaning of signs denoting measurement results is enabled via the extension of the foundational ontology DOLCE with semantic reference spaces. This work was conducted while the author (Probst) was working at the Institute for Geoinformatics, University of Munster, Germany.\",\"PeriodicalId\":49238,\"journal\":{\"name\":\"Applied Ontology\",\"volume\":\"3 1\",\"pages\":\"1-12\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2008-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.3233/AO-2008-0046\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Ontology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.3233/AO-2008-0046\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Ontology","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.3233/AO-2008-0046","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
What is needed to enable communication about observation and measurement results in information systems? Information system ontologies make a certain conceptualization explicit and partially account for the meanings of symbols associated with this conceptualization. Yet, the meaning of signs denoting measurement results such as “10 m”, “red” or “high” cannot be specified with currently available ontologies. They fail to separate the ontological nature of some observable quality from the specification of how to observe and name the measurement result. We employ the foundational ontology DOLCE for characterizing the ontological nature of observable magnitudes. This involves dealing with ontological questions like “What kinds of observable qualities exist, in which entity does the observed quality inhere and how are the magnitudes of the observed quality structured?”. Then, in order to capture the semantic aspects of an observation result, we introduce semantic reference spaces, which help deal with semantic questions like “Do the signs “10 m”, “33 feet” or “shallow” have the same meaning? Do these signs refer to the same entity, e.g. the depth magnitude of a lake? How to establish a unit of measure?". We posit that the semantic questions can be approached efficiently only if agreement is reached on the ontological questions, and show that the specification of the meaning of signs denoting measurement results is enabled via the extension of the foundational ontology DOLCE with semantic reference spaces. This work was conducted while the author (Probst) was working at the Institute for Geoinformatics, University of Munster, Germany.
Applied OntologyCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
4.80
自引率
30.00%
发文量
15
审稿时长
>12 weeks
期刊介绍:
Applied Ontology focuses on information content in its broadest sense. As the subtitle makes clear, two broad kinds of content-based research activities are envisioned: ontological analysis and conceptual modeling. The former includes any attempt to investigate the nature and structure of a domain of interest using rigorous philosophical or logical tools; the latter concerns the cognitive and linguistic structures we use to model the world, as well as the various analysis tools and methodologies we adopt for producing useful computational models, such as information systems schemes or knowledge structures. Applied Ontology is the first journal with explicit and exclusive focus on ontological analysis and conceptual modeling under an interdisciplinary view. It aims to establish a unique niche in the realm of scientific journals by carefully avoiding unnecessary duplication with discipline-oriented journals. For this reason, authors will be encouraged to use language that will be intelligible also to those outside their specific sector of expertise, and the review process will be tailored to this end. For example, authors of theoretical contributions will be encouraged to show the relevance of their theory for applications, while authors of more technological papers will be encouraged to show the relevance of a well-founded theoretical perspective. Moreover, the journal will publish papers focusing on representation languages or algorithms only where these address relevant content issues, whether at the level of practical application or of theoretical understanding. Similarly, it will publish descriptions of tools or implemented systems only where a contribution to the practice of ontological analysis and conceptual modeling is clearly established.