341 research outputs found

    Minimal Aristotelian Ontology

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    The Industrial Ontologies Foundry proof-of-concept project

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    The current industrial revolution is said to be driven by the digitization that exploits connected information across all aspects of manufacturing. Standards have been recognized as an important enabler. Ontology-based information standard may provide benefits not offered by current information standards. Although there have been ontologies developed in the industrial manufacturing domain, they have been fragmented and inconsistent, and little has received a standard status. With successes in developing coherent ontologies in the biological, biomedical, and financial domains, an effort called Industrial Ontologies Foundry (IOF) has been formed to pursue the same goal for the industrial manufacturing domain. However, developing a coherent ontology covering the entire industrial manufacturing domain has been known to be a mountainous challenge because of the multidisciplinary nature of manufacturing. To manage the scope and expectations, the IOF community kicked-off its effort with a proof-of-concept (POC) project. This paper describes the developments within the project. It also provides a brief update on the IOF organizational set up

    The Requirements for Ontologies in Medical Data Integration: A Case Study

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    Evidence-based medicine is critically dependent on three sources of information: a medical knowledge base, the patients medical record and knowledge of available resources, including where appropriate, clinical protocols. Patient data is often scattered in a variety of databases and may, in a distributed model, be held across several disparate repositories. Consequently addressing the needs of an evidence-based medicine community presents issues of biomedical data integration, clinical interpretation and knowledge management. This paper outlines how the Health-e-Child project has approached the challenge of requirements specification for (bio-) medical data integration, from the level of cellular data, through disease to that of patient and population. The approach is illuminated through the requirements elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three diseases being studied in the EC-funded Health-e-Child project.Comment: 6 pages, 1 figure. Presented at the 11th International Database Engineering & Applications Symposium (Ideas2007). Banff, Canada September 200

    Exploiting conceptual spaces for ontology integration

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    The widespread use of ontologies raises the need to integrate distinct conceptualisations. Whereas the symbolic approach of established representation standards – based on first-order logic (FOL) and syllogistic reasoning – does not implicitly represent semantic similarities, ontology mapping addresses this problem by aiming at establishing formal relations between a set of knowledge entities which represent the same or a similar meaning in distinct ontologies. However, manually or semi-automatically identifying similarity relationships is costly. Hence, we argue, that representational facilities are required which enable to implicitly represent similarities. Whereas Conceptual Spaces (CS) address similarity computation through the representation of concepts as vector spaces, CS rovide neither an implicit representational mechanism nor a means to represent arbitrary relations between concepts or instances. In order to overcome these issues, we propose a hybrid knowledge representation approach which extends FOL-based ontologies with a conceptual grounding through a set of CS-based representations. Consequently, semantic similarity between instances – represented as members in CS – is indicated by means of distance metrics. Hence, automatic similarity detection across distinct ontologies is supported in order to facilitate ontology integration

    The use of the concept of event in enterprise ontologies and requirements engineering literature.

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    The concept of event is used in a lot of meanings. It can be the possible outcome of doing something (probability theory), it can be a business transaction (accounting), or just a plain happening. In software engineering, the concept of event is also used a lot. It is used to accomplish loose coupling between software components or to realise interaction between different services. There is however not a consensus on the meaning of `an event'. In enterprise ontologies, an event is defined as a happening at one point in time, or as an activity which takes time to complete. In requirement engineering, the same different uses can be found, together with an event as a request for something that needs to be done. These differences can also be found in implementation. All these distinct purposes of the word event make it difficult to integrate and use different requirement engineering techniques. Comparison or transformations between models drawn in different grammars is impossible because of the ambiguity of the concept of event. We define three meanings for an event that are used by enterprise ontologies and requirement engineering techniques: an achievement (happening at one point in time), an activity (happening over time) and a request (a demand for something that needs to be done). We also identify a missing link between real economic events, the events defined in the requirements model and the events used in implementation.Requirements modelling; Enterprise ontology; Process modelling; Dynamic; Event;

    Towards an Ontology for Data-driven Discovery of New Materials

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    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, re-use and integration of materials science data and experimentation

    Spatial groundings for meaningful symbols

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    The increasing availability of ontologies raises the need to establish relationships and make inferences across heterogeneous knowledge models. The approach proposed and supported by knowledge representation standards consists in establishing formal symbolic descriptions of a conceptualisation, which, it has been argued, lack grounding and are not expressive enough to allow to identify relations across separate ontologies. Ontology mapping approaches address this issue by exploiting structural or linguistic similarities between symbolic entities, which is costly, error-prone, and in most cases lack cognitive soundness. We argue that knowledge representation paradigms should have a better support for similarity and propose two distinct approaches to achieve it. We first present a representational approach which allows to ground symbolic ontologies by using Conceptual Spaces (CS), allowing for automated computation of similarities between instances across ontologies. An alternative approach is presented, which considers symbolic entities as contextual interpretations of processes in spacetime or Differences. By becoming a process of interpretation, symbols acquire the same status as other processes in the world and can be described (tagged) as well, which allows the bottom-up production of meaning
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