151 research outputs found

    Towards ontology interoperability through conceptual groundings

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    Abstract. The widespread use of ontologies raises the need to resolve heterogeneities between distinct conceptualisations in order to support interoperability. The aim of ontology mapping is, to establish formal relations between a set of knowledge entities which represent the same or a similar meaning in distinct ontologies. Whereas the symbolic approach of established SW representation standards – based on first-order logic and syllogistic reasoning – does not implicitly represent similarity relationships, the ontology mapping task strongly relies on identifying semantic similarities. However, while concept representations across distinct ontologies hardly equal another, manually or even semi-automatically identifying similarity relationships is costly. Conceptual Spaces (CS) enable the representation of concepts as vector spaces which implicitly carry similarity information. But CS provide 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 first-order logic 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 between instances across distinct ontologies is supported in order to facilitate ontology mapping

    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

    Enabling Future Smart Energy Systems

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    The on-going transition to more sustainable energy production methods means that we are moving away from a monolithic, centrally controlled model to one in which both production and consumption are progressively decentralised and localised. This in turn gives rise to complex interacting networks. ICT and mathematics will be instrumental in making these networks more efficient and resilient. This article highlights two research areas that we expect will play an important role in these developments

    MEMPERKAYA ONTOLOGI DARI BERBAGAI ONLINE SCHEMA DATA

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    Database relasional dianggap salah satu solusi penyimpanan yang paling populer untuk berbagai macam data dan telah diakui sebagai faktor kunci dalam pengelolaan data untuk berbagai aplikasi. Ontologi, di sisi lain, adalah salah satu konsep kunci dan media utama di bidang penelitian Semantik Web. Masalah menjembatani kesenjangan antara database relasional dan ontologi telah menarik minat masyarakat Semantik Web, bahkan dari tahun-tahun awal keberadaannya dan umumnya disebut sebagai masalah pemetaan database-ke-ontologi. Proses manual dalam memperkaya ontologi yang sudah ada menjadi hal yang harus dilakukan berulang kali dan menjadi pekerjaan yang tiada akhir. Usaha membuat proses memperkaya ontologi secara semi otomatis menjadi suatu kebutuhan. Adapun tujuan dari paper ini adalah untuk mengembangkan arsitektur pemetaan database ke ontologi dalam rangka memperkaya ontologi yang sudah ada. Model pendekatan ini dapat memperkaya ontologi yang ada baik dari level class, property maupun instance/individu yang bersumber dari informasi dijital dalam bentuk database terstruktur
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