Search (5 results, page 1 of 1)

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Abstract

In representing the shared view of all the people involved, building a knowledge organization system (KOS) from scratch is extremely costly, and it is therefore fundamental to reuse existing resources. This can be done by progressively extending the KOS with knowledge coming from similar KOSs and by promoting interoperability among them. The linked data initiative is indeed encouraging people to share and integrate their datasets into a giant network of interconnected resources. This enables different applications to interoperate and share their data. The integration should take into account the purpose of the datasets, however, and make explicit the semantics. In fact, the difference in the purpose is reflected in the difference in the semantics. With this paper we (a) highlight the potential problems that may arise by not taking into account purpose and semantics; (b) make clear how the difference in the purpose is reflected in totally different semantics and (c) provide an algorithm to translate from one semantics into another as a preliminary step towards the integration of ontologies designed for different purposes. This will allow reusing the ontologies even in contexts different from those in which they were designed.

Source

Classification and ontology: formal approaches and access to knowledge: proceedings of the International UDC Seminar, 19-20 September 2011, The Hague, The Netherlands. Eds.: A. Slavic u. E. Civallero

Type

a

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Abstract

Despite the progress made, one of the main barriers towards the use of semantics is the lack of background knowledge. Dealing with this problem has turned out to be a very difficult task because on the one hand the background knowledge should be very large and virtually unbound and, on the other hand, it should be context sensitive and able to capture the diversity of the world, for instance in terms of language and knowledge. Our proposed solution consists in addressing the problem in three steps: (1) create an extensible diversity-aware knowledge base providing a continuously growing quantity of properly organized knowledge; (2) given the problem, build at run-time the proper context within which perform the reasoning; (3) solve the problem. Our work is based on two key ideas. The first is that of using domains, i.e. a general semantic-aware methodology and technique for structuring the background knowledge. The second is that of building the context of reasoning by a suitable combination of domains. Our goal in this paper is to introduce the overall approach, show how it can be applied to an important use case, i.e. the matching of classifications, and describe our first steps towards the construction of a large scale diversity-aware knowledge base.

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Abstract

In representing the shared view of all the people involved, building a Knowledge Organization System (KOS) from scratch is extremely costly, and it is therefore fundamental to reuse existing resources. This can be done by progressively extending the KOS with knowledge coming from similar KOS and by promoting interoperability among them. The linked data initiative is indeed fostering people to share and integrate their datasets into a giant network of interconnected resources. This enables different applications to interoperate and share their data. However, the integration should take into account the purpose of the datasets and make explicit the semantics. In fact, the difference in the purpose is reflected in the difference in the semantics. With this paper we (a) highlight the potential problems that may arise by not taking into account purpose and semantics, (b) make clear how the difference in the purpose is reflected in totally different semantics and (c) provide an algorithm to translate from one semantic into another as a preliminary step towards the integration of ontologies designed for different purposes. This will allow reusing the ontologies even in contexts different from those in which they were designed.

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Abstract

So far, within the library and information science (LIS) community, knowledge organization (KO) has developed its own very successful solutions to document search, allowing for the classification, indexing and search of millions of books. However, current KO solutions are limited in expressivity as they only support queries by document properties, e.g., by title, author and subject. In parallel, within the artificial intelligence and semantic web communities, knowledge representation (KR) has developed very powerful end expressive techniques, which via the use of ontologies support queries by any entity property (e.g., the properties of the entities described in a document). However, KR has not scaled yet to the level of KO, mainly because of the lack of a precise and scalable entity specification methodology. In this paper we present DERA, a new methodology inspired by the faceted approach, as introduced in KO, that retains all the advantages of KR and compensates for the limitations of KO. DERA guarantees at the same time quality, extensibility, scalability and effectiveness in search.

Type

a

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Type

a