Save Up to 99% of Your Time in Mapping Validation

Maltese, Vincenzo; Giunchiglia, Fausto; Autayeu, Aliaksandr

doi:10.1007/978-3-642-16949-6_28

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…This makes manual validation much easier, faster, and less error-prone. In [34] we give concrete suggestions on how to effectively conduct the validation process.…”

Section: Discussionmentioning

confidence: 99%

Computing minimal mappings between lightweight ontologies

2012

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Abstract. As a valid solution to the semantic heterogeneity problem, many matching solutions have been proposed. Given two lightweight ontologies, we compute the minimal mapping, namely the subset of all possible correspondences, that we call mapping elements, between them such that i) all the others can be computed from them in time linear in the size of the input ontologies, and ii) none of them can be dropped without losing property i). We provide a formal definition of minimal mappings and define a time efficient computation algorithm which minimizes the number of comparisons between the nodes of the two input ontologies. The experimental results show a substantial improvement both in the computation time and in the number of mapping elements which need to be handled, for instance for validation, navigation and search.

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“…This makes manual validation much easier, faster, and less error-prone. In [34] we give concrete suggestions on how to effectively conduct the validation process.…”

Section: Discussionmentioning

confidence: 99%

Computing minimal mappings between lightweight ontologies

2012

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“…As humans, we may understand that they were both built to categorize documents about places and, by tagging them, to eventually provide some opinions about those places. The identification of semantic correspondences between the nodes makes the two classifications interoperable [19], for instance, we may use the content of the node Rome to enrich the content of the node cities in Italy.…”

Section: Diversity-aware Semantic Matchingmentioning

confidence: 99%

“…Semantics is core in many knowledge management applications, such as natural language data and metadata understanding [20,22,23,24], natural language driven image generation [54], abstract reasoning [55,56], converting classifications into formal ontologies [7,27,28], automatic classification [25,26], ontology matching [17,18,19] and semantic search [29]. However, despite the progress made, one of the main barriers towards the success of these applications is the lack of background knowledge.…”

Section: Introductionmentioning

confidence: 99%

Domains and context: First steps towards managing diversity in knowledge

Giunchiglia

Maltese

Dutta

2012

Journal of Web Semantics

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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.

show abstract

Section: Introductionmentioning

confidence: 99%

Domains and Context: First Steps Towards Managing Diversity in Knowledge

2012

Self Cite

View full text Add to dashboard Cite

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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Save Up to 99% of Your Time in Mapping Validation

Cited by 9 publications

References 15 publications

Computing minimal mappings between lightweight ontologies

Computing minimal mappings between lightweight ontologies

Domains and context: First steps towards managing diversity in knowledge

Domains and Context: First Steps Towards Managing Diversity in Knowledge

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