BookDOI
The Semantic Web: Research and Applications
TLDR
DODDLE-R, a support environment for user-centered ontology development, consists of two main parts: pre-processing part and quality improvement part, which generates a prototype ontology semi-automatically and supports the refinement of it interactively.Abstract:
In order to realize the on-the-fly ontology construction for the Semantic Web, this paper proposes DODDLE-R, a support environment for user-centered ontology development. It consists of two main parts: pre-processing part and quality improvement part. Pre-processing part generates a prototype ontology semi-automatically, and quality improvement part supports the refinement of it interactively. As we believe that careful construction of ontologies from preliminary phase is more efficient than attempting generate ontologies full-automatically (it may cause too many modification by hand), quality improvement part plays significant role in DODDLE-R. Through interactive support for improving the quality of prototype ontology, OWL-Lite level ontology, which consists of taxonomic relationships (class sub class relationship) and non-taxonomic relationships (defined as property), is constructed effi-read more
Citations
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A survey of schema-based matching approaches
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TL;DR: This paper presents a new classification of schema-based matching techniques that builds on the top of state of the art in both schema and ontology matching and distinguishes between approximate and exact techniques at schema-level; and syntactic, semantic, and external techniques at element- and structure-level.
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YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia
TL;DR: YAGO2 as mentioned in this paper is an extension of the YAGO knowledge base, in which entities, facts, and events are anchored in both time and space, and it contains 447 million facts about 9.8 million entities.
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Recommender system application developments
TL;DR: This paper reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories, and summarizes the related recommendation techniques used in each category.
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LogMap: logic-based and scalable ontology matching
TL;DR: This paper presents LogMap--a highly scalable ontology matching system with 'built-in' reasoning and diagnosis capabilities, and is the only matching system that can deal with semantically rich ontologies containing tens (and even hundreds of thousands of classes).
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A string metric for ontology alignment
TL;DR: A new string metric for the comparison of names which performs better on the process of ontology alignment as well as to many other field matching problems is presented.
References
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Nicola Guarino,Chris Welty +1 more
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Journal ArticleDOI
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TL;DR: Differences between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution, but there are also important differences between database schemas and ontologies.
Book ChapterDOI
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