From Web Directories to Ontologies: Natural Language Processing Challenges

Zaihrayeu, Ilya; Sun, Lei; Giunchiglia, Fausto; Ping, Wei; Ju, Qi; Chi, Mingmin; Huang, Xuanjing

doi:10.1007/978-3-540-76298-0_45

Cited by 29 publications

(27 citation statements)

References 16 publications

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“…Using NLP techniques tuned for short phrases, such as those in [20,24], their meaning is determined by constructing a corresponding formula (i.e. the concept at label).…”

Section: 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%

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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.

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“…Using NLP techniques tuned for short phrases, such as those in [20,24], their meaning is determined by constructing a corresponding formula (i.e. the concept at label).…”

Section: Semantic Matchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Domains and context: First steps towards managing diversity in knowledge

Giunchiglia

Maltese

Dutta

2012

Journal of Web Semantics

Self Cite

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show abstract

“…Following the approach described in [1] and exploiting dedicated NLP techniques tuned to short phrases (for instance, as described in [13]), classifications can be converted, exactly or with a certain degree of approximation, into their formal alter-ego, namely into lightweight ontologies. Lightweight ontologies are acyclic graph structures where each natural language node label is translated into a propositional Description Logic (DL) formula codifying the meaning of the node.…”

Section: Fig 1 Two Classificationsmentioning

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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“…Next, we perform word sense filtering, i.e., we discard word senses which are not relevant in the given context. In order to do this, we follow the approach presented in [22], which exploits POS tagging information and WordNet lexical database for disambiguation of words in short noun phrases. Differently from [22] we do not use the disambiguation technique which leaves only the most probable sense of the word, because of its low accuracy.…”

Section: From Words To Complex Concepts (W → C)mentioning

confidence: 99%