Exploiting corpus‐related ontologies for conceptualizing document corpora

Zheng, Haitao; Borchert, Charles; Kim, Hong-Gee

doi:10.1002/asi.21145

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…However, most of these methods do not directly extract key concepts and their semantic relationships from document corpora, which could help better to illuminate the conceptual structures within given information. To resolve this problem Zheng 23 , et al proposed "Clonto" approach which identifies the key concepts, and automatically generates ontologies based on these concepts for the purpose of conceptualisation. LIS professionals have always shown interest in integrating various vocabulary control tools.…”

Section: Uses Of Ontologymentioning

confidence: 99%

Construction and Applications of Ontology: Recent Trends

Deshpande¹,

Kumbhar²

2011

DJLIT

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The purpose of ontology is to achieve inter-operability by providing a common terminology and understanding of a given domain of interest, which in turn allows for the assignment of clear meaning to learning object. Ontologies are used in artificial intelligence, biomedical informatics, library science, chemistry and many other subjects. The purpose of this paper is to make the library professionals alert about the literature published on ontology and applications of ontology.

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Section: Uses Of Ontologymentioning

confidence: 99%

Construction and Applications of Ontology: Recent Trends

Deshpande¹,

Kumbhar²

2011

DJLIT

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“…In (Zheng et al, 2009a), concept induction is performed using Latent Semantic Analysis (LSA) techniques, based on single terms and common phrases extracted from the documents. Relations between concepts are constructed using WordNet, extracting hypernyms for each detected concept.…”

Section: Aggregation Of Related Features: Metaterm Creationmentioning

confidence: 99%

Unsupervised Organisation of Scientific Documents

Lourenço¹,

Medina²,

Fred³

et al. 2011

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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Unsupervised organisation of documents, and in particular research papers, into meaningful groups is a difficult problem. Using the typical vector-space-model representation (Bag-of-words paradigm), difficulties arise due to its intrinsic high dimensionality, high redundancy of features, and the lack of semantic information. In this work we propose a document representation relying on a statistical feature reduction step, and an enrichment phase based on the introduction of higher abstraction terms, designated as metaterms, derived from text, using as prior knowledge papers topics and keywords. The proposed representation, combined with a clustering ensemble approach, leads to a novel document organization strategy. We evaluate the proposed approach taking as application domain conference papers, topic information being extracted from conference topics or areas. Performance evaluation on data sets from NIPS and INSTICC conferences show that the proposed approach leads to interesting and encouraging results.

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Exploiting corpus‐related ontologies for conceptualizing document corpora

Cited by 2 publications

References 25 publications

Construction and Applications of Ontology: Recent Trends

Construction and Applications of Ontology: Recent Trends

Unsupervised Organisation of Scientific Documents

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