Semantic cores for representing documents in IR

Baziz, Mustapha; Boughanem, Mohand; Aussenac-Gilles, Nathalie; Chrisment, Claude

doi:10.1145/1066677.1066911

Cited by 39 publications

(31 citation statements)

References 12 publications

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“…A first solution aimed to build ontology from corpora on which RI tasks will be carried [4], [5]. A second solution involves to reuse existing resources, in this case, ontology are usually chosen only from the knowledge domain that they address [6], [7].…”

Section: Ontology a Crucial Need In Irmentioning

confidence: 99%

How Ontology Can be Used to Improve Semantic Information Retrieval: The AnimSe Finder Tool

Bouramoul¹,

Kholladi²,

Doan³

2011

IJCA

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In this paper, we propose a new approach for locating and retrieving documents; the search process is guided by the 'AnimOnto' domain ontology that we have constructed for this purpose. This ontology is used at two different stages: First, for the semantic indexing of documents, in this stage the representative concepts of each document are selected by a projection of the ontology on the document by attaching their terms to the 'AnimOnto' concepts. Then, during the semantics queries reformulation; in this stage we exploit the semantic links between concepts to expand the initial query. To validate these proposals, we have implemented the 'AnimSe Finder' tool (Animal Semantic Finder) which materializes the different phases of the proposed approach. The obtained scores show that the semantic indexing and the queries reformulation have generated a gain of 13.06 in terms of recall and 16.13 in terms of precision, which significantly reduces the documentary noise and silence. General TermsSemantic Web, Information Retrieval.

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Section: Ontology a Crucial Need In Irmentioning

confidence: 99%

How Ontology Can be Used to Improve Semantic Information Retrieval: The AnimSe Finder Tool

Bouramoul¹,

Kholladi²,

Doan³

2011

IJCA

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“…However, the systems applying WordNet suffered from word sense disambiguation (WSD) because of polysemy 1 . This problem has been addressed in [14], [15], and [16]. Additionally, researchers report difficulties in applying linguistic-based ontologies to non-linguistic applications [18].…”

Section: Concept Knowledge Basesmentioning

confidence: 99%

Human-inspired Identification of High-level Concepts using OWA and Linguistic Quantifiers

Reformat

Yager

et al. 2011

INT J COMPUT COMMUN

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Intelligent agent based system can be used to identify high-level concepts matching sets of keywords provided by users. A new human-inspired approach to concept identification in documents is introduced here. The proposed method takes keywords and builds concept structures based on them. These concept structures are represented as hierarchies of concepts (HofC). The ontology is used to enrich HofCs with terms and other concepts (sub-concepts) based on concept definitions, as well as with related concepts. Additionally, the approach uses levels of importance of terms defining the concepts. The levels of importance of terms are continuously updated based on a flow of documents using an Adaptive Assignment of Term Importance (AATI) schema. The levels of activation of concepts identified in a document that match these in the HofC are estimated using ordered weighted averaging (OWA) operators with linguistic quantifiers. A simple case study presented in the paper is designed to illustrate the approach.

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“…In particular, an ontology enumerates domain concepts and relationships among the concepts (Guarino, 1995), and provides a sound semantic ground of machine-understandable description of digital content. Ontology is popular in annotating documents with metadata, improving the performance of information retrieval and reasoning, and making data interoperable between different applications (Baziz, Boughanem, Aussenac-Gilles, & Chrisment, 2005;Duo, Juan-Zi, & Bin, 2005;Fensel, 2002;Khan, McLeod, & Hovy, 2004;Schreiber, Dubbeldam, Wielemaker, & Wielinga, 2001). In addition, an ontologyenabled semantic description of behaviors and services allows for better coordination of software agents in a multiagent system (Hendler & McGuinness, 2001;Takeda, Iino, & Nishida, 1995;Takeda, Iwata, Takaai, Sawada, & Nishida, 1996).…”

Section: Ontologymentioning

confidence: 99%

Ontology‐supported polarity mining

Zhou

Chaovalit

2007

J. Am. Soc. Inf. Sci.

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Polarity mining provides an in‐depth analysis of semantic orientations of text information. Motivated by its success in the area of topic mining, we propose an ontology‐supported polarity mining (OSPM) approach. The approach aims to enhance polarity mining with ontology by providing detailed topic‐specific information. OSPM was evaluated in the movie review domain using both supervised and unsupervised techniques. Results revealed that OSPM outperformed the baseline method without ontology support. The findings of this study not only advance the state of polarity mining research but also shed light on future research directions.

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Semantic cores for representing documents in IR

Cited by 39 publications

References 12 publications

How Ontology Can be Used to Improve Semantic Information Retrieval: The AnimSe Finder Tool

How Ontology Can be Used to Improve Semantic Information Retrieval: The AnimSe Finder Tool

Human-inspired Identification of High-level Concepts using OWA and Linguistic Quantifiers

Ontology‐supported polarity mining

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