Vocabulary mining for information retrieval: rough sets and fuzzy sets

Srinivasan, Padmini; Ruiz, Miguel E.; Kraft, Donald H.; Chen, Jianhua

doi:10.1016/s0306-4573(00)00014-5

Cited by 89 publications

(31 citation statements)

References 10 publications

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“…The documents and queries, represented by vectors, were compared via their approximations in the approximation space. An interesting approach for computing rough similarity measures to compute document's overlaps is presented in [22]. Let S1and S2 represent two subsets, which are the collections of weighted words.…”

Section: Related Workmentioning

confidence: 99%

A Semantic Information Retrieval Approach Based on Rough Ontology

Hu¹,

Lu²,

Guan³

2014

TOCSJ

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With the emergence of the semantic web, ontology has attracted a great deal of attention in field of information retrieval. But the conceptual formalism supported by typical ontology is not sufficient for handling incomplete information that is confronted in the real world knowledge. To tackle this problem, a semantic information retrieval approach based on a rough ontology is proposed. Rough ontology in this paper is in the form of an ontology information system. Given a keyword based query, our approach infers the individuals and properties correlated to the query through a procedure of association searches in the rough ontology, and takes properties as equivalence relations to construct an approximation space of rough ontology. Afterward, an algorithm of computing similarity in rough ontology is presented, and approximation space is employed to compute similarity for ranking documents in semantic document indexing space. The proposed approach has been compared with two other information retrieval techniques, and the experiments conducted on CNKI collections, support the better efficacy which results from our approach.

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Section: Related Workmentioning

confidence: 99%

A Semantic Information Retrieval Approach Based on Rough Ontology

Hu¹,

Lu²,

Guan³

2014

TOCSJ

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“…The authors also state that the classification rate is 100% in their experiment. In the field of informat ion retrieval, Rough Sets has been employed for instance in [14]. It proposes an approach based on Rough Sets and Fuzzy Sets to address the vocabulary mining problem.…”

Section: Applications Of the Theory Of Rough Setsmentioning

confidence: 99%

A Rough Sets-based Agent Trust Management Framework

Abedinzadeh

Sadaoui²

2013

IJISA

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Abstract-In a virtual society, which consists of several autonomous agents, trust helps agents to deal with the openness of the system by identify ing the best agents capable of performing a specific task, or achieving a special goal. In this paper, we introduce ROSTAM, a new approach for agent trust management based on the theory of Rough Sets. ROSTAM is a generic trust management framework that can be applied to any types of mu lti agent systems. However, the features of the application do main must be provided to ROSTAM. These features form the trust attributes. By collecting the values for these attributes, ROSTAM is able to generate a set of trust rules by employing the theory of Rough Sets. ROSTAM then uses the trust rules to extract the set of the most trusted agents and forwards the user's request to those agents only. After getting the results, the user must rate the interaction with each trusted agent. The rating values are subsequently utilized for updating the trust rules. We applied ROSTAM to the domain o f cross -language Web search. The resulting Web search system reco mmends to the user the set of the most trusted pairs of translator and search engine in terms of the pairs that return the results with the highest precision of retrieval.

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“…Srinivasan et al [39] used a combination of rough sets and fuzzy sets to create a framework to mine the vocabulary. They also examined the problem of co-coordinating multiple views of the vocabulary.…”

Section: Vocabulary Base: Related Workmentioning

confidence: 99%

Assessing traceability of software engineering artifacts

et al. 2010

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The generation of traceability links or traceability matrices is vital to many software engineering activities. It is also person-power intensive, time-consuming, error-prone, and lacks tool support. The activities that require traceability information include, but are not limited to, risk analysis, impact analysis, criticality assessment, test coverage analysis, and verification and validation of software systems. Information Retrieval (IR) techniques have been shown to assist with the automated generation of traceability links by reducing the time it takes to generate the traceability mapping. Researchers have applied techniques such as Latent Semantic Indexing (LSI), vector space retrieval, and probabilistic IR and have enjoyed some success. This paper concentrates on examining issues not previously widely studied in the context of traceability: the importance of the vocabulary base used for tracing and the evaluation and assessment of traceability mappings and methods using secondary measures. We examine these areas and perform empirical studies to understand the importance of each to the traceability of software engineering artifacts.

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Vocabulary mining for information retrieval: rough sets and fuzzy sets

Cited by 89 publications

References 10 publications

A Semantic Information Retrieval Approach Based on Rough Ontology

A Semantic Information Retrieval Approach Based on Rough Ontology

A Rough Sets-based Agent Trust Management Framework

Assessing traceability of software engineering artifacts

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