Techniques for Textual Document Indexing and Retrieval via Knowledge Sources and Data Mining

Chu, Wesley W.; Liu, Victor Zhenyu; Mao, Wenlei

doi:10.1007/978-1-4613-0227-8_5

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…Chu et al [8] have applied query expansion using UMLS and the OSHUMED collection based on template specific queries in which the expansion terms are combined based on the relations in the UMLS and the document collection.…”

Section: Knowledge Source Dependentmentioning

confidence: 99%

Terminological cleansing for improved information retrieval based on ontological terms

Jimeno-Yepes

Berlanga

Rebholz‐Schuhmann

2009

Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval

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Ontologies are frequently used in information retrieval being their main applications the expansion of queries, semantic indexing of documents and the organization of search results. However, the optimization of an ontology to perform information retrieval tasks is still unclear. In this paper, we propose an ontology query model to analyze the usefulness of ontologies in effectively performing document searches. Moreover, we propose a series of heuristic techniques that optimize ontologies for information retrieval tasks. Preliminary results demonstrate that current domain ontologies provide enough information to support user requests and that ontologies might be improved with simple methods.

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Section: Knowledge Source Dependentmentioning

confidence: 99%

Terminological cleansing for improved information retrieval based on ontological terms

Jimeno-Yepes

Berlanga

Rebholz‐Schuhmann

2009

Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval

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“…Query expansion (QE) is a technique used to aid users to express their requirements. There are many works in QE techniques, such as the mechanisms of relevance feedback [5] and statistical term co-occurrence [6]. The drawback of relevance feedback and statistical term co-occurrence methods is the analysis of pervious results documents which may provide a relationship between extracted terms and the www.ijacsa.thesai.org original query.…”

Section: Related Workmentioning

confidence: 99%

Ranking Documents Based on the Semantic Relations Using Analytical Hierarchy Process

El-Dsouky¹,

Ali²,

Rashed³

2016

ijacsa

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Abstract-With the rapid growth of the World Wide Web comes the need for a fast and accurate way to reach the information required. Search engines play an important role in retrieving the required information for users. Ranking algorithms are an important step in search engines so that the user could retrieve the pages most relevant to his query.In this work, we present a method for utilizing genealogical information from ontology to find the suitable hierarchical concepts for query extension, and ranking web pages based on semantic relations of the hierarchical concepts related to query terms, taking into consideration the hierarchical relations of domain searched (sibling, synonyms and hyponyms) by different weighting based on AHP method. So, it provides an accurate solution for ranking documents when compared to the three common methods.

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“…The relevance scores obtained by the four hyponyms regarding D a and D b are shown in Table 4. Then, the normalised relevance scores are calculated as shown in Table 5 using (5).…”

Section: Simple Search Scenario With Sementioning

confidence: 99%

“…Then, the outputs of the evidence located at the attribute level of Fig. 3 are listed in Table 6, when normalised by (5).…”

Section: Simple Search Scenario With Sementioning

confidence: 99%

Improved document ranking in ontology‐based document search engine using evidential reasoning

2014

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This study presents a novel approach to document ranking in an ontology-based document search engine (ODSE) using evidential reasoning (ER). Firstly, a domain ontology model, used for query expansion, and a connection interface to an ODSE are developed. A multiple attribute decision making (MADM) tree model is proposed to organise expanded query terms. Then, an ER algorithm, based on the Dempster-Shafer theory, is used for evidence combination in the MADM tree model. The proposed approach is discussed in a generic frame for document ranking, which is evaluated using document queries in the domain of electrical substation fault diagnosis. The results show that the proposed approach provides a suitable solution to document ranking and the precision at the same recall levels for ODSE searches have been improved significantly with ER embedded, in comparison with a traditional keyword-matching search engine, an ODSE without ER and a non-randomness-based weighting model.

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Techniques for Textual Document Indexing and Retrieval via Knowledge Sources and Data Mining

Cited by 5 publications

References 8 publications

Terminological cleansing for improved information retrieval based on ontological terms

Terminological cleansing for improved information retrieval based on ontological terms

Ranking Documents Based on the Semantic Relations Using Analytical Hierarchy Process

Improved document ranking in ontology‐based document search engine using evidential reasoning

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