Relevance weighting of multi-term queries for Vector Space Model

Wang, Louis S.

doi:10.1109/cidm.2009.4938677

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Many papers talk about the importance of information retrieval algorithm in retrieving the material and ranking them according to the most related fields [28]. One of the algorithm is vector space model [32] which computes a continuous degree of similarity between queries and documents, and also can make partial comparing.…”

Section: Resultsmentioning

confidence: 99%

“…The Vector Space Model is one of the most common information retrieval, the cosine of the angle or the Euclidean distance between the query vector and each document vector is commonly used to measure similarity for query matching. In [32] paper presents a modified vector space model for measuring similarity between the query and the document. This paper presents a modified vector space model for measuring similarity between the query and the document when responding to a multi-term query .…”

Section: Information Retrievalmentioning

confidence: 99%

See 1 more Smart Citation

Filtering Features and Recommender Model for Messaging System of MOODLE

Barhoom¹,

Atallah²

2015

جامعة فلسطين للأبحاث و الدراسات

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First, I thank and I'm grateful for Allah for taking care of me and protecting me all the time. I would Thanks everyone who helped me in reaching this stage My Father and My Mother thank you for everything you have done to me my entire life My supervisor Dr. Tawfiq S. Barhoom for his encouragement and support, without his effort I couldn't finish my thesis, My Brothers (Hossam, Mohanned) & my sisters (Rana, Haneen, Randa) for their love I would like to express my appreciation to the academic staff of information technology program at the Islamic University-Gaza.

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Section: Resultsmentioning

confidence: 99%

Section: Information Retrievalmentioning

confidence: 99%

Filtering Features and Recommender Model for Messaging System of MOODLE

Barhoom¹,

Atallah²

2015

جامعة فلسطين للأبحاث و الدراسات

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“…A set of relevance scores may be obtained with ‘Condition assessment’ as

(}{, {normalRS}_{ca, 1}, \dots, {normalRS}_{ca, w}, \dots, {normalRS}_{ca, W})

where RS ca , w is the relevance score between ‘Condition assessment’ and a relevant document D w extracted from D rele . The value of RS ca, w is assigned to a closed interval [0, 1].…”

Section: Evidential Reasoning Based On Ds Theorymentioning

confidence: 99%

“…In the past few years, a variety of information retrieval (IR) models have been developed for document ranking in document search engines, for example, a probabilistic model, an inference network model and a vector space model (VSM) [1, 2] and so on, out of which VSM is the dominant one. In [3], it summarises that the main function of a search engine is to discover the information in relation to a query input.…”

Section: Introductionmentioning

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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“…The model is not without its limitations, for instance it assumes all terms are independently represented and related to each other only if the words are matched in the query and the document [13]. Many enhancements to VSM have been developed in the past including the study by Tai et al [13] based on RF, multi-term VSM [14] based on adjacency terms relationship, and multi-term VSM based on adjacency keyword-order [15]. The current study extends the work by Lim et al [15] by including RF and CBR.…”

Section: Information Retrieval Modelsmentioning

confidence: 99%

An Architecture to Enhance Post Retrieval Document Relevancy Using Integrated Techniques

Balakrishnan¹

2013

IJMLC

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Abstract-This paper is a proposal based on an on-going work in which we attempt to take advantage of information retrieval (IR) and case-based reasoning (CBR) techniques combined with the aim to improve the document relevancy of a search result. The proposed architecture contains two main phases: first is the IR phase whereby relevance feedback (RF) is implemented on search results produced based on adjacency keyword algorithm. Second is the CBR phase which further improves the results based on the output from phase one. This paper presents an explanation on the proposed RF-CBR model. It is believed that the integration of these two popular techniques would result in an improved document relevancy.

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Relevance weighting of multi-term queries for Vector Space Model

Cited by 6 publications

References 12 publications

Filtering Features and Recommender Model for Messaging System of MOODLE

Filtering Features and Recommender Model for Messaging System of MOODLE

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

An Architecture to Enhance Post Retrieval Document Relevancy Using Integrated Techniques

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