An ontology-based approach for semantics ranking of the web search engines results

Bouramoul, Abdelkrim; Kholladi, Mohamed-Khireddine; Doan, Bich-Liên

doi:10.1109/icmcs.2012.6320318

Cited by 9 publications

(9 citation statements)

References 7 publications

(7 reference statements)

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“…Lexxe extracts the most concise form of most credible answer. Sometime it provides the answer directly from database but it usually answers the question from web links [6]. Cognition is domain specific search engine so its relevancy is higher in comparison of other search engines [19] and it provides about 90% more relevant results according to users' need.…”

Section: B Rankingmentioning

confidence: 99%

“…After being merged with Microsoft the changes are visible in live search and related search and in content of Wikipedia [16]. Lexxe answers the questions and gives better results on standard keyword searching that are much relevant to leading search engine experience, and Lexxe achieves this by implementing NLP Technology [6]. Cognition is linguistic search engine that supports ontology, morphology and synonym, tapping one of the world's largest computational dictionaries.…”

Section: A Large Scale Searchmentioning

confidence: 99%

“…Hakia works on OntoSem to retrieve relevant results efficiently [4]. Sensebot works on text mining [16] and Powerset, Lexxe and cognition work on strong NLP to retrieve results on-the-fly and human desired result [6], [7].…”

Section: Entity Searchmentioning

confidence: 99%

“…Semantic based approach for the evaluation of information retrieval systems has been proposed. The purpose is to increase the selectivity of search tools and to improve how these tools are evaluated [6]. Another paper proposes a novel approach to construct the snippets based on a semantic evaluation of the segments in the page.…”

mentioning

confidence: 99%

“…Powerset refines query by giving advance options such as sort by articles and sort by sentence to get desired results. It provides refined results by presenting them in forms of clusters, answers and web page snippet [6]. Cognition advance syntactic parser provides result by making use of syntactic relation between words [18].…”

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confidence: 99%

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Comparative Analysis of Semantic Search Engines Based on Requirement Space Pyramid

Qureshi¹

2013

IJFCC

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Abstract-Semantic Web promises to add metadata to web content to make it understandable to computers. Search is the most widely used activity on web. Semantic search engines have already changed the way we search the data on web. Uren. V, Yuanguilei Uren et al., proposed requirement space pyramid arguing that iterative and exploratory search modes are important to the usability of search engines. It identified the types of semantic queries the users need to make, the issues concerning the search development and the problems intrinsic to semantic search in particular. We have extensively examined the semantic search engines and have done broad survey to analyse the semantic search engines. Comparative analysis of the semantic search engines have been done on the basis of factors cited in the pyramid. The research provides deep understanding of five main semantic search engines based on comparative analysis that may help for future work for semantic web in general and for semantic search engines in particular.Index Terms-Semantic web, semantic search engine, requirement space pyramid. I. INTRODUCTIONSemantic Web [1], the future of web, promises to provide content that will be understandable to computer. Search is the most important and popular applications of the web. Semantic search [2] is one of the most significant applications of semantic web. Recently a number of semantic search engines have been introduced. Semantic Search engine is a tool that produces precise results to user queries by retrieving data semantically [3]. The purpose of this paper is to compare semantic search engines along with their salient characteristic approaches. This paper focuses mainly on five semantic search engines including Hakia, Sensebot, Powerset, Lexxe and Cognition. There are many benchmarks and standards used in every field and computer science is not an exception. Google and other search engines have revolutionized the world with their services but with the idea of semantic search, the rise of semantic search engines has changed the requirements and issues related with search engines. A pyramid scheme has been used as model for discussion to present the requirements, issues and challenges for semantic search engines. The remaining paper has been formatted as follows that related work have discussed in section II. Comparative evaluation of the semantic search engines on the basis of the pyramid has been described in Manuscript received November 8, 2012; revised January 28, 2013 section III. The semantic search engines along with their GUI's have been discussed in section IV while finally the concluding remarks include the issues and challenges of semantic search engines. II. RELATED WORKSSemantic search engine is new field and realtively less work has been done in this domain. Hakia has been compared with google, yahoo and msn in [4] and that of Hakia with dogpile. A relation-based page rank algorithm has been proposed to be used as semantic web search engine. In this work, relevance is measured as probability of finding con...

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Section: B Rankingmentioning

confidence: 99%

Section: A Large Scale Searchmentioning

confidence: 99%

Section: Entity Searchmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Comparative Analysis of Semantic Search Engines Based on Requirement Space Pyramid

Qureshi¹

2013

IJFCC

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GeTFIRST: ontology-based keyword search towards semantic disambiguation

Nguyen

Tran

et al. 2015

International Journal of Web Information Systems

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Purpose – This paper aims to improve the semantic-disambiguation capability of an information-retrieval system by taking advantages of a well-crafted classification tree. The unstructured nature and sheer volume of information accessible over networks have made it drastically difficult for users to seek relevant information. Many information-retrieval methods have been developed to address this problem, and keyword-based approach is amongst the most common approach. Such an approach is often inadequate to cope with the conceptualization associated with user needs and contents. This brings about the problem of semantic ambiguation that refers to the disagreement in meaning of terms between involving parties of a communication due to polysemy, leading to increased complexity and lesser accuracy in information integration, migration, retrieval and other related activities. Design/methodology/approach – A novel ontology-based search approach, named GeTFIRST (short for Graph-embedded Tree Fostering Information Retrieval SysTem), is proposed to disambiguate keywords semantically. The contribution is twofold. First, a search strategy is proposed to prune irrelevant concepts for accuracy improvement using our Graph-embedded Tree (GeT)-based ontology. Second, a path-based ranking algorithm is proposed to incorporate and reward the content specificity. Findings – An empirical evaluation was performed on United States Patent And Trademark Office (USPTO) patent datasets to compare our approach with full-text patent search approaches. The results showed that GeTFIRST handled the ambiguous keywords with higher keyword-disambiguation accuracy than traditional search approaches. Originality/value – The search approach of this paper copes with the semantic ambiguation by using our proposed GeT-based ontology and a path-based ranking algorithm.

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