Ontology Based Meaningful Search Using Semantic Web and Natural Language Processing Techniques

Palaniammal, K.; Vijayalakshmi, S.

doi:10.21917/ijsc.2013.0095

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…The literature revealed that current studies on traditionalbased search method are not mature enough: either they are not scalable to large knowledge bases or they cannot capture all the semantics in the query (13)(14)(15)(16) . To overcome the weaknesses of the current web system and to utilize the strengths query expansion a novel framework based on ontology and NLP is proposed for information retrieval (17)(18)(19)(20)(21)(22)(23) . In the proposed framework, domain specific knowledge is utilized for ontology construction.…”

Section: Introductionmentioning

confidence: 99%

Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

Palaniammal¹

2021

IJST

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Objectives: To refine search performance using semantic web with an improved algorithm to retrieve the information efficiently. Methods: In order to establish the SCBR model and improve the performance of Web search, this paper adopts the Natural Language Processing (NLP) technology and the Quality of Service (QoS) ranking method, and endeavors to develop a relevant reliable and efficient search engine. Findings: Mean average precision tests revealed for quickness and precious of search results, and achieves the values from 82.98% to 99.53%. The experimental results show that the NLP technique improves the performance of SCBR model, and achieves higher average precision and recall values. Novelty: This research focuses to develop a related reliable and an efficient search engine to retrieve the accurate results for the user's complex query. It even bears the human error in typing, and suggests the expected word to search for. It also aims to retrieving the same result for synonym words which prevent the appearance of irrelevant search results.

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Section: Introductionmentioning

confidence: 99%

Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

Palaniammal¹

2021

IJST

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Semantic annotation for news feeds

Hendi¹,

Al-Feel

Hassanein

2017

2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

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Advancing Computer-Aided Drug Discovery (ACADD): In-Silico Approach towards Nuclear Receptors by Big Data

Palaniammal¹,

Mani²,

Kumar

2021

JPRI

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The progression of drug discovery and development is time consuming and costly. Advancing Computer-aided drug discovery (ACADD) is an effective tool in reducing the time and cost of research and development. This study deals with the evaluation of the nuclear receptors for the in-silico biological activity using ligand betulinic acid and dexamethasone. Docking results showed that binding energy was -74.190 kcal/mol when compared with that of the standard (-51.551 kcal/mol). Interaction energy -44.16 & -25.14 kcal/mol) of the ligands also coincide with the binding energy. These ligands have shown the best ligand-receptor interaction based on their structural parameters.

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Ontology Based Meaningful Search Using Semantic Web and Natural Language Processing Techniques

Cited by 3 publications

References 11 publications

Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

Refining Search Performance through Semantic based CBR Model and QoS Ranking Methodology

Semantic annotation for news feeds

Advancing Computer-Aided Drug Discovery (ACADD): In-Silico Approach towards Nuclear Receptors by Big Data

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