Semantic query for Quran documents results

The query-based on the result is less relevant documents results. In this study, a query has been improved in order to retrieve more relevant documents across language boundaries, a mechanism for query translation with semantic which is applied on as semantic query (SQ). Therefore, this study is conducted with the purposes to investigate semantic approach against the queries and vice versa. Furthermore, it is also conducted to investigate the performance query based on total retrieve and relevant. The retrieval however, included the irrelevant documents because of the translation polysemy. Results from the experiments suggest that semantic approach is most important process in cross language information retrieval (CLIR). It also found that semantic approach contributes to better performance in retrieving more relevant and related Quran document results.

[1]  Ping He,et al.  Latent Attribute Space Tree Classifiers: Latent Attribute Space Tree Classifiers , 2010 .

[2]  Enrico Motta,et al.  AquaLog: An ontology-driven question answering system for organizational semantic intranets , 2007, J. Web Semant..

[3]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[4]  Peter Brusilovsky,et al.  Semantic annotation based exploratory search for information analysts , 2010, Inf. Process. Manag..

[5]  Hajer Baazaoui Zghal,et al.  Towards an on-Line Semantic Information Retrieval System based on Fuzzy Ontologies , 2008, J. Digit. Inf. Manag..

[6]  Peter Willett,et al.  The effectiveness of stemming for natural‐language access to Slovene textual data , 1992 .

[7]  Edward R. Tufte,et al.  The Visual Display of Quantitative Information , 1986 .

[8]  Akrivi Katifori,et al.  Ontology visualization methods—a survey , 2007, CSUR.

[9]  Hajer Baazaoui Zghal,et al.  A Model-Driven Approach of Ontological Components for On-line Semantic Web Information Retrieval , 2007, J. Web Eng..

[10]  Peter Willett,et al.  The Effectiveness of Stemming for Natural-Language Access to Slovene Textual Data , 1992, J. Am. Soc. Inf. Sci..

[11]  Edward Rolf Tufte,et al.  The visual display of quantitative information , 1985 .

[12]  Bustanul Bustanul Arifin Metode Penafsiran Muhsin Khan dan Taqi al-Diin al-Hilali dalam Buku Interpretation of The Meaning of The Noble Quran , 2010 .

[13]  Gabriella Pasi,et al.  Flexible information retrieval: some research trends , 2002 .

[14]  Gerard Salton,et al.  Experiments in Automatic Thesaurus Construction for Information Retrieval , 1971, IFIP Congress.

[15]  Enrico Motta,et al.  PowerAqua: Fishing the Semantic Web , 2006, ESWC.

[16]  Jesualdo Tomás Fernández-Breis,et al.  Semantic Web-based system for managing the educative curriculum , 2010 .

[17]  Roziati Zainuddin,et al.  Visualization Systems Supporting the Reading of Arabic Document for non Arabic Speakers , 2009 .

[18]  Zainab Abu Bakar,et al.  Evaluating the Effectiveness of Thesaurus and Stemming Methods in Retrieving Malay Translated Al-Quran Documents , 2003, ICADL.

[19]  Li Zhi A Fast Algorithm for Synthesis of Quantum Reversible Logic Circuits , 2009 .

[20]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[21]  Bhavani M. Thuraisingham,et al.  Geospatial Resource Description Framework (GRDF) and security constructs , 2008, 2008 IEEE 24th International Conference on Data Engineering Workshop.

[22]  Nacéra Bennacer,et al.  Metadata- and Ontology- Based Semantic Web Mining , 2006 .

[23]  Colin Ware,et al.  Visualizing graphs in three dimensions , 2008, TAP.

[24]  Henrik I. Christensen,et al.  Computational visual attention systems and their cognitive foundations: A survey , 2010, TAP.

[25]  Margaret-Anne D. Storey,et al.  Using a degree of interest model to facilitate ontology navigation , 2009, 2009 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC).

[26]  Xu Xiao Latent Attribute Space Tree Classifiers , 2009 .