Conceptual Search Based on Semantic Relatedness
Traditional search engines based on syntactic search are unable to solve key issues like synonymy and polysemy. Solving these issues leads to the invention of the semantic web. The semantic search engines indeed overcome these issues. Nowadays the most important part of the data remains unstructured...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2014-08-01.
|
| Subjects: | |
| Online Access: | Get fulltext |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 02343 am a22002893u 4500 | ||
|---|---|---|---|
| 001 | ijeecs3762_2094 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Boubacar, Abdoulahi |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Niu, Zhendong |e author |
| 245 | 0 | 0 | |a Conceptual Search Based on Semantic Relatedness |
| 260 | |b Institute of Advanced Engineering and Science, |c 2014-08-01. | ||
| 520 | |a Traditional search engines based on syntactic search are unable to solve key issues like synonymy and polysemy. Solving these issues leads to the invention of the semantic web. The semantic search engines indeed overcome these issues. Nowadays the most important part of the data remains unstructured documents. It is consequently very time consuming to annotate such big data. Concept based retrieval systems intend to manage directly unstructured documents. Semantic relationships are their main feature to extend syntactic search. In most of the methods implemented so far, concepts are used for both indexing and searching. Words remain the smallest unit to process semantic relatedness. The differences persist in the way that concepts are represented, mapped to each other, and managed for the sake of indexing and/or searching. Our approach is based on Wikipedia concepts. Concepts are represented as an undirected graph. Their semantic relatedness are computed with a distance derived from a semantic similarity measure. The same distance is used to calculate both semantic relatedness and query matching. DOI: http://dx.doi.org/10.11591/telkomnika.v12i8.5143 | ||
| 540 | |a Copyright (c) 2014 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Information retrieval , Data mining. | ||
| 690 | |a Concept analysis, Information retrieval, Semantic relatedness | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 12, No 8: August 2014; 6380-6385 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v12.i8 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3762/2094 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3762/2094 |z Get fulltext |