@inproceedings{a0ffda98790e4f5a9745161366ae2085,
title = "An improved shark search algorithm based on domain ontology",
abstract = "In recent years, the prevailing topic crawler algorithms are concentrated on the contents of topical words. These existing approaches neglect the sematic relationship among textual concepts, which lead to low correlation between crawled webpages. To address the issue, this paper presents a deep analysis of Shark Search algorithm, and makes an optimization in terms of incorporating the characteristics associated with semi-structured webpages. Furthermore, we enhance the performance of vector space model utilized in Shark Search algorithm by virtue of domain ontology, and propose a standardized method based on the vector space of ontology model to improve the evaluation metric of TF-IDF. The experimental results demonstrate the effectiveness of our algorithm that outperforms the state-of-the-art significantly in precision and recall.",
keywords = "Domain ontology, Shark Search algorithm, Topic crawler, Vector space model",
author = "Li, {Zhi Qiang} and Yuan Tan and Guo, {Hong Chen} and Chong Feng",
note = "Publisher Copyright: {\textcopyright} (2014) Trans Tech Publications, Switzerland.; 3rd International Conference on Advanced Engineering Materials and Architecture Science, ICAEMAS 2014 ; Conference date: 26-07-2014 Through 27-07-2014",
year = "2014",
doi = "10.4028/www.scientific.net/AMM.651-653.2252",
language = "English",
series = "Applied Mechanics and Materials",
publisher = "Trans Tech Publications Ltd.",
pages = "2252--2257",
editor = "H.W. Liu and G. Wang and G.W. Zhang",
booktitle = "Material Science, Civil Engineering and Architecture Science, Mechanical Engineering and Manufacturing Technology II",
address = "Switzerland",
}