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Rank web documents based on multi-domain ontology

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Abstract

The importance of web document ranking algorithms for search engines keeps increasing along with the growth of web. Traditional ranking algorithms rank web documents according to hyperlinks in them. These algorithms are prone to problem of topic drifting, and it is difficult to ensure the accuracy of the sorting result. In recent years, researchers began to study the semantic understanding of web content. Methods such as ontology based ranking were proposed to sort web documents better. In this paper, the existing methods of ontology construction and web document ranking algorithm are comprehensively reviewed. Then a multi-domain ontologies construction method for massive unstructured text, singular value decomposition ontology construct (SVDOC) is proposed. Based on multi-domain ontology, a ranking algorithm that can exploit the semantic meaning of the text, multiply domain ontology PageRank (MDOPR) is also presented. We use SVDOC in the experiments to construct multi-domain ontologies in the field of shipping industry (ship, port, route and other related fields). The results show that SVDOC can effectively construct multi-domain ontologies, and MDOPR has higher accuracy and feasibility than other ranking methods.

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Notes

  1. https://lucene.apache.org/core/4_6_0/core/org/apache/lucene/search/similarities/TFIDFSimilarity.html.

  2. https://spark.apache.org/.

  3. http://cn.bing.com.

  4. http://www.baidu.com.

  5. http://info.shippingchina.com.

  6. https://protege.stanford.edu/.

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Acknowledgements

This work was supported by Shanghai Maritime University research fund project (20130469), and by Shanghai Science & Technology Innovation Plan Fund (14511107400), and by State Oceanic Administration China research fund project (201305026). Professor Jin Wang is the corresponding author.

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Authors and Affiliations

  1. College of Information Engineering, Shanghai Maritime University, Shanghai, China

    Jin Liu, Mingji Zhou & Li Lin

  2. Business Administration Research Institute, Sungshin W. University, Seoul, Korea

    Hye-jin Kim

  3. College of Information Engineering, Yangzhou University, Yangzhou, China

    Jin Wang

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  1. Jin Liu
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  2. Mingji Zhou
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  3. Li Lin
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  4. Hye-jin Kim
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  5. Jin Wang
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Correspondence to Jin Wang.

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Liu, J., Zhou, M., Lin, L. et al. Rank web documents based on multi-domain ontology. J Ambient Intell Human Comput 15, 1573–1582 (2024). https://doi.org/10.1007/s12652-017-0566-5

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