A State-of-the-Art Survey on Semantic Web Mining

The increasing volume of data available on the Web makes information retrieval a tedious and difficult task. The vision of the Semantic Web introduces the next generation of the Web by establishing a layer of machine-understandable data, e.g., for software agents, sophisticated search engines and Web services. The success of the Semantic Web crucially depends on the easy creation, integration and use of semantic data. This chapter is a state-of-the-art review of techniques which could make the Web more “semantic”. Beyond this state-of-the-art, we describe open research areas and we present major current research programs in this domain.

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Metadata- and Ontology-Based Semantic Web Mining

Web Semantics & Ontology ◽

10.4018/978-1-59140-905-2.ch009 ◽

2006 ◽

pp. 259-296 ◽

Cited By ~ 5

Author(s):

Marie Aude Aufaure ◽

Bénédicte Le Grand ◽

Michel Soto ◽

Nacera Bennacer

Keyword(s):

Information Retrieval ◽

Web Services ◽

Semantic Web ◽

Web Mining ◽

State Of The Art ◽

Next Generation ◽

Research Areas ◽

Semantic Data ◽

Open Research ◽

The Web

The increasing volume of data available on the Web makes information retrieval a tedious and difficult task. The vision of the Semantic Web introduces the next generation of the Web by establishing a layer of machine-understandable data, e.g., for software agents, sophisticated search engines and Web services. The success of the Semantic Web crucially depends on the easy creation, integration and use of semantic data. This chapter is a state-of-the-art review of techniques which could make the Web more “semantic”. Beyond this state-of-the-art, we describe open research areas and we present major current research programs in this domain.

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Semantic Web Mining. State of the Art and Future Directions

SSRN Electronic Journal ◽

10.2139/ssrn.3199332 ◽

2006 ◽

Cited By ~ 3

Author(s):

Gerd Stumme ◽

Andreas Hotho ◽

Bettina Berendt

Keyword(s):

Semantic Web ◽

Web Mining ◽

State Of The Art ◽

Future Directions

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Semantic Web mining for Content-Based Online Shopping Recommender Systems

International Journal of Intelligent Information Technologies ◽

10.4018/ijiit.2019100103 ◽

2019 ◽

Vol 15 (4) ◽

pp. 41-56 ◽

Cited By ~ 1

Author(s):

Ibukun Tolulope Afolabi ◽

Opeyemi Samuel Makinde ◽

Olufunke Oyejoke Oladipupo

Keyword(s):

Semantic Web ◽

Recommender Systems ◽

Web Mining ◽

Online Shopping ◽

Semantic Analysis ◽

Second Phase ◽

Web Content ◽

Textual Data ◽

Content Mining ◽

Bayes Algorithm

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

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