A Topic Modeling Guided Approach for Semantic Knowledge Discovery in e-Commerce

Abstract

The task of mining large unstructured text archives, extracting useful patterns and then organizing them into a knowledgebase has attained a great attention due to its vast array of immediate applications in business. Businesses thus demand new and efficient algorithms for leveraging potentially useful patterns from heterogeneous data sources that produce huge volumes of unstructured data. Due to the ability to bring out hidden themes from large text repositories, topic modeling algorithms attained significant attention in the recent past. This paper proposes an efficient and scalable method which is guided by topic modeling for extracting concepts and relationships from e-commerce product descriptions and organizing them into knowledgebase. Semantic graphs can be generated from such a knowledgebase on which meaning aware product discovery experience can be built for potential buyers. Extensive experiments using proposed unsupervised algorithms with e-commerce product descriptions collected from open web shows that our proposed method outperforms some of the existing methods of leveraging concepts and relationships so that efficient knowledgebase construction is possible.

Keywords

• e-commerce
• Graphs
• Latent Dirichlet Allocation
• Semantic Web
• Text Mining
• Web Mining