Finding semantic associations in hierarchically structured groups of Web data

TLDR: This paper proposes an approach for finding semantic associations which would not emerge without considering the structure of the data groups, and is based on the introduction of a new metadata model, that is an extension of the direct, labelled graph allowing the possibility to have nodes with a hierarchical structure.

Abstract: Most of the activities usually performed by Web users are today effectively supported by using appropriate metadata that make the Web practically readable by software agents operating as users' assistants. While the original use of metadata mostly focused on improving queries on Web knowledge bases, as in the case of SPARQL-based applications on RDF data, other approaches have been proposed to exploit the semantic information contained in metadata for performing more sophisticated knowledge discovery tasks. Finding semantic associations between Web data seems a promising framework in this context, since it allows that novel, potentially interesting information can emerge by the Web's sea, deeply exploiting the semantic relationships represented by metadata. However, the approaches for finding semantic associations proposed in the past do not seem to consider how Web entities are logically collected into groups, that often have a complex hierarchical structure. In this paper, we focus on the importance of taking into account this additional information, and we propose an approach for finding semantic associations which would not emerge without considering the structure of the data groups. Our approach is based on the introduction of a new metadata model, that is an extension of the direct, labelled graph allowing the possibility to have nodes with a hierarchical structure. To evaluate our approach, we have implemented it on the top of an existing recommender system for Web users, experimentally analyzing the introduced advantages in terms of effectiveness of the recommendation activity.