Dynamic Associative Relationships on the Linked Open Data Web

In this work we approach relationships on the Linked Open Data Web as key facilitators of information exploration. Linked Open Data (LOD) principles contribute to a shift in paradigm for information representation and access, enhancing the ability of users and computers to connect, browse and query data on the Web through standard languages and protocols. We present a brief discussion on the current relationship types on the Web, and observe the need for the extraction of a particular type. We focus on trending socially contextual relationships since they highlight the dynamics of the social interaction between users creating, linking, and consuming information on the Web. Our extraction method is based on the identification of co-occurring entity mentions in microblog posts. Real time entity and relationship extraction can be useful tools for the on demand extension of the Web of Data. We demonstrate the usefulness of this approach in the context of two applications: database exploration and semantic browsing for exploratory search.