Harvesting Knowledge from Social Networks: Extracting Typed Relationships Among Entities

Abstract

Knowledge bases like DBpedia, Yago or Google’s Knowledge Graph contain huge amounts of ontological knowledge harvested from (semi-)structured, curated data sources, such as relational databases or XML and HTML documents. Yet, the Web is full of knowledge that is not curated and/or structured and, hence, not easily indexed, for example social data. Most work so far in this context has been dedicated to the extraction of entities, i.e., people, things or concepts. This paper describes our work toward the extraction of relationships among entities. The objective is reconstructing a typed graph of entities and relationships to represent the knowledge contained in social data, without the need for a-priori domain knowledge. The experiments with real datasets show promising performance across a variety of domains.