FuhSen: A federated hybrid search engine for building a knowledge graph on-demand

A vast amount of information about various types of entities is spread across the Web, e.g., people or organizations on the Social Web, product offers on the Deep Web or on the Dark Web. These data sources can comprise heterogeneous data and are equipped with different search capabilities e.g., Search API. End users such as investigators from law enforcement institutions searching for traces and connections of organized crime have to deal with these interoperability problems not only during search time but also while merging data collected from different sources. We devise FuhSen, a keyword-based federated engine that exploits the search capabilities of heterogeneous sources during query processing and generates knowledge graphs on-demand applying an RDF-Molecule integration approach in response to keyword-based queries. The resulting knowledge graph describes the semantics of entities collected from the integrated sources, as well as relationships among these entities. Furthermore, FuhSen utilizes ontologies to describe the available sources in terms of content and search capabilities and exploits this knowledge to select the sources relevant for answering a keyword-based query. We conducted a user evaluation where FuhSen is compared to traditional search engines. FuhSen semantic search capabilities allow users to complete search tasks that could not be accomplished with traditional Web search engines during the evaluation study.