Semantic Search for Novel Information

Talk by Michael Färber

Location: APB 3027
Start: 19. January 2017 at 11:30 am
End: 19. January 2017 at 12:00 pm
Research group: Computational Logic
Event series: KBS Seminar
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Users more and more face the challenge to screen the continuously increasing number of (Web) documents and to assess the contained information with respect to their relevance and novelty. For instance, technology scouts need to discover and monitor new technologies, while investors and stock brokers would like to be informed about recent acquisitions. The approaches used so far for detecting novel information in text documents (semi-)automatically are often very inefficient. This is due to the fact that most approaches only consider the relevance, but not the novelty of text documents. The few existing novel information detection approaches do not use any semantically-structured representation of the already given and of the extracted information.

In this talk, new approaches for detecting and extracting novel, relevant information from unstructured text documents are presented which exploit the explicit modeling of the semantics of the given and extracted information. Using semantics has the benefit of resolving ambiguities in the language and to specify the exact information need regarding relevance and novelty. The explicit modeling is performed by using Semantic Web technologies such as the Resource Description Framework (RDF). In the presented work we assume that all knowledge which is known to the system is available in the form of an RDF knowledge graph.

Firstly, we consider existing large knowledge graphs for the task of semantic novel information detection. Secondly, we present approaches for detecting emerging entities and novel RDF statements. Ultimately, the developed approaches can be used for knowledge graph population. This describes the task of enriching existing knowledge graphs with additional entities or triples.