Enriching Structured Knowledge with Open Information

We propose an approach for semantifying web extracted facts. In particular, we map subject and object terms of these facts to instances; and relational phrases to object properties defined in a target knowledge base. By doing this we resolve the ambiguity inherent in the web extracted facts, while simultaneously enriching the target knowledge base with a significant number of new assertions. In this paper, we focus on the mapping of the relational phrases in the context of the overall workflow. Furthermore, in an open extraction setting identical semantic relationships can be represented by different surface forms, making it neces-sary to group these surface forms together. To solve this problem we propose the use of markov clustering. In this work we present a complete, ontology independent, general-ized workflow which we evaluate on facts extracted by Nell and Reverb. Our target knowledge base is DBpedia. Our evaluation shows promising results in terms of producing highly precise facts. Moreover, the results indicate that the clustering of relational phrases pays off in terms of an im-proved instance and property mapping