Probabilistic enrichment of knowledge graph entities for relation detection in conversational understanding

Knowledge encoded in semantic graphs such as Freebase has been shown to benefit semantic parsing and interpretation of natural language user utterances. In this paper, we propose new methods to assign weights to semantic graphs that reflect common usage types of the entities and their relations. Such statistical information can improve the disambiguation of entities in natural language utterances. Weights for entity types can be derived from the populated knowledge in the semantic graph, based on the frequency of occurrence of each type. They can also be learned from the usage frequencies in real world natural language text, such as related Wikipedia documents or user queries posed to a search engine. We compare the proposed methods with the unweighted version of the semantic knowledge graph for the relation detection task and show that all weighting methods result in better performance in comparison to using the unweighted version.