Applying Semantic Role Labeling and Spreading Activation Techniques for Semantic Information Retrieval

Abstract

Semantically enhanced information retrieval (IR) is aimed at improving classical IR methods and goes way beyond plain Boolean keyword matching with the main goal of better serving implicit and ambiguous information needs. As a de-facto pre-requisite to semantic IR, different information extraction (IE) techniques are used to mine unstructured text for underlying knowledge.  In this paper we present a method that combines both IE and IR to enable semantic search in natural language texts. First, we apply semantic role labeling (SRL) to automatically extract event-oriented information found in natural language texts to an RDF knowledge graph leveraging semantic web technology. Second, we investigate how a custom flavored graph traversal spreading activation algorithm can be employed to interpret user’s information needs on top of the prior-extracted knowledge base. Finally, we present an assessment on the applicability of our method for semantically enhanced IR. An experimental evaluation on partial WikiQA dataset shows the strengths of our approach and also unveils common pitfalls that we use as guidelines to draw further work directions in the open-domain semantic search field.