Information Extraction and Linking in a Retrieval Context

Abstract

We witness a growing interest and capabilities of automatic content recognition (often referred to as information extraction) in various media sources that identify entities (e.g. persons, locations and products) and their semantic attributes (e.g., opinions expressed towards persons or products, relations between entities).These extraction techniques are most advanced for text sources, but they are also researched for other media, for instance for recognizing persons and objects in images or video. The extracted information enriches and adds semantic meaning to document and queries (the latter e.g., in a relevance feedback setting). In addition, content recognition techniques trigger automated linking of information across documents and even across media. This situation poses a number of opportunities and challenges for retrieval and ranking models. For instance, instead of returning full documents, information extraction provides the means to return very focused results in the form of entities such as persons and locations. Another challenge is to integrate content recognition and content retrieval as much as possible, for instance by using the probabilistic output from the information extraction tools in the retrieval phase. These approaches are important steps towards semantic search, i.e., retrieval approaches that truly use the semantics of the data.