From Open Information Extraction to Semantic Web: A Context Rule-Based Strategy

Abstract

The Web represents a valuable data source of information that is presented mainly as unstructured text. The extraction of structured and valuable information from sources such as the Web is an important challenge for the Semantic Web and Information Extraction areas, where elements representing real-world objects (aka named entities) and their relations need to be extracted from text and formally represented through RDF triples. Thus, extracting such information from the Web is manually unfeasible due to its large scale and heterogeneity of domains. In this sense, Open Information Extraction (OIE) is an independent domain task based on patterns to extract any kind of relation between named entities. Hence, one step further is to transform such relations into RDF triples. This paper proposes a method to represent relations obtained by an OIE approach into RDF triples. The method is based on the extraction of named entities, their relation, and contextual information from an input sentence and a set of defined rules that lead to map the extracted elements with resources from a Knowledge Base of the Semantic Web. The evaluation demonstrates promising results regarding the extraction and representation of information.