Semantically-Enhanced Information Extraction

Information Extraction using Natural Language Processing (NLP) produces entities along with some of the relationships that may exist among them. To be semantically useful, however, such discrete extractions must be put into context through some form of intelligent analysis. This paper1,2 offers a two-part architecture that employs the statistical methods of traditional NLP to extract discrete information elements in a relatively domain-agnostic manner, which are then injected into an inference-enabled environment where they can be semantically analyzed. Within this semantic environment, extractions are woven into the contextual fabric of a user-provided, domain-centric ontology where users together with user-provided logic can analyze these extractions within a more contextually complete picture. Our demonstration system infers the possibility of a terrorist plot by extracting key events and relationships from a collection of news articles and intelligence reports

Distributed Conversion of RDF Data to the Relational Model

Ve formátu RDF je ukládán rostoucí objem hodnotných informací. Relační databáze však stále přinášejí výhody z hlediska výkonu a množství podporovaných nástrojů. Představujeme RDF2X, nástroj pro automatický distribuovaný převod RDF dat do relačního modelu. Poskytujeme srovnání souvisejících přístupů, analyzujeme měření převodu 8.4 miliard RDF trojic a ilustrujeme přínos našeho nástroje na dvou případových studiích.The Resource Description Framework (RDF) stores a growing volume of valuable information. However, relational databases still provide advantages in terms of performance, familiarity and the number of supported tools. We present RDF2X, a tool for automatic distributed conversion of RDF datasets to the relational model. We provide a comparison of related approaches, report on the conversion of 8.4 billion RDF triples and demonstrate the contribution of our tool on case studies from two different domains