Rivindu Perera


RealTextlex: A Lexicalization Framework for Linked Open Data

Abstract
Linked Open Data (LOD) is growing rapidly as a source of structured knowledge used in a variety of text processing applications. However, the applications using the LOD need to be able to mediate between the front end user interfaces and LOD. This often requires a natural language interpretation of this structured, linked data. We demonstrate a middle-tier framework that can generate patterns which can be used to transform LOD triples back into natural text. The framework utilizes preprocessed free text to extract a wide range of relations which are then aligned with triples to identify possible lexicalization patterns. These lexicalization patterns can then be used to transform a given triple into natural language sentence.