Toward veracity assessment in RDF knowledge bases: An exploratory analysis
Abstract
Among different characteristics of knowledge bases, data quality is one of the most relevant to maximize the benefits of the provided information. Knowledge base quality assessment poses a number of big data challenges such as high volume, variety, velocity, and veracity. In this article, we focus on answering questions related to the assessment of the veracity of facts through Deep Fact Validation (DeFacto), a triple validation framework designed to assess facts in RDF knowledge bases. Despite current developments in the research area, the underlying framework faces many challenges. This article pinpoints and discusses these issues and conducts a thorough analysis of its pipeline, aiming at reducing the error propagation through its components. Furthermore, we discuss recent developments related to this fact validation as well as describing advantages and drawbacks of state-of-the-art models. As a result of this exploratory analysis, we give insights and directions toward a better architecture to tackle the complex task of fact-checking in knowledge bases.
