Toward Veracity Assessment in RDF Knowledge Bases

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.

Knowledge Graphs

“…Assessing the level of semantic inaccuracies is challenging. While one option is to apply manual verification, an automatic option may be to check the stated relation against several sources [141,300]. Another option is to rather validate the quality of individual processes used to generate the knowledge graph, based on measures such as precision, possibly with the help of human experts or gold standards [324].…”

TISCO: Temporal scoping of facts

“…Other approaches on fact checking are described in more detail in [12]. The authors present a systematic review of the components of DeFacto.…”

“…Step 6 The generated provenance output can also be saved directly as RDF and abides by the PROV Ontology 12 . The source code of the DeFacto algorithms and DeFacto's user interface are open-source 13 .…”

Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web

“…For instance, websites spreading manipulative political content or hoaxes can be persuasive. To tackle this problem, different fact-checking tools and frameworks have been proposed (Zubiaga et al, 2017), mainly divided into two categories: fact-checking over natural language claims (Thorne and Vlachos, 2018) and fact-checking over knowledge bases, i.e., triplebased approaches (Esteves et al, 2018). Overall, fact-checking algorithms aim at determining the veracity of claims, which is considered a very challenging task due to the nature of underlying steps, from natural language understanding (e.g.…”