Explaining Facts over Knowledge Graphs

Considering this.   Always thinking how we can best translate to more understandable forms.  Both for human understanding, machine use, the establishment of context, and relationship to goals ...

ExFaKT: a framework for explaining facts over knowledge graphs and text

Last week we took a look at Graph Neural Networks for learning with structured representations. Another kind of graph of interest for learning and inference is the knowledge graph.

Knowledge Graphs (KGs) are large collections of factual triples of the form \langle subject\ predicate\ object \rangle (SPO) about people, companies, places etc.

Today’s paper choice focuses on the topical area of fact-checking : how do we know whether a candidate fact, which might for example be harvested from a news article or social media post, is likely to be true? For the first generation of knowledge graphs, fact checking was performed manually by human reviewers, but this clearly doesn’t scale to the volume of information published daily. Automated fact checking methods typically produce a numerical score (probability the fact is true), but these scores are hard to understand and justify without a corresponding explanation.

To better support KG curators in deciding the correctness of candidate facts, we propose a novel framework for finding semantically related evidence in Web sources and the underlying KG, and for computing human—comprehensible explanations for facts. We refer to our framework as ExFaKT (Explaining Facts over KGs and Text resources).

There could be multiple ways of producing evidence for a given fact. Intuitively we prefer a short concise explanation to a long convoluted one. This translates into explanations that use as few atoms and rules as possible. Furthermore, we prefer evidence from trusted sources to evidence over less trusted sources. In this setting, the sources available to us are the existing knowledge graph and external text resources. The assumption is that we have some kind of quality control over the addition of facts to the knowledge graph, and so we prefer explanations making heavy use of knowledge graph facts to those than rely mostly on external texts. ... "