Explainable Fact Checking by Combining Automated Rule Discovery with Probabilistic Answer Set Programming
|Abstract||The goal of fact checking is to determine if a given claim holds. A promising ap- proach for this task is to exploit reference information in the form of knowledge graphs (KGs), a structured and formal representation of knowledge with semantic descriptions of entities and relations. KGs are successfully used in multiple appli- cations, but the information stored in a KG is inevitably incomplete. In order to address the incompleteness problem, this thesis proposes a new method built on top of recent results in logical rule discovery in KGs called RuDik and a probabilistic extension of answer set programs called LPMLN.
This thesis presents the integration of RuDik which discovers logical rules over a given KG and LPMLN to do probabilistic in... (more)
|Contributor||Pradhan, Anish (Author) / Lee, Joohyung (Advisor) / Baral, Chitta (Committee member) / Papotti, Paolo (Committee member) / Arizona State University (Publisher)|
|Subject||Computer science / Fact Checking / Knowledge Graph / LPMLN / Probabilistic Reasoning / RUDIK / Rule Learning|
|Note||Masters Thesis Computer Science 2018|
|Collaborating Institutions||Graduate College / ASU Library|
|Additional Formats||MODS / OAI Dublin Core / RIS|