Knowledge Graphs – Mining, Cleaning and Maintenance

Extraction of knowledge from information sources ranging from unstructured and semi-structured, to structured has gained significant interest both in academia and in the industry. This is fueled by applications such as question answering and computational fact checking. Knowledge graphs (KG) have lately emerged as a de facto standard for knowledge representation, whereby knowledge is expressed as a collection of “facts", represented in the form of (subject, predicate, object) triples where subject and object are entities and predicate is a relation between those entities. This collection can be conveniently stored, queried, and maintained as a graph, with the entities modeled as vertices and relations as links or edges. In this project, Dr. Lakshmanan and his team will mine a large KG from information sources, with an emphasis on publicly available documents—including structured sources such as tables. They will also develop techniques for cleaning the KG and for maintaining it against updates. Finally, they will exploit the resulting KG in applications of question answering and computational fact checking, both of which will leverage the pattern search capabilities of a knowledge graph.

Trainees: Michael Simpson (PDF), Sarah Habashi (MSc candidate)

This project is sponsored by the DSI-Huawei Research Program