Partha Pratim Talukdar

TL;DR: The Never-Ending Language Learner (NELL) as discussed by the authors is a case study of a machine learning system that learns to read the Web 24hrs/day since January 2010, and so far has acquired a knowledge base with 120mn diverse, confidence-weighted beliefs (e.g., servedWith(tea,biscuits), while learning thousands of interrelated functions that continually improve its reading competence over time.

TL;DR: The Never-Ending Language Learner (NELL) as discussed by the authors is a machine learning system that learns to read the web 24 hours/day since January 2010, and so far has acquired a knowledge base with over 80 million confidence-weighted beliefs (e.g., servedWith(tea, biscuits), while continuously improving its reading competence over time.

TL;DR: CompGCN as discussed by the authors proposes a novel Graph Convolutional framework which jointly embeds both nodes and relations in a relational graph, which leverages a variety of entity-relation composition operations from knowledge graph embedding techniques and scales with the number of relations.

TL;DR: EmbedKGZA is particularly effective in performing multi-hop KGQA over sparse KGs, and relaxes the requirement of answer selection from a pre-specified neighborhood, a sub-optimal constraint enforced by previous multi- Hop KG QA methods.

TL;DR: HyTE is a temporally aware KG embedding method which explicitly incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane and not only performs KG inference using temporal guidance, but also predicts temporal scopes for relational facts with missing time annotations.