We are very pleased to announce that our group got a paper accepted for presentation at NAACL21.The North American Chapter of the Association for Computational Linguistics (NAACL) provides a regional focus for members of the Association for Computational Linguistics (ACL) in North America as well as in Central and South America, organizes annual conferences, promotes cooperation and information exchange among related scientific and professional societies, encourages and facilitates ACL membership by people and institutions in the Americas, and provides a source of information on regional activities for the ACL Executive Committee.
Here is the abstract and the link to the paper:
Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings
By
Chengjin Xu,
Yung-Yu Chen,
Mojtaba Nayyeri, and
Jens Lehmann.
Abstract
Representation learning approaches for knowledge graphs have been mostly designed for static data. However, many knowledge graphs involve evolving data, e.g., the fact (The President of the United States is Barack Obama) is valid only from 2009 to 2017. This introduces important challenges for knowledge representation learning since the knowledge graphs change over time. In this paper, we present a novel time-aware knowledge graph embedding approach, TeLM, which performs 4th-order tensor factorization of a Temporal knowledge graph using a Linear temporal regularizer and Multivector embeddings. Moreover, we investigate the effect of the temporal dataset’s time granularity on temporal knowledge graph completion. Experimental results demonstrate that our proposed models trained with the linear temporal regularizer achieve state-of-the-art performances on link prediction over four well-established temporal knowledge graph completion benchmarks.
