Abstract:
Many advances in the computer science field, such as semantic search, recommendation systems, question-answering, natural language processing, are drawn-out using the help of large scale knowledge bases (e.g., YAGO, NELL, DBPedia). However, many of these knowledge bases are static representations of knowledge and do not model time on its own dimension or do it only for a small portion of the graph. In contrast, projects such as GDELT and ICEWS have constructed large temporally annotated knowledge graphs of events collected from news hubs. In this paper, we study the problem of reasoning over such graphs. In particular, transpose two well-known techniques from knowledge base reasoning to utilize the temporal dimension: rule mining and graph embeddings. We mine temporally constrained first-order inference rules using the state-of-the-art relational knowledge base model. We interpret the learned rules as event sequence rules. We also use simple embedding methods to jointly learn a universal representation of entities and time-specific representations of the knowledge graph. We present the first set of temporal rules mined over event knowledge graphs and preliminary results on using the learned embeddings in the temporal link prediction task.
Authors:
Ali Sadeghian, Miguel Rodriguez, Daisy Zhe Wang, Anthony Colas
Bibtex:
@inproceedings{Sadeghian2018TemporalRO,
title={Temporal Reasoning Over Event Knowledge Graphs},
author={Ali Sadeghian and Miguel Paris Rodriguez and Daisy Zhe Wang},
year={2018}
}
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