In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020, p. 2184-2190
Location prediction is a key problem in human mobility modeling, which predicts a user's next location based on historical user mobility traces. As a sequential prediction problem by nature, it has been recently studied using Recurrent Neural Networks (RNNs). Due to the sparsity of user mobility traces, existing techniques strive to improve RNNs by considering spatiotemporal contexts. The...
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In: The Web Conference 2021, Ljubljana, Slovenia, April 12-23, 2021, 2021, p. 1-12
Knowledge Graph (KG) completion has been widely studied to tackle the incompleteness issue (i.e., missing facts) in modern KGs. A fact in a KG is represented as a triplet (ℎ, 𝑟, 𝑡 ) linking two entities ℎ and 𝑡 via a relation 𝑟 . Existing work mostly consider link prediction to solve this problem, i.e., given two elements of a triplet predicting the missing one, such as (ℎ,...
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In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, p. 1162–1172
Embeddings have become a key paradigm to learn graph representations and facilitate downstream graph analysis tasks. Existing graph embedding techniques either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization, or factorize a high-order proximity/adjacency matrix of the graph via expensive matrix factorization. However, these techniques...
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In: Encyclopedia of Big Data Technologies, 2018, p. 1–7
With the growing popularity of multi-relational data on the Web, knowledge graphs (KGs) have become a key data source in various application domains, such as Web search, question answering, and natural language understanding. In a typical KG such as Freebase (Bollacker et al. 2008) or Google’s Knowledge Graph (Google 2014), entities are connected via relations. For example, Bern is capital...
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