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Jialong Han

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AAAI Conference 2020 Conference Paper

CASE: Context-Aware Semantic Expansion

Jialong Han
Aixin Sun
Haisong Zhang
Chenliang Li
Shuming Shi

In this paper, we deﬁne and study a new task called Context- Aware Semantic Expansion (CASE). Given a seed term in a sentential context, we aim to suggest other terms that well ﬁt the context as the seed. CASE has many interesting applications such as query suggestion, computer-assisted writing, and word sense disambiguation, to name a few. Previous explorations, if any, only involve some similar tasks, and all require human annotations for evaluation. In this study, we demonstrate that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner. On a dataset of 1. 8 million sentences thus derived, we propose a network architecture that encodes the context and seed term separately before suggesting alternative terms. The context encoder in this architecture can be easily extended by incorporating seed-aware attention. Our experiments demonstrate that competitive results are achieved with appropriate choices of context encoder and attention scoring function.

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AAAI Conference 2019 Conference Paper

Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding

Peifeng Wang
Jialong Han
Chenliang Li
Rong Pan

Knowledge graph embedding aims at modeling entities and relations with low-dimensional vectors. Most previous methods require that all entities should be seen during training, which is unpractical for real-world knowledge graphs with new entities emerging on a daily basis. Recent efforts on this issue suggest training a neighborhood aggregator in conjunction with the conventional entity and relation embeddings, which may help embed new entities inductively via their existing neighbors. However, their neighborhood aggregators neglect the unordered and unequal natures of an entity’s neighbors. To this end, we summarize the desired properties that may lead to effective neighborhood aggregators. We also introduce a novel aggregator, namely, Logic Attention Network (LAN), which addresses the properties by aggregating neighbors with both rules- and network-based attention weights. By comparing with conventional aggregators on two knowledge graph completion tasks, we experimentally validate LAN’s superiority in terms of the desired properties.

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