GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs
This addresses a specific bottleneck in knowledge graph reasoning for applications like question answering, though it is incremental as it builds on existing geometric embedding methods.
The paper tackled the challenge of modeling negation and union operators in knowledge graph embeddings for logical queries, proposing GammaE which uses Gamma distributions to reduce uncertainty and achieve state-of-the-art performance on three large datasets.
Embedding knowledge graphs (KGs) for multi-hop logical reasoning is a challenging problem due to massive and complicated structures in many KGs. Recently, many promising works projected entities and queries into a geometric space to efficiently find answers. However, it remains challenging to model the negation and union operator. The negation operator has no strict boundaries, which generates overlapped embeddings and leads to obtaining ambiguous answers. An additional limitation is that the union operator is non-closure, which undermines the model to handle a series of union operators. To address these problems, we propose a novel probabilistic embedding model, namely Gamma Embeddings (GammaE), for encoding entities and queries to answer different types of FOL queries on KGs. We utilize the linear property and strong boundary support of the Gamma distribution to capture more features of entities and queries, which dramatically reduces model uncertainty. Furthermore, GammaE implements the Gamma mixture method to design the closed union operator. The performance of GammaE is validated on three large logical query datasets. Experimental results show that GammaE significantly outperforms state-of-the-art models on public benchmarks.