The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing
arxiv(2024)
摘要
The Knowledge Graph Entity Typing (KGET) task aims to predict missing type
annotations for entities in knowledge graphs. Recent works only utilize the
structural knowledge in the local neighborhood of entities,
disregarding semantic knowledge in the textual
representations of entities, relations, and types that are also crucial for
type inference. Additionally, we observe that the interaction between semantic
and structural knowledge can be utilized to address the false-negative problem.
In this paper, we propose a novel Semantic and
Structure-aware KG Entity
Typing (SSET) framework, which is composed of three
modules. First, the Semantic Knowledge Encoding module encodes factual
knowledge in the KG with a Masked Entity Typing task. Then, the
Structural Knowledge Aggregation module aggregates knowledge from the
multi-hop neighborhood of entities to infer missing types. Finally, the
Unsupervised Type Re-ranking module utilizes the inference results
from the two models above to generate type predictions that are robust to
false-negative samples. Extensive experiments show that SSET significantly
outperforms existing state-of-the-art methods.
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