Confidence-Aware Embedding for Knowledge Graph Entity Typing

Abstract

Knowledge graphs (KGs) entity typing aims to predict the potential types to an entity, that is, (entity, entity type = ?). Recently, several embedding models are proposed for KG entity types prediction according to the existing typing information of the (entity, entity type) tuples in KGs. However, most of them unreasonably assume that all existing entity typing instances in KGs are completely correct, which ignore the nonnegligible entity type noises and may lead to potential errors for the downstream tasks. To address this problem, we propose ConfE, a novel confidence-aware embedding approach for modeling the (entity, entity type) tuples, which takes tuple confidence into consideration for learning better embeddings. Specifically, we learn the embeddings of entities and entity types in separate entity space and entity type space since they are different objects in KGs. We utilize an asymmetric matrix to specify the interaction of their embeddings and incorporate the tuple confidence as well. To make the tuple confidence more universal, we consider only the internal structural information in existing KGs. We evaluate our model on two tasks, including entity type noise detection and entity type prediction. The extensive experimental results in two public benchmark datasets (i.e., FB15kET and YAGO43kET) demonstrate that our proposed model outperforms all baselines on all tasks, which verify the effectiveness of ConfE in learning better embeddings on noisy KGs. The source code and data of this work can be obtained from https://github.com/swufenlp/ConfE.

Knowledge Graph Error Detection with Contrastive Confidence Adaption

“…The embedding-based models, e.g., TransE (Bordes et al 2013), DistMult (Yang et al 2015), and ComplEx (Trouil- 2018), CAGED (Zhang et al 2022), and SCEF (Zhao and Liu 2019) adopt the TransE's score function as a component. The embedding-based models heavily rely on negative sampling, and their effectiveness is hampered by the challenge of accurately modeling the real noise distribution.…”

DSGEM: Dual scene graph enhancement module‐based visual question answering

“…Besides, by merging the relations with similar semantics into the unified commonsense relation set, these relations have a large semantic distance from each other and are stable for the task of visual explicit relational inference. Besides, commonsense KGs are also widely applied for cross‐modal structured knowledge retrieval on multi‐modal tasks, for example, SGG [40], Visual Commonsense Reasoning [41], Image Captioning [42], and Multimodal Retrieval [43].…”

Refining large knowledge bases using co-occurring information in associated KBs

“…Mahdavi et al [41] designed an error detection system (Raha) and updated a system (Baran) for error correction by transfer learning. Other studies correct entity type [5,16,42] in the task of cleaning KBs. The work of fixing bugs is carried out by checking whether the KB violates the constraints of the schema [6,43] automatically.…”

“…Also, some correction systems [30,41] are designed to refine KBs. Usually, some correction methods focus on solving specific problems [5,6,16,42,43]. Extending these studies, natural language processing methods are combined with knowledge correction algorithms [44,45].…”