Embedding Learning with Triple Trustiness on Noisy Knowledge Graph

Yu, Zhiwu; Feng, Huali; Gallinari, Patrick

doi:10.3390/e21111083

Cited by 12 publications

(4 citation statements)

References 46 publications

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“…To tackle this issue, several studies [ 32 , 33 , 34 , 35 ] propose methods that aggregate information from neighborhood nodes for latent messages. Furthermore, some studies [ 23 , 37 ] explore the integration of external information, such as entities’ labels, to enhance KG embedding. However, obtaining label information for entities in MKGs is not always a straightforward process.…”

Section: Methodsmentioning

confidence: 99%

“…To address these challenges, NKRL [ 20 ] introduces the concept of negative confidence and proposes a negative sampling method for training. In addition, TransT [ 37 ] measures the confidence score via external entity type and description. These models generate confidence scores in representation learning to estimate the triplets, thereby enhancing the robustness of KG embedding with noise.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label

Yu,

Ye,

Ruan

2024

Bioengineering

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The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledge graphs via intrinsic labEL (EMKGEL). Considering the absence of hyper-view KG, we establish a hyper-view KG and a triplet-level KG for implicit label information and neighborhood information, respectively. Inspired by the success of graph attention networks (GATs), we introduce the hyper-view GAT to incorporate label messages and neighborhood information into representation learning. We leverage a confidence score that combines local and global trustworthiness to estimate the triplets. To validate the effectiveness of our approach, we conducted experiments on three publicly available MKGs, namely PharmKG-8k, DiseaseKG, and DiaKG. Compared with the baseline models, the Precision@K value improved by 0.7%, 6.1%, and 3.6%, respectively, on these datasets. Furthermore, our method empirically showed that it significantly outperformed the baseline on a general knowledge graph, Nell-995.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label

Yu,

Ye,

Ruan

2024

Bioengineering

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“…e noise issue can be roughly classified into two classes, that is, false relationships between entities (head entity, relationship, tail entity) and false entity type instances (entity, entity type). Most of the existing research concentrates on the deal with the noisy triple facts in KG [9,14,[14][15][16][17][18][19][20][21]. For example, Jiang et al [15] present a Markov logic-based system for cleaning an extracted knowledge base.…”

Section: Kg Noise Detectionmentioning

confidence: 99%

“…Xie et al [20] propose a confidence-aware knowledge representation learning framework that detects possible noises in KGs while learning knowledge representations with confidence simultaneously. Zhao et al [21] propose a trustiness-aware method for KG noise detection. Despite their success, which focuses on detecting triple fact noises, their goals are different from this paper.…”

Section: Kg Noise Detectionmentioning

confidence: 99%

Confidence-Aware Embedding for Knowledge Graph Entity Typing

Hou

et al. 2021

Complexity

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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.

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Cybersecurity knowledge graphs construction and quality assessment

Li,

Shi,

Pan

et al. 2023

Complex Intell. Syst.

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Cyber-attack activities are complex and ever-changing, posing severe challenges to cybersecurity personnel. Introducing knowledge graphs into the field of cybersecurity helps depict the intricate cybersecurity landscape and provides technical support for threat identification and situational awareness. However, during the process of constructing knowledge graphs, inevitable noise and conflicts may be introduced, leading to misleading inferences and decisions. This paper aims to address the issues of constructing and assessing the quality of cybersecurity knowledge graphs. We manually constructed a dataset of cybersecurity knowledge graphs (CS13K) and expanded the existing cybersecurity ontology, building a more versatile ontology applicable to existing data features. Building upon this, we utilized Neo4j to construct the cybersecurity knowledge graph. Furthermore, we propose an AttTucker model, based on Transformer, for assessing the quality of knowledge graphs. By employing numerous self-attention heads, this model captures latent information among entities and relations. While reducing the dimensionality of knowledge embeddings, the model achieves evaluation results comparable to high-dimensional embeddings. Finally, we incorporate the path-level information between entities in the knowledge graph into the evaluation of knowledge graph quality based on the AttTucker model. Experimental analysis on both generic datasets and cybersecurity datasets demonstrates that our model significantly improves F1 value and accuracy in knowledge graph quality assessment tasks, outperforming other models.

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Embedding Learning with Triple Trustiness on Noisy Knowledge Graph

Cited by 12 publications

References 46 publications

Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label

Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label

Confidence-Aware Embedding for Knowledge Graph Entity Typing

Cybersecurity knowledge graphs construction and quality assessment

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