Representation Learning of Knowledge Graphs With Entity Attributes

Zhang, Zhongwei; Cao, Lei; Chen, Xiliang; Wei, Tang; Xu, Zongben; Meng, Yangyang

doi:10.1109/access.2020.2963990

Cited by 16 publications

(6 citation statements)

References 31 publications

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“…The problem is that current methods for Knowledge Graph Embedding rely on the graph's topology, treating attribute triples as relation triples, and essential information about entities and relations has not been fully employed [32] [33] [34] failing to utilize the graph's ontology to limit the spurious growth of edges leading to false, misleading, and fabricated knowledge.…”

Section: A Problem Statementmentioning

confidence: 99%

Restricting the Spurious Growth of Knowledge Graphs by Using Ontology Graphs

Tatchukova,

2024

IEEE Access

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Knowledge Graphs have demonstrated a real advantage in knowledge representation, leveraging graphs NoSQL structures and schema-less technology, which offers superior comprehension, knowledge representation, interpretation, and reasoning. The problem is that current methods for Knowledge Graph Embedding rely on the graph's topology, and essential information about entities and relations has not been fully employed, failing to utilize the graph's ontology to limit the spurious growth of edges, leading to inaccurate, misleading, and fabricated knowledge. This research aims to establish a method to restrict the spurious growth of host KGs by imposing an upper bound on edge embedding using the claim's and the host's ontology graph. Through this research, a Claim-Ontology Signature artifact is designed to facilitate openenvironment KG completion. This artifact establishes the upper bound for the type of edges predicted by the link prediction algorithm, thus preventing the spurious growth of edges within the Knowledge Graph. Further, the artifact is evaluated in the context of three use cases: Host-Guided embedding, Claim-Guided embedding, and Topic-Guided embedding, using a quantitative framework for design science evaluation. The main finding is that the spurious growth of edges can be limited by imposing an upper bound on the possible edge embedding using the claim's graph and the host ontology graph. A secondary finding is that the artifact could serve as an instrument to manage the ontology-topology tradeoff in Knowledge Graphs.

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Section: A Problem Statementmentioning

confidence: 99%

Restricting the Spurious Growth of Knowledge Graphs by Using Ontology Graphs

Tatchukova,

2024

IEEE Access

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“…We first use the knowledge graph to represent these relations and then predict the temporal attributes of the knowledge graph. In (Zhang et al, 2020), the authors regard entities, attributes, and attribute values as attribute triples and then use a deep convolution neural network to learn the representation of entities. Similarly, in (Tay et al, 2017), attributes are represented by a neural network and then predicted.…”

Section: Knowledge Graph Temporal Attribute Predictionmentioning

confidence: 99%

Land Subsidence Prediction Through Modeling of Temporal Attribute Prediction of Knowledge Graph

Lei

Song

Fan

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Land subsidence is a geological disaster. It will lead to the decline of land elevation, resulting in the potential safety hazards of urban facilities. Thus, the prediction of land subsidence displacement is significant. Among the existing prediction methods, the methods based on the time-series prediction model only analyze the settlement series data without considering the settlement mechanism, so they are easy to apply. However, they less consider the influence of other factors on land subsidence. Besides, they independently input displacement time-series data from different monitoring points without considering their relationship. To solve these problems, we take the monitoring point as the entity and take the DEM, soil type, building height, and land subsidence displacement sequence at the corresponding position of the monitoring point as the attributes to construct the knowledge graph. And then, we propose a framework Graph-TAP for modeling temporal attribute prediction of the knowledge graph’s entity. This framework learns the representation of events with the specific entity at first. Then it captures the temporal dependency between historical events using GRU. Finally, it predicts the entity’s displacement attribute. We randomly selected 61 subsidence monitoring points in Shenzhen, China. We used the land subsidence displacement InSAR time-series data (12-day time interval) and other attribute data from June 22, 2015, to April 5, 2016, for model training, validation, and testing. The experimental results show that our method is better than the time-series prediction based on the LSTM model and the DARNET (a knowledge graph temporal attribute prediction framework).

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“…KR-EAR [3] distinguishes existing knowledge graph relationships into attributes and relationships; it can learn representations of entities, attributes, and relationships to complete the knowledge completion task by separating relationship triplets and attribute triplets for modeling. Zhang et al [4] utilized triplet information and entity attributes simultaneously for knowledge representation learning, encoding attribute information through convolutional methods, and fusing triplet information and attribute information to entity representations. Lin et al [5] proposed a joint attribute preserving vector model for cross-language entity alignment, which embeds the vectors obtained from the two knowledge representations into a unified vector space, and further refines them by using the attribute correlation in knowledge embedding.…”

Section: Introductionmentioning

confidence: 99%

KTAT: A Complex Embedding Model of Knowledge Graph Integrating Type Information and Attention Mechanism

2023

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Knowledge graph embedding learning aims to represent the entities and relationships of real-world knowledge as low-dimensional dense vectors. Existing knowledge representation learning methods mostly aggregate only the internal information of triplets and graph structure information. Recent research has proved that multi-source information of entities is conducive to more accurate knowledge embedding tasks. In this paper, we propose a model based on an attention mechanism and integrating the type information of entities, named KTAT. This model is based on the graph attention mechanism, to distribute corresponding attention mechanisms according to different weights between nodes. We introduce a type-specific hyperplane, which enables entities to have different embedding representations according to their type in the current triplet. Simultaneously, we also used textual description information of entities to improve the performance of the model. We conducted link prediction experiments on the FB15k and FB15k-237 datasets. The experimental results show that our model outperforms previous advanced methods compared to some baseline models, and demonstrate that combining type information can effectively improve the performance of link prediction.

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Representation Learning of Knowledge Graphs With Entity Attributes

Cited by 16 publications

References 31 publications

Restricting the Spurious Growth of Knowledge Graphs by Using Ontology Graphs

Restricting the Spurious Growth of Knowledge Graphs by Using Ontology Graphs

Land Subsidence Prediction Through Modeling of Temporal Attribute Prediction of Knowledge Graph

KTAT: A Complex Embedding Model of Knowledge Graph Integrating Type Information and Attention Mechanism

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