OpenKE: An Open Toolkit for Knowledge Embedding

Han, Xu; Cao, Shulin; Lv, Xin; Lin, Yankai; Liu, Zhiyuan; Sun, Maosong; Li, Juanzi

doi:10.18653/v1/d18-2024

Cited by 279 publications

(161 citation statements)

References 12 publications

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“…Three bi-vector Trans models named TransE-SYM, TransH-SYM and TransD-SYM proposed by us. Experimental code implementation reference open source project OpenKE [15]. These models run on datasets completed symmetric relation and get good results.…”

Section: Results Of Experimentsmentioning

confidence: 99%

Knowledge Graph Embedding Bi-vector Models for Symmetric Relation

Yao

Zhao

2019

Lecture Notes in Electrical Engineering

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Knowledge graph embedding (KGE) models have been proposed to improve the performance of knowledge graph reasoning. However, there is a general phenomenon in most of KGEs, as the training progresses, the symmetric relations tend to zero vector, if the symmetric triples ratio is high enough in the dataset. This phenomenon causes subsequent tasks, e.g. link prediction etc., of symmetric relations to fail. The root cause of the problem is that KGEs do not utilize the semantic information of symmetric relations. We propose KGE bi-vector models, which represent the symmetric relations as vector pair, significantly increasing the processing capability of the symmetry relations. We generate the benchmark datasets based on FB15k and WN18 by completing the symmetric relation triples to verify models. The experiment results of our models clearly affirm the effectiveness and superiority of our models against baseline.2. The symmetric semantic information of relations is combined with KGE, which is a new research method of knowledge graph reasoning.3. We run the model on the extended benchmark datasets and verify the effectiveness and advantages of the models.

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Section: Results Of Experimentsmentioning

confidence: 99%

Knowledge Graph Embedding Bi-vector Models for Symmetric Relation

Yao

Zhao

2019

Lecture Notes in Electrical Engineering

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“…We implemented TransE [16], one of the most popular techniques of this kind, on top of Trident and compared the runtime of training vs. the one produced by OpenKE [35], a state-of-the-art library. Table 6 reports the runtime to train a model using as input a subset of YAGO which was used in other works [69].…”

Section: Sparqlmentioning

confidence: 99%

Adaptive Low-level Storage of Very Large Knowledge Graphs

Urbani

Jacobs

2020

Proceedings of the Web Conference 2020

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The increasing availability and usage of Knowledge Graphs (KGs) on the Web calls for scalable and general-purpose solutions to store this type of data structures. We propose Trident, a novel storage architecture for very large KGs on centralized systems. Trident uses several interlinked data structures to provide fast access to nodes and edges, with the physical storage changing depending on the topology of the graph to reduce the memory footprint. In contrast to single architectures designed for single tasks, our approach offers an interface with few low-level and general-purpose primitives that can be used to implement tasks like SPARQL query answering, reasoning, or graph analytics. Our experiments show that Trident can handle graphs with 10 11 edges using inexpensive hardware, delivering competitive performance on multiple workloads.

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“…We obtain the highest Hits@10 scores on the validation set when learning rate at 5e −5 , the number of filters at 400 on WN18; and learning rate at 1e −5 , the number of filters at 50 on FB15k; and the learning rate at 5e −6 , the number of filters at 400 on WN18RR; and the learning rate at 1e −5 , the number of filters at 200 on FB15k-237. For comparison methods, we use the codes released by [11], [7] and [22]. Table 2.…”

Section: Implementation Detailsmentioning

confidence: 99%

Improving Knowledge Graph Embedding Using Locally and Globally Attentive Relation Paths

Jia

Cheng

2020

Lecture Notes in Computer Science

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Knowledge graphs' incompleteness has motivated many researchers to propose methods to automatically infer missing facts in knowledge graphs. Knowledge graph embedding has been an active research area for knowledge graph completion, with great improvement from the early TransE to the current state-of-the-art ConvKB. ConvKB considers a knowledge graph as a set of triples, and employs a convolutional neural network to capture global relationships and transitional characteristics between entities and relations in the knowledge graph. However, it only utilizes the triple information, and ignores the rich information contained in relation paths. In fact, a path of one relation describes the relation from some aspect in a fine-grained way. Therefore, it is beneficial to take relation paths into consideration for knowledge graph embedding. In this paper, we present a novel convolutional neural network-based embedding model PConvKB, which improves knowledge graph embedding by incorporating relation paths locally and globally. Specifically, we introduce attention mechanism to measure the local importance of relation paths. Moreover, we propose a simple yet effective measure DIPF to compute the global importance of relation paths. Experimental results show that our model achieves substantial improvements against state-of-the-art methods.

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OpenKE: An Open Toolkit for Knowledge Embedding

Cited by 279 publications

References 12 publications

Knowledge Graph Embedding Bi-vector Models for Symmetric Relation

Knowledge Graph Embedding Bi-vector Models for Symmetric Relation

Adaptive Low-level Storage of Very Large Knowledge Graphs

Improving Knowledge Graph Embedding Using Locally and Globally Attentive Relation Paths

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