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SimRE: Simple contrastive learning with soft logical rule for knowledge graph embedding

INFORMATION SCIENCES 2024年 661卷

作者： Zhang, Dong Rong, Zhe Xue, Chengyuan Li, Guanyu Dalian Maritime Univ Informat Sci & Technol Coll 1 Linghai Rd Dalian 116000 Liaoning Peoples R China

knowledge graphs serve as a pivotal framework for the structured representation of information regarding entities and relations. However, in the real world, these knowledge graphs are often incomplete and harboring missing facts. knowledge graph completion (KGC) has emerged as a central research focus, entailing the automated prediction of these missing facts and garnering substantial scholarly attention in recent years. Text -based knowledge graph embedding methods have demonstrated considerable potential for tackling the challenges associated with KGC by employing pre -trained language models. However, their limitation lies in the lack of logical features, which constrains the efficacy of capturing intricate patterns within knowledge graphs. This paper proposed SimRE, a straightforward contrastive learning framework augmented with soft logic rules. SimRE introduces a self -supervised framework that leverages the input rule bodies to predict the corresponding rule heads through a contrastive objective. We introduced two rule sampling techniques to enhance the efficiency and accuracy of the model: in -batch rule negatives and pre -batch rule negatives. SimRE employs a simple method for integrating logical features with the text -based model. The experimental results on benchmark datasets demonstrate that the proposed approach outperforms state-of-the-art methods.

关键词： knowledge graph completion knowledge graph embedding Contrastive learning Logical rule

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Enhanced knowledge graph embedding by Jointly Learning Soft Rules and Facts

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ALGORITHMS 2019年第12期12卷 265页

作者： Zhang, Jindou Li, Jing Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China

Combining first order logic rules with a knowledge graph (KG) embedding model has recently gained increasing attention, as rules introduce rich background information. Among such studies, models equipped with soft rules, which are extracted with certain confidences, achieve state-of-the-art performance. However, the existing methods either cannot support the transitivity and composition rules or take soft rules as regularization terms to constrain derived facts, which is incapable of encoding the logical background knowledge about facts contained in soft rules. In addition, previous works performed one time logical inference over rules to generate valid groundings for modeling rules, ignoring forward chaining inference, which can further generate more valid groundings to better model rules. To these ends, this paper proposes Soft Logical rules enhanced embedding (SoLE), a novel KG embedding model equipped with a joint training algorithm over soft rules and KG facts to inject the logical background knowledge of rules into embeddings, as well as forward chaining inference over rules. Evaluations on Freebase and DBpedia show that SoLE not only achieves improvements of 11.6%/5.9% in Mean Reciprocal Rank (MRR) and 18.4%/15.9% in HITS@1 compared to the model on which SoLE is based, but also significantly and consistently outperforms the state-of-the-art baselines in the link prediction task.

关键词： knowledge graph knowledge graph embedding logical rule link prediction

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Modelling Multi-relations for Convolutional-based knowledge graph embedding

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Procedia Computer Science 2022年 207卷 624-633页

作者： Sirui Li Kok Wai Wong Dengya Zhu Chun Che Fung Discipline of Information Technology Murdoch University South St Murdoch Western Australia

Representation learning of knowledge graphs aims to embed entities and relations into low-dimensional vectors. Most existing works only consider the direct relations or paths between an entity pair. It is considered that such approaches disconnect the semantic connection of multi-relations between an entity pair, and we propose a convolutional and multi-relational representation learning model, ConvMR. The proposed ConvMR model addresses the multi-relation issue in two aspects: (1) Encoding the multi-relations between an entity pair into a unified vector that maintains the semantic connection. (2) Since not all relations are necessary while joining multi-relations, we propose an attention-based relation encoder to automatically assign weights to different relations based on semantic hierarchy. Experimental results on two popular datasets, FB15k-237 and WN18RR, achieved consistent improvements on the mean rank. We also found that ConvMR is efficient to deal with less frequent entities.

关键词： knowledge graph embedding Multi-relation Convolution Representation learning

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TransModE: Translational knowledge graph embedding Using Modular Arithmetic

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Procedia Computer Science 2022年 207卷 1154-1163页

作者： Hussein Baalbaki Hussein Hazimeh Hassan Harb Rafael Angarita Institut Supérieur d'Electronique de Paris LISITE 75006 Paris France Data Science department Faculty of Information Lebanese University Beirut Lebanon Computer Science department Faculty of Sciences Lebanese University Beirut Lebanon

In order to improve link prediction process in a knowledge graph, we tackle the problem of learning representations of entities and relations. The strength of the existing models in this domain mainly relies on its ability of modeling and inferring the patterns of the relations. In this paper, we propose a new knowledge graph embedding approach called TransModE that has the ability to represent all simple and complex relation patterns. Inspired by TransE, TransModE represents the relations between the entities of a knowledge graph as a transition in the modulus space. Experimenting our model on multiple benchmark knowledge graphs shows the simplicity and the scalability of TransModE. It also shows that TransModE outperforms existing state-of-the-art models by being able to infer and model all types of relations.

关键词： knowledge graph embedding knowledge graphs Link Prediction Reasoning

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SDFormer: A shallow-to-deep feature interaction for knowledge graph embedding

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knowledge-BASED SYSTEMS 2024年 284卷

作者： Li, Duantengchuan Xia, Tao Wang, Jing Shi, Fobo Zhang, Qi Li, Bing Xiong, Yu Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing 400065 Peoples R China Cent China Normal Univ Natl Engn Res Ctr Elearning Wuhan 430079 Peoples R China Cent China Normal Univ Sch Informat Management Wuhan 430079 Peoples R China Hubei Luojia Lab Wuhan 430079 Peoples R China Chongqing Univ Posts & Telecommun Res Ctr Artificial Intelligence & Smart Educ Chongqing 400065 Peoples R China

Inferring missing information from current facts in a knowledge graph (KG) is the target of the link prediction task. Currently, existing methods embed the entities and relations of KG as a whole into a low-dimensional vector space. Nonetheless, they ignore the multi-level interactions (shallow interactions, deep interactions) among the finer-grained sub-features of entities and relations. To overcome these limitations, we present a shallow-to-deep feature interaction for knowledge graph embedding (SDFormer). It takes into account the interpretability of sub-feature tokens of entities and relations and learns shallow-to-deep interaction information between entities and relations at a more fine-grained level. Specifically, entity and relation vectors are decomposed into sub-features to represent multi-dimensional information. Then, a shallow-to-deep feature interaction method is designed to capture multi-level interactions between entities and relations. This process enriches the feature representation by modeling the interaction between sub-features. Finally, a 1-X scoring function is utilized to calculate the score of each knowledge triplet. The experimental results on several benchmark datasets show that SDFormer obtains competitive performance results and more efficient training efficiency on other comparative models and because of the shallow-to-deep feature interaction between entities and relations.

关键词： Link prediction knowledge graph embedding Shallow interaction Deep interaction Attention mechanism Vector tokenization

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Prediction of adverse drug reactions based on knowledge graph embedding

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2021年第1期21卷 38-38页

作者： Zhang, Fei Sun, Bo Diao, Xiaolin Zhao, Wei Shu, Ting Chinese Acad Med Sci & Peking Union Med Coll Natl Ctr Cardiovasc Dis Fuwai Hosp Dept Informat Ctr 167 North Lishi Rd Beijing 100037 Peoples R China Natl Hlth Commiss Natl Inst Hosp Adm Bldg 3Yard 6Shouti South Rd Beijing 100044 Peoples R China

BackgroundAdverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug *** on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ***, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was *** this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.

关键词： Adverse Drug Reactions knowledge graph embedding Word2Vec DrugBank

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knowledge graph incremental embedding for unseen modalities

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knowledge AND INFORMATION SYSTEMS 2023年第9期65卷 3611-3631页

作者： Wei, Yuyang Chen, Wei Wen, Shiting Liu, An Zhao, Lei Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China NingboTech Univ Sch Comp & Data Engn Ningbo 315100 Zhejiang Peoples R China

knowledge graph embedding (KGE) projects entities and relations into low-dimension continuous vector space, which promotes the widespread use of knowledge graphs in many downstream applications. Despite the fact that most of existing KGE models focus on static single-modal knowledge graphs, many knowledge graphs are incremental and multi-modal in the real world. Especially, the incremental entities are accompanied by new modalities, i.e., unseen modalities in background knowledge graphs. To well address the novel task, i.e., incrementally embed the new entities with unseen modal information effectively and efficiently, we propose a novel incremental multi-modal knowledge graph embedding model entitled Multi-Modal Rotating on Hyperplanes, which consists of the following two modules. (1) To gain a high-quality background KGE space, the module Background knowledge graph embedding Module is developed to fuse seen modal information with a gated multi-modal encoder and decode the triples by a rotation-based KGE model. (2) The module Incremental knowledge graph embedding Module is designed to fuse unseen modal information of incremental entities and incrementally embed the new entities into the trained embedding space. Extensive experiments are conducted on two real-world multi-modal datasets, and the results demonstrate the superiority of the proposed model in terms of both effectiveness and efficiency compared with the state-of-the-art approaches.

关键词： knowledge graph knowledge graph embedding Incremental embedding Unseen modalities

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KEMA plus plus : A Full Representative knowledge-graph embedding Model (036)

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND knowledge ENGINEERING 2022年第11N12期32卷 1619-1641页

作者： Baalbaki, Hussein Hazimeh, Hussein Harb, Hassan Angarita, Rafael Sorbonne Univ ISEP Paris France Lebanese Univ Fac Sci Beirut Lebanon Amer Univ Middle East Coll Engn & Technol Egaila Kuwait

Nowadays, representing entities and relations in a machine understandable way through knowledge graph embedding (KGE) has been proven as an effective approach for predicting missing links in knowledge graphs (KGs). Mainly, the success of such approach depends on the model ability to infer the patterns of the relations. Indeed, most of the existing KG models highly focus on modeling simple relation patterns such as symmetry, anti-symmetry, inversion, and composition. However, there are few models in the literature that take into consideration the modeling of complex relation patterns like 1-N, N-1 and N-N, which are common in real-world applications. To overcome this challenge, this paper presents a new KGE model called KEMA++, i.e. KGE using Modular Arithmetic, that relies on the combination of projection and modular arithmetic. The main idea behind KEMA++ is to project the entities of a relation to represent the relations of a KG, before applying a modular arithmetic over it. Thus, KEMA++ will be able to infer all simple and complex relation patterns for any KGE applications. Through extensive experiments on several datasets, we demonstrated the relevance of KEMA++ in terms of effectively representing all model relations in a KG. Simulations on several tested datasets show that KEMA++ obtains the good scores for Mean Rank (MR) and Hits@1 tests. Moreover, KEMA++ obtains the good Hits@1 score compared to the existing models.

关键词： knowledge graph embedding knowledge graphs link prediction reasoning modular arithmetic (036)

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A Closer Look at Probability Calibration of knowledge graph embedding 22

A Closer Look at Probability Calibration of Knowledge Graph ...

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Proceedings of the 11th International Joint Conference on knowledge graphs

作者： Ruiqi Zhu Fangrong Wang Alan Bundy Xue Li Kwabena Nuamah Lei Xu Stefano Mauceri Jeff Z. Pan University of Edinburgh School of Informatics United Kingdom Ireland Research Centre Huawei Ireland Research Centre Huawei Ireland School of Informatics University of Edinburgh United Kingdom and Huawei United Kingdom

ISBN: (纸本)9781450399876

When the estimated probabilities do not match the relative frequencies, we say these estimated probabilities are uncalibrated [39], which may cause incorrect decision making, and is particularly undesired in high-stakes tasks [45]. knowledge graph embedding models are reported to produce uncalibrated probabilities [36], e.g., for all the triples predicted with probability 0.9, the percentage of them being truly correct triples is not . In this article, we take a closer look at this problem. First, we confirmed the issue that typical KG embedding models are uncalibrated. Then, we show how off-the-shelf calibration techniques can be used to mitigate this issue, among which binning-based calibration produces more calibrated probabilities. We also investigated the possible reasons for the uncalibrated probabilities and found that the expit transform, the way used to convert embedding scores into probabilities, is ineffective in most cases.

关键词： knowledge graph embedding Probability Calibration

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Explainable knowledge Reasoning Framework Using Multiple knowledge graph embedding 21

Explainable Knowledge Reasoning Framework Using Multiple Kno...

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Proceedings of the 10th International Joint Conference on knowledge graphs

作者： Mori Kurokawa KDDI Research Inc. Japan

ISBN: (纸本)9781450395656

knowledge reasoning using knowledge graphs has attracted much attention. However, there is difficulty in integrating various related works to realize complex reasoning with explanation using multiple knowledge graphs. To do this, I propose a reasoning framework which combines multiple knowledge graph embedding techniques with corresponding explainable AI techniques. Experiments using the third knowledge graph reasoning challenge dataset demonstrate the effectiveness of the framework.

关键词： knowledge reasoning explainable AI knowledge graph embedding knowledge graph

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