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11th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC)

作者： Xiao, Yao Zhou, Guangyou Liu, Jin Cent China Normal Univ Wuhan Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9783031171208;9783031171192

Question answering over temporal knowledge graphs (TKGQA) has attracted great attentions in natural language processing community. One of the key challenges is how to effectively model the representations of questions and the candidate answers associated with timestamp constraints. Many existing methods attempt to learn temporal knowledge graph embedding for entities, relations and timestamps. However, these existing methods cannot effectively exploiting temporal knowledge graph embeddings to capture time intervals (e.g., "WWII" refers to 1939-1945) as well as temporal relation words (e.g., "first" and "last") appeared in complex questions, resulting in the sub-optimal results. In this paper, we propose a temporal-sensitive information for complex question answering (TSIQA) framework to tackle these problems. We employ two alternative approaches to augment questions embeddings with question-specific time interval information, which consists of specific start and end timestamps. We also present auxiliary contrastive learning to contrast the answer prediction and prior knowledge regarding time approximation for questions that only differ by the temporal relation words. To evaluate the effectiveness of our proposed method, we conduct the experiments on CRONQUESTION. The results show that our proposed model achieves better improvements over the state-of-the-art models that require multiple steps of reasoning.

关键词： Question answering Temporal knowledge graphs knowledge graph embedding

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Enhancing citation recommendation using citation network embedding

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SCIENTOMETRICS 2022年第1期127卷 233-264页

作者： Pornprasit, Chanathip Liu, Xin Kiattipadungkul, Pattararat Kertkeidkachorn, Natthawut Kim, Kyoung-Sook Noraset, Thanapon Hassan, Saeed-Ul Tuarob, Suppawong Mahidol Univ Fac Informat & Commun Technol Salaya Nakhon Pathom Thailand Natl Inst Adv Ind Sci & Technol Tokyo Japan Manchester Metropolitan Univ Dept Comp & Math Manchester Lancs England

Automatic recommendation of citations has been a focal point of research in scholarly digital libraries. Many graph-based citation recommendation algorithms have been proposed;however, most of them utilize local citation behavior from the citation network that results in recommending papers in the same proximity as the query article. In this paper, we propose to capture the global citation behavior in the citation network and use it to enhance the citation recommendation performance. Specifically, we develop a novel citation network embedding algorithm, ConvCN, to encode the citation relationship among papers. We then propose to enhance existing graph-based citation recommendation algorithms by incorporating ConvCN to improve the recommendation efficacy. ConvCN has been shown to improve the citation recommendation performance by 44.86% and 34.87% on average in terms of Bpref and F-measure@20, respectively. The findings from this research not only confirm that global citation behavior could be additionally useful for improving the performance of traditional citation recommendation algorithms but also shed light on the possibility to adapt the proposed ConvCN algorithm for other recommendation tasks that rely on graph-like information such as items recommendation in social networks and people recommendation in referral networks.

关键词： Citation recommendation knowledge graph embedding Convolutional neural networks graph representation learning

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Comprehensive Analysis of Freebase and Dataset Creation for Robust Evaluation of knowledge graph Link Prediction Models 1

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22nd International Semantic Web Conference (ISWC)

作者： Shirvani-Mahdavi, Nasim Akrami, Farahnaz Saeef, Mohammed Samiul Shi, Xiao Li, Chengkai Univ Texas Arlington Arlington TX 76019 USA

ISBN: (数字)9783031472435

ISBN: (纸本)9783031472428;9783031472435

Freebase is amongst the largest public cross-domain knowledge graphs. It possesses three main data modeling idiosyncrasies. It has a strong type system;its properties are purposefully represented in reverse pairs;and it uses mediator objects to represent multiary relationships. These design choices are important in modeling the real-world. But they also pose nontrivial challenges in research of embedding models for knowledge graph completion, especially when models are developed and evaluated agnostically of these idiosyncrasies. This paper lays out a comprehensive analysis of the challenges associated with the idiosyncrasies of Freebase and measures their impact on knowledge graph link prediction. The results fill an important gap in our understanding of embedding models for link prediction as such models were never evaluated using a proper full-scale Freebase dataset. The paper also makes available several variants of the Freebase dataset by inclusion and exclusion of the data modeling idiosyncrasies. It fills an important gap in dataset availability too as this is the first-ever publicly available full-scale Freebase dataset that has gone through proper preparation.

关键词： knowledge graph completion Link prediction knowledge graph embedding Benchmark dataset

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A knowledge graph-based Clustering Approach for Drug Side Effects Prediction

A Knowledge Graph-based Clustering Approach for Drug Side Ef...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zheng, Ying Xu, ShiBo Changsha University of Science & Technology School of Computer & Communication Engineering Changsha China

ISBN: (纸本)9798350337488

Research on drug side effects contributes to reducing health risks for patients and decreasing drug development costs. In recent years, machine learning methods have emerged as prominent tools to support analyzing and predicting drug side effects. In this paper, we propose a clustering model based on knowledge graph embedding (GEBC) to predict potential side effects. GEBC integrates various drug features into a heterogeneous network, then mines the underlying relationship of nodes in network using Node2vec algorithm. Finally, GEBC clusters the node embeddings by Gaussian Mixture Model, and builds a Bayesian probability prediction model. GEBC achieves 0.4343, 0.442, 0.411 AUPR score on three benchmark datasets, respectively. © 2023 IEEE.

关键词： clustering knowledge graph embedding side effect

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AMD Results for OAEI 2023 18

AMD Results for OAEI 2023

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18th International Workshop on Ontology Matching, OM 2023

作者： Wang, Zhu ADVIS Lab. Dept of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

AgreementMakerDeep (AMD) is a new flexible and extensible ontology matching system. It exploits the contextual and structural information of ontologies by infusing knowledge to pre-trained masked language model, and then filter the output mappings using knowledge graph embedding techniques. AMD learns from classes and their relations between classes by constructing vector representations into the low dimensional embedding space with knowledge graph embedding methods. The results demonstrate that AMD achieves a competitive performance in many OAEI tracks, but AMD has limitations for property and instance matching. © 2023 Copyright for this paper by its authors.

关键词： knowledge graph embedding Large Language model Ontology matching

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SAMI: A Structure-Aware Multi-Partition embedding Interaction Model for Accurate Link Prediction in knowledge graphs

SAMI: A Structure-Aware Multi-Partition Embedding Interactio...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gao, Shuai Li, Ming Zhao, Jing Shi, Junkang Jinan China College of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (纸本)9798350337020

knowledge graph embedding (KGE), which applies representation learning to represent entities and relationships in knowledge graphs, has attracted significant attention from researchers due to its potential applications in various domains. However, most of the existing KGE methods suffer from the limitation of using single semantic information. This limitation fails to capture the complex and structural information in knowledge graphs (KGs). In this paper, we introduce a novel method called the Structure-Aware Enhanced Multi- Partition embedding Interaction (SAMI) model for knowledge graph embedding (KGE). SAMI leverages both graph attention network and tensor decomposition to learn expressive and structural enhanced representations for KGs. Specifically, it uses the graph attention layers to aggregate nodes' features in a neighborhood as an encoder and utilizes an Enhanced Multi- Partition embedding Interaction (EMEI) to learn independent local features as a decoder. SAMI shows impressive results on several popular datasets compared with baseline methods in terms of both Mean Reciprocal Rank (MRR) and Hits@K. © 2023 IEEE.

关键词： graph Attention Network knowledge graph embedding Link Prediction

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Contextualized Structural Self-supervised Learning for Ontology Matching 18

Contextualized Structural Self-supervised Learning for Ontol...

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18th International Workshop on Ontology Matching, OM 2023

作者： Wang, Zhu Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Ontology matching (OM) entails the identification of semantic relationships between concepts within two or more knowledge graphs (KGs) and serves as a critical step in integrating KGs from various sources. Recent advancements in deep OM models have harnessed the power of transformer-based language models and the advantages of knowledge graph embedding. Nevertheless, these OM models still face persistent challenges, such as a lack of reference alignments, runtime latency, and unexplored different graph structures within an end-to-end framework. In this study, we introduce a novel self-supervised learning OM framework with input ontologies, called LaKERMap. This framework capitalizes on the contextual and structural information of concepts by integrating implicit knowledge into transformers. Specifically, we aim to capture multiple structural contexts, encompassing both local and global interactions, by employing distinct training objectives. To assess our methods, we utilize the Bio-ML datasets and tasks. The findings from our innovative approach reveal that LaKERMap surpasses state-of-the-art systems in terms of alignment quality and inference time. Our models and codes are available here https://***/ellenzhuwang/lakermap. © 2023 Copyright for this paper by its authors.

关键词： knowledge graph embedding Ontology matching Self-supervised learning

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Fantastic knowledge graph embeddings and How to Find the Right Space for Them 19th

Fantastic Knowledge Graph Embeddings and How to Find the Rig...

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19th International Semantic Web Conference (ISWC)

作者： Nayyeri, Mojtaba Xu, Chengjin Vahdati, Sahar Vassilyeva, Nadezhda Sallinger, Emanuel Yazdi, Hamed Shariat Lehmann, Jens Univ Bonn Smart Data Analyt Grp SDA Bonn Germany InfAI Dresden Lab Dresden Germany Univ Oxford Oxford England TU Wien Vienna Austria Fraunhofer IAIS Dresden Lab Dresden Germany

ISBN: (纸本)9783030624194;9783030624187

During the last few years, several knowledge graph embedding models have been devised in order to handle machine learning problems for knowledge graphs. Some of the models which were proven to be capable of inferring relational patterns, such as symmetry or transitivity, show lower performance in practice than those not allowing to infer those patterns. It is often unknown what factors contribute to such performance differences among KGE models in the inference of particular patterns. We develop the concept of a solution space as a factor that has a direct influence on the practical performance of knowledge graph embedding models as well as their capability to infer relational patterns. We showcase the effect of solution space on a newly proposed model dubbed SpacEss. We describe the theoretical considerations behind the solution space and evaluate our model against state-of-the-art models on a set of standard benchmarks namely WordNet and FreeBase.

关键词： Link prediction knowledge graph embedding Relation pattern Solution space knowledge completion

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Introducing RezoJDM16k: a French knowledge graph DataSet for Link Prediction 13

Introducing RezoJDM16k: a French Knowledge Graph DataSet for...

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13th International Conference on Language Resources and Evaluation (LREC)

作者： Mirzapour, Mehdi Ragheb, Waleed Saeedizade, Mohammad Javad Cousot, Kevin Jacquenet, Helene Carbon, Lawrence Lafourcade, Mathieu ContentSide R&D Dept Lyon France Univ Montpellier LIRMM CNRS Montpellier France IUST Tehran Iran Emvista R&D Dept Montpellier France Cairo Univ FCAI Cairo Egypt

ISBN: (纸本)9791095546726

knowledge graphs applications, in industry and academia, motivate substantial research directions towards large-scale information extraction from various types of resources. Nowadays, most of the available knowledge graphs are either in English or multilingual. In this paper, we introduce RezoJDM16k, a French knowledge graph dataset based on RezoJDM (Lafourcade, 2007). With 16k nodes, 832k triplets and 53 relation types, RezoJDM16k can be employed in many NLP downstream tasks for the French language such as machine translation, question-answering and recommendation systems. In addition, we provide strong knowledge graph embedding baselines that are used in link prediction task for future benchmarking. Compared to the state-of-the-art English knowledge graph datasets used in link prediction, RezoJDM16k shows a similar promising predictive behavior.

关键词： language resource knowledge graph dataset link prediction knowledge graph embedding knowledge graph completion lexical-semantic network

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Learning Context-based embeddings for knowledge graph Completion

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Journal of Data and Information Science 2022年第2期7卷 84-106页

作者： Fei Pu Zhongwei Zhang Yan Feng Bailin Yang School of Computer and Information Engineering Zhejiang Gongshang University Hangzhou 310018China

Purpose:Due to the incompleteness nature of knowledge graphs(KGs),the task of predicting missing links between entities becomes *** previous approaches are static,this posed a notable problem that all meanings of a polysemous entity share one embedding *** study aims to propose a polysemous embedding approach,named KG embedding under relational contexts(ContE for short),for missing link ***/methodology/approach:ContE models and infers different relationship patterns by considering the context of the relationship,which is implicit in the local neighborhood of the *** forward and backward impacts of the relationship in ContE are mapped to two different embedding vectors,which represent the contextual information of the ***,according to the position of the entity,the entity’s polysemous representation is obtained by adding its static embedding vector to the corresponding context vector of the ***:ContE is a fully expressive,that is,given any ground truth over the triples,there are embedding assignments to entities and relations that can precisely separate the true triples from false *** is capable of modeling four connectivity patterns such as symmetry,antisymmetry,inversion and *** limitations:ContE needs to do a grid search to find best parameters to get best performance in practice,which is a time-consuming ***,it requires longer entity vectors to get better performance than some other *** implications:ContE is a bilinear model,which is a quite simple model that could be applied to large-scale *** considering contexts of relations,ContE can distinguish the exact meaning of an entity in different triples so that when performing compositional reasoning,it is capable to infer the connectivity patterns of relations and achieves good performance on link prediction ***/value:ContE considers the contexts of entities in terms of thei

关键词： Full expressiveness Relational contexts knowledge graph embedding Relation patterns Link prediction

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