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检索条件"主题词=Knowledge Graph embedding"

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Efficient Parallel Translating embedding For knowledge graphs 17

Efficient Parallel Translating Embedding For Knowledge Graph...

引用

IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Zhang, Denghui Li, Manling Jia, Yantao Wang, Yuanzhuo Cheng, Xueqi Chinese Acad Sci Inst Comp Technol 6 Kexueyuan South Rd Beijing 100190 Peoples R China

ISBN: (纸本)9781450349512

knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces. Translating embedding methods regard relations as the translation from head entities to tail entities, which achieve the state-of-the-art results among knowledge graph embedding methods. However, a major limitation of these methods is the time consuming training process, which may take several days or even weeks for large knowledge graphs, and result in great difficulty in practical applications. In this paper, we propose an efficient parallel framework for translating embedding methods, called ParTrans-X, which enables the methods to be paralleled without locks by utilizing the distinguished structures of knowledge graphs. Experiments on two datasets with three typical translating embedding methods, i.e., TransE [3], TransH [19], and a more efficient variant TransE- AdaGrad [11] validate that ParTrans-X can speed up the training process by more than an order of magnitude.

关键词： knowledge graph embedding Translation-based Parallel

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Relation embedding for Personalised Translation-Based POI Recommendation 24th

Relation Embedding for Personalised Translation-Based POI Re...

引用

24th Pacific-Asia Conference on knowledge Discovery and Data Mining (PAKDD)

作者： Wang, Xianjing Salim, Flora D. Ren, Yongli Koniusz, Piotr RMIT Univ Melbourne Vic Australia CSIRO Data61 Canberra ACT Australia Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9783030474263;9783030474256

Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI recommendations. To this end, we propose a translation-based relation embedding for POI recommendation. Our approach encodes the temporal and geographic information, as well as semantic contents effectively in a low-dimensional relation space by using knowledge graph embedding techniques. To further alleviate the issue of user-POI matrix sparsity, a combined matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information. Experiments on two real-world datasets demonstrate the effectiveness of our proposed model.

关键词： knowledge graph embedding Collaborative filtering Matrix factorization Recommender System POI recommendation

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LiterallyWikidata - A Benchmark for knowledge graph Completion Using Literals 20th

LiterallyWikidata - A Benchmark for Knowledge Graph Completi...

引用

20th International Semantic Web Conference (ISWC)

作者： Gesese, Genet Asefa Alam, Mehwish Sack, Harald FIZ Karlsruhe Leibniz Inst Informat Infrastruct Eggenstein Leopoldshafen Germany Karlsruhe Inst Technol Inst AIFB Karlsruhe Germany

ISBN: (纸本)9783030883614;9783030883607

In order to transform a knowledge graph (KG) into a low dimensional vector space, it is beneficial to preserve as much semantics as possible from the different components of the KG. Hence, some link prediction approaches have been proposed so far which leverage literals in addition to the commonly used links between entities. However, the procedures followed to create the existing datasets do not pay attention to literals. Therefore, this study presents a set of KG completion benchmark datasets extracted from Wikidata and Wikipedia, named LiterallyWikidata. It has been prepared with the main focus on providing benchmark datasets for multimodal KG embedding (KGE) models, specifically for models using numeric and/or text literals. Hence, the benchmark is novel as compared to the existing datasets in terms of properly handling literals for those multimodal KGE models. LiterallyWikidata contains three datasets which vary both in size and structure. Benchmarking experiments on the task of link prediction have been conducted on LiterallyWikidata with extensively tuned unimodal/multimodal KGE models. The datasets are available at https://***/10.5281/zenodo.4701190.

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

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A knowledge graph Question Answering Approach Based on graph Attention Networks and Relational Path Encoding 20th

A Knowledge Graph Question Answering Approach Based on Graph...

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20th International Conference on Intelligent Computing (ICIC)

作者： Cao, Shuxin Zhu, Xiaoxu Li, Peifeng Soochow Univ Suzhou 215026 Peoples R China

ISBN: (纸本)9789819756681;9789819756698

Most of the previous knowledge graph question answering (KGQA) methods focus on question parsing and ignore the knowledge graph information. In order to improve the utilization of knowledge graph structure information, based on the good performance of graph networks in the field of knowledge graph embedding, this paper proposes to utilize relational graph attention networks to pre-train the knowledge graph Q&A database. Entity and relation representations are obtained by fusing multi-hop information and the results are used to initialize the vector representation of nodes and edges in the graph. Inference is then performed using a multi-hop Q&A network based on relational paths. Since most current inference models do not consider path-level information, LSTM is introduced to encode paths in the model fusion process. In order to improve the reliability of inference, a multilayer attention mechanism is introduced to calculate the similarity between the paths and the original problem, so as to obtain the weight coefficients of the paths, which are fused into the relation and entity scores. Experiments demonstrate that the optimized Q&A inference method outperforms the original and baseline models on the MetaQA, WebQSP and CWQ datasets.

关键词： knowledge graph Q&A Second Keyword knowledge graph embedding graphical attention network

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Representing knowledge graphs with Gaussian Mixture embedding 14th

Representing Knowledge Graphs with Gaussian Mixture Embeddin...

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14th International Conference on knowledge Science, Engineering, and Management (KSEM)

作者： Feng, Wenying Zha, Daren Guo, Xiaobo Dong, Yao He, Yuanye Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9783030821364;9783030821357

knowledge graph embedding (KGE) has attracted more and more attention and has been widely used in downstream AI tasks. Some proposed models learn the embeddings of knowledge graph (KG) into a low-dimensional continuous vector space by optimizing a customized loss function. However, these methods either ignore the polysemy of entities/relations in KG, or cannot model the uncertainty of them. Therefore, we propose KG2GM (knowledge graph to Gaussian Mixture), a density-based KGE method, for modeling the polysemy and uncertainty of KG simultaneously. Each entity/relation in KG is represented by a Gaussian mixture probability distribution. Each Gaussian component of the mixed distribution represents one kind of semantics, where the mean vector denotes its space position, and the covariance matrix denotes its uncertainty. In this way we can model the polysemy and uncertainty of KG simultaneously. We employ symmetrized KL divergence and EL (Expected Likelihood) to measure the component similarities and make their probabilistic combination as the score function of the triplet. We conduct experiments on link prediction with two benchmark datasets (WN18RR and FB15k-237). The results show that our method can effectively model the polysemy and uncertainty of entities and relations in KG.

关键词： knowledge graph embedding Gaussian Mixture Model Distributed representation knowledge graph completion Link prediction

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Neural-Answering Logical Queries on knowledge graphs 21

Neural-Answering Logical Queries on Knowledge Graphs

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27th ACM SIGKDD International Conference on knowledge Discovery and Data Mining (KDD)

作者： Liu, Lihui Du, Boxin Ji, Heng Zhai, ChengXiang Tong, Hanghang Univ Illinois Dept Comp Sci Champaign IL 61820 USA

ISBN: (纸本)9781450383325

Logical queries constitute an important subset of questions posed in knowledge graph question answering systems. Yet, effectively answering logical queries on large knowledge graphs remains a highly challenging problem. Traditional subgraph matching based methods might suffer from the noise and incompleteness of the underlying knowledge graph, often with a prolonged online response time. Recently, an alternative type of method has emerged whose key idea is to embed knowledge graph entities and the query in an embedding space so that the embedding of answer entities is close to that of the query. Compared with subgraph matching based methods, it can better handle the noisy or missing information in knowledge graph, with a faster online response. Promising as it might be, several fundamental limitations still exist, including the linear transformation assumption for modeling relations and the inability to answer complex queries with multiple variable nodes. In this paper, we propose an embedding based method (NEWLOOK) to address these limitations. Our proposed method offers three major advantages. First (Applicability), it supports four types of logical operations and can answer queries with multiple variable nodes. Second (Effectiveness), the proposed NEWLOOK goes beyond the linear transformation assumption, and thus consistently outperforms the existing methods. Third (Efficiency), compared with subgraph matching based methods, NEWLOOK is at least 3 times faster in answering the queries;compared with the existing embedding based methods, NEWLOOK bears a comparable or even faster online response and offline training time.

关键词： knowledge graph query knowledge graph embedding Logical query embedding

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Entity Alignment Across knowledge graphs Based on Representative Relations Selection 5

Entity Alignment Across Knowledge Graphs Based on Representa...

引用

5th International Conference on Systems and Informatics (ICSAI)

作者： Zhang, Youmin Liu, Li Fu, Shun Zhong, Fujin Chongqing Inst Engn Sch Software Chongqing Peoples R China Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9781728101200

Entity alignment across knowledge graphs is an important task in web mining. The aligned entities can be used for transferring knowledge across knowledge graphs and benefit several tasks such as cross-lingual knowledge graph construction and knowledge reasoning. This paper propose a representation learning based algorithm for embedding knowledge graph and aligning entities. In particular, considering the multi-type relations in knowledge graph, we select the alignment-task driven representative relations based on the pre-aligned entity pairs. With the help of selected relations, we embed the entities across networks into a common space by modeling entities' head/tail are with corresponding context vectors. For entity alignment task, pre-aligned entities are adopted to facilitate the transfer of context information across the knowledges graphs. Through this way, the problem of entity embedding and alignment can be solved simultaneously under a unified framework.. Extensive experiments on two multi-lingual knowledge graphs demonstrate the effectiveness of the proposed model comparing with several state-of-the-art models.

关键词： Entity Alignment knowledge graph embedding Representative Relation Selection

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embedding Dynamic knowledge graphs based on Observational Ontologies in Semantic Vector Spaces

Embedding Dynamic Knowledge Graphs based on Observational On...

引用

Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III

作者： Millar, Declan Braines, Dave D'Arcy, Laura Barclay, Iain Summers-Stay, Doug Cripps, Paul IBM Res Europe Hursley England Cardiff Univ Sch Comp Sci & Informat Cardiff Wales US Army Res Lab Adelphi MD USA Def Sci & Technol Lab Salisbury Wilts England

ISBN: (数字)9781510643307

ISBN: (纸本)9781510643307

knowledge graphs (KGs) provide a useful representation format for capturing complex knowledge about an information domain, with rich logical descriptions available for defining the relationships between entities. Separately, semantic vector spaces (SVSs) capture the relative meanings of terms based on their actual usage within a dataset and allow useful operations for exploring the relationships between these terms. Combining KGs and SVSs via knowledge graph embedding (KGE) enables further analysis tasks to leverage learned semantic vectors to gain additional insights. Therefore, KGE represents an interesting and potentially powerful tool for identifying emergent or unexpected behavior, or for seeking previously unaccounted for relationships, event, and groups. In this work, we report on the state-of-the-art in KGE. We describe the operational benefits that can be gained from this approach and the considerations that apply for observational ontologies that describe a complex, untrusted, time-sensitive, and rapidly-evolving environment. We suggest several promising avenues for future research in this context.

关键词： Semantic vector spaces knowledge graphs knowledge graph embedding Observational ontologies

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

引用

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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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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