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knowledge graph embedding Based Collaborative Filtering

IEEE ACCESS 2020年 8卷 134553-134562页

作者： Zhang, Yuhang Wang, Jun Luo, Jie Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Software Dev Environm Beijing 100191 Peoples R China

Along with the rapidly increasing massive online data, recommender systems have been used as an effective approach for filtering useful information, which have been widely adopted in many web applications. In recent years, new techniques such as neural network and deep learning have demonstrated its effectiveness in studies of recommender systems. In this paper, we propose a novel approach for collaborative filtering with implicit feedback based on knowledge graph embedding. The basic ideal is to model the interactions between users and items as an interaction knowledge graph with a single relation, whose vector representation is learned through knowledge graph embedding. Base on the learned representation, the collaborative filtering problem is converted into link prediction in the interaction knowledge graph. KGECF, a neural network for knowledge graph embedding based collaborative filtering, is proposed based on this ideal and the RotatE knowledge graph embedding model. Experimental results on five datasets with different characteristics show that the KGECF model achieves the state-of-the-art (SOTA) performance on all datasets. And unlike other SOTA models that have obvious performance drops on AMusic and AToy datasets, our model's performance is very stable across all datasets.

关键词： Collaboration Recommender systems Neural networks Computational modeling Measurement Task analysis Collaborative filtering link prediction knowledge graph embedding recommender system

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Multi-hop knowledge graph Reasoning Based on Hyperbolic knowledge graph embedding and Reinforcement Learning 21

Multi-hop Knowledge Graph Reasoning Based on Hyperbolic Know...

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Proceedings of the 10th International Joint Conference on knowledge graphs

作者： Xingchen Zhou Peng Wang Qiqing Luo Zhe Pan School of Computer Science and Engineering Southeast University China

ISBN: (纸本)9781450395656

Some unseen facts in knowledge graphs (KGs) can be complemented by multi-hop knowledge graph reasoning. The process of multi-hop reasoning can be presented as a serialized decision problem, and then be solved with reinforcement learning (RL). However, existing RL-based reasoning methods cannot deal with the hierarchical information in KGs effectively. To solve this issue, we propose a novel RL-based multi-hop KG reasoning model Path Additional Action-space Ranking (PAAR). Our model first proposes a hyperbolic knowledge graph embedding (KGE) model which can effectively capture hierarchical information in KGs. To introduce hierarchical information into RL, the hyperbolic KGE vector is subsequently added to the state space and helps to expand the action space. In order to alleviate the reward sparsity problem in RL, our model utilizes the score function of hyperbolic KGE model as a soft reward. Finally, the metric of hyperbolic space is added to the training of RL as a penalty strategy to constrain the sufficiency of multi-hop reasoning paths. Experimental results on two real-world datasets show that our proposed model not only provides effective answers but also offers sufficient paths.

关键词： reinforcement learning knowledge graph reasoning knowledge graph embedding

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Relaxing Unanswerable Geographic Questions Using A Spatially Explicit knowledge graph embedding Model 22nd

Relaxing Unanswerable Geographic Questions Using A Spatially...

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22nd AGILE Conference on Geographic Information Science

作者： Mai, Gengchen Yan, Bo Janowicz, Krzysztof Zhu, Rui UC Santa Barbara STKO Lab Santa Barbara CA 93106 USA

ISBN: (纸本)9783030147457;9783030147440

Recent years have witnessed a rapid increase in Question Answering (QA) research and products in both academic and industry. However, geographic question answering remained nearly untouched although geographic questions account for a substantial part of daily communication. Compared to general QA systems, geographic QA has its own uniqueness, one of which can be seen during the process of handling unanswerable questions. Since users typically focus on the geographic constraints when they ask questions, if the question is unanswerable based on the knowledge base used by a QA system, users should be provided with a relaxed query which takes distance decay into account during the query relaxation and rewriting process. In this work, we present a spatially explicit translational knowledge graph embedding model called TransGeo which utilizes an edge-weighted PageRank and sampling strategy to encode the distance decay into the embedding model training process. This embedding model is further applied to relax and rewrite unanswerable geographic questions. We carry out two evaluation tasks: link prediction as well as query relaxation/rewriting for an approximate answer prediction task. A geographic knowledge graph training/testing dataset, DB18, as well as an unanswerable geographic query dataset, GeoUQ, are constructed. Compared to four other baseline models, our TransGeo model shows substantial advantages in both tasks.

关键词： Geographic question answering Query relaxation knowledge graph embedding Spatially explicit model

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Kernel multi-attention neural network for knowledge graph embedding

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knowledge-BASED SYSTEMS 2021年 227卷 107188-107188页

作者： Jiang, Dan Wang, Ronggui Yang, Juan Xue, Lixia Hefei Univ Technol Sch Comp & Informat Hefei 230601 Peoples R China

Link prediction is the problem of predicting missing link between entities and relations for knowledge graph. In recent years, some tasks have achieved great success for link prediction, but these tasks are far from expanding entity relation vectors, and cannot predict missing links more efficiently. In this paper, we propose a novel link prediction method called kernel multi-attention neural network for knowledge graph embedding (KMAE) which is able to extend kernel separately in entity and relation attributes. The kernel function uses Gaussian kernel function to expand into more robust entity kernel and relation kernel. In addition, we constructed a novel multi-attention neural network that acts on the entity kernel and relation kernel which can capture local important characteristics. Experiments on FB15k-237 and WN18RR, show that multi-attention fully reflect excellent performance in the task of knowledge graph embedding. Our proposed KMAE achieves better results than previous state-of-the-art link prediction methods. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Link prediction Entity kernel Relation kernel Multi-attention neural network knowledge graph embedding

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Rotat3D: A knowledge graph embedding using Relational Rotation in 3D Vector Space 12

Rotat3D: A Knowledge Graph Embedding using Relational Rotati...

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12th International Conference on knowledge and Systems Engineering (KSE)

作者： Quan Dang An Mai Man Ngo Thanh Bui Vietnam Natl Univ John von Neumann Inst Ho Chi Minh City Vietnam Vietnam Natl Univ Int Univ Sch CSE Ho Chi Minh City Vietnam Vietnam Natl Univ Univ Sci Dept Math & CS Ho Chi Minh City Vietnam

ISBN: (数字)9781728145105

ISBN: (纸本)9781728145105

To extract the entities and understand the relations in knowledge graphs is probably one of the challenging research focuses recently in machine learning community. In this paper, we aim to study one of the narrow-down problem of learning the embedding of entities and relations in knowledge graphs. From using a 3D rotation transformation in high dimensional vector spaces, we present a new method for knowledge graph embedding named Rotat3D. More specifically, the 3D-valued embeddings will be used to represent for the entities in the graphs, in which the rotations are modeled as popular rotation formulation in 3D vector spaces. Experimental results, carried out on four common benchmark datasets for link prediction, have shown that our proposed Rotat3D method is able to infer the common relation patterns in a graph more easily, and also has a critical improvement compared with some other state-of-the-art methods.

关键词： knowledge graph embedding Rodrigues' relational rotation formula self-adversarial negative sampling RotatE

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Pykg2vec: a Python library for knowledge graph embedding

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 754-759页

作者： Shih-Yuan Yu Sujit Rokka Chhetri Arquimedes Canedo Palash Goyal Mohammad Abdullah Al Faruque Department of Electrical Engineering and Computer Science University of California-Irvine Siemens Corporate Technology Princeton Department of Computer Science University of Southern California

Pykg2vec is a Python library for learning the representations of the entities and relations in knowledge graphs. Pykg2vec's exible and modular software architecture currently implements 25 state-of-the-art knowledge graph embedding algorithms, and is designed to easily incorporate new algorithms. The goal of pykg2vec is to provide a practical and educational platform to accelerate research in knowledge graph representation learning. Pykg2vec is built on top of PyTorch and Python's multiprocessing framework and provides modules for batch generation, Bayesian hyperparameter optimization, evaluation of KGE tasks, embedding, and result visualization. Pykg2vec is released under the MIT License and is also available in the Python Package Index (PyPI).

关键词： knowledge graph embedding representation learning

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Beyond Triplets: Hyper-Relational knowledge graph embedding for Link Prediction 20

Beyond Triplets: Hyper-Relational Knowledge Graph Embedding ...

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29th World Wide Web Conference (WWW)

作者： Rosso, Paolo Yang, Dingqi Cudre-Mauroux, Philippe Univ Fribourg Fribourg Switzerland Univ Macau Zhuhai Peoples R China

ISBN: (纸本)9781450370233

knowledge graph (KG) embeddings are a powerful tool for predicting missing links in KGs. Existing techniques typically represent a KG as a set of triplets, where each triplet (h, r, t) links two entities h and t through a relation r, and learn entity/relation embeddings from such triplets while preserving such a structure. However, this triplet representation oversimplifies the complex nature of the data stored in the KG, in particular for hyper-relational facts, where each fact contains not only a base triplet (h, r, t), but also the associated key-value pairs (k, v). Even though a few recent techniques tried to learn from such data by transforming a hyper-relational fact into an n-ary representation (i.e., a set of key-value pairs only without triplets), they result in suboptimal models as they are unaware of the triplet structure, which serves as the fundamental data structure in modern KGs and preserves the essential information for link prediction. To address this issue, we propose HINGE, a hyper-relational KG embedding model, which directly learns from hyper-relational facts in a KG. HINGE captures not only the primary structural information of the KG encoded in the triplets, but also the correlation between each triplet and its associated key-value pairs. Our extensive evaluation shows the superiority of HINGE on various link prediction tasks over KGs. In particular, HINGE consistently outperforms not only the KG embedding methods learning from triplets only (by 0.81 41.45% depending on the link prediction tasks and settings), but also the methods learning from hyper-relational facts using the n-ary representation (by 13.2 84.1%).

关键词： knowledge graph embedding Hyper-relation Link prediction

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Attention-Based Direct Interaction Model for knowledge graph embedding 1

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9th Joint International Semantic Technology Conference (JIST)

作者： Zhou, Bo Chen, Yubo Liu, Kang Zhao, Jun Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (数字)9789811534126

ISBN: (纸本)9789811534126;9789811534119

knowledge graph embedding aims at learning low-dimensional representations for entities and relations in knowledge graph. Previous knowledge graph embedding methods usually assign a score to each triple in order to measure the plausibility of it. Despite of the effectiveness of these models, they ignore the fine-grained (matching signals between entities and relations) clues since their scores are mainly obtained by manipulating the triple as a whole. To address this problem, we instead propose a model which firstly produces diverse features of entity and relation by multi-head attention and then introduces the interaction mechanism to incorporate matching signals between entities and relations. Experiments show that our model achieves better link prediction performance than multiple strong baselines on two benchmark datasets WN18RR and FB15k-237.

关键词： knowledge graph embedding Link prediction Attention-based Direct interaction

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TransP: A New knowledge graph embedding Model by Translating on Positions 11

TransP: A New Knowledge Graph Embedding Model by Translating...

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11th IEEE International Conference on knowledge graph (IEEE ICKG)

作者： Ren, Feiliang Li, Jucheng Zhang, Huihui Yang, Xiaochun Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781728181561

embedding knowledge graph into continuous space(s) is attracting more and more research attention, and lots of novel methods have been proposed. Among them, translation based methods achieved state-of-the-art experimental results. However, most of existing work ignore following two facts. First, once a relation is fixed, its linked head and tail entities will be fixed to a certain extent. Second, in a triplet, if one of its entities and the relation are fixed, the other entity's candidates will also be fixed to a certain extent. Taking these two facts into consideration, we propose a new knowledge graph embedding model named TransP, which defines a head entity space and a tail entity space for each relation. During embedding, TransP first projects entities into these two position spaces. Then the entities in these two position spaces are further projected into a common transformation space, in which the relation is converted into two transformation matrices. A symmetrical score function is designed to connect a correct triplet's head and tail entity in the common space. The basic idea behind this score function is that if a correct triplet holds, its head (tail) entity should be able to be converted into its tail (head) entity when taking the relation's transformation matrix as an intermediate bridge. Viewing the transformation matrices as decoders, this process is just like a common translation process. We evaluate TransP on triplet classification task and link prediction task. Extensive experiments show that TransP achieves much better performance than other baseline models.

关键词： knowledge graph embedding translation based KGE triplet classification link prediction

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knowledge graph embedding Based Question Answering 19

Knowledge Graph Embedding Based Question Answering

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12th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Huang, Xiao Zhang, Jingyuan Li, Dingcheng Li, Ping Baidu Res Cognit Comp Lab Sunnyvale CA 94089 USA

ISBN: (纸本)9781450359405

Question answering over knowledge graph (QA-KG) aims to use facts in the knowledge graph (KG) to answer natural language questions. It helps end users more efficiently and more easily access the substantial and valuable knowledge in the KG, without knowing its data structures. QA-KG is a nontrivial problem since capturing the semantic meaning of natural language is difficult for a machine. Meanwhile, many knowledge graph embedding methods have been proposed. The key idea is to represent each predicate/entity as a low-dimensional vector, such that the relation information in the KG could be preserved. The learned vectors could benefit various applications such as KG completion and recommender systems. In this paper, we explore to use them to handle the QA-KG problem. However, this remains a challenging task since a predicate could be expressed in different ways in natural language questions. Also, the ambiguity of entity names and partial names makes the number of possible answers large. To bridge the gap, we propose an effective knowledge embedding based Question Answering (KEQA) framework. We focus on answering the most common types of questions, i.e., simple questions, in which each question could be answered by the machine straightforwardly if its single head entity and single predicate are correctly identified. To answer a simple question, instead of inferring its head entity and predicate directly, KEQA targets at jointly recovering the question's head entity, predicate, and tail entity representations in the KG embedding spaces. Based on a carefully-designed joint distance metric, the three learned vectors' closest fact in the KG is returned as the answer. Experiments on a widely-adopted benchmark demonstrate that the proposed KEQA outperforms the state-of-the-art QA-KG methods.

关键词： Question answering knowledge graph embedding deep learning

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