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10th International Joint Conference on knowledge graphs (IJCKG)

作者： Zhang, Long Wu, Tianxing Chen, Xiuqi Lu, Bingjie Na, Chongning Qi, Guilin Zhejiang Lab Hangzhou Peoples R China Southeast Univ Nanjing Peoples R China

ISBN: (纸本)9781450395656

In recent years, feature engineering based machine learning models have made great progress in auto insurance fraud detection. However, their performance on single fraud case detection has never reached to a high level, and they cannot detect gang frauds. One of the main causes is that such machine learning models directly neglect the associations between auto insurance cases. To resolve this problem, we propose to leverage knowledge graph techniques to discover associations between cases for fraud detection. We first construct an auto insurance knowledge graph (AIKG) with ontology building and knowledge extraction from relational database. We then apply AIKG to both gang fraud detection and single fraud case detection. We finally conduct comprehensive experiments on fraud detection, and our methods significantly outperform stateof-the-art baselines in different evaluation metrics. Our built auto insurance ontology which is the core part of AIKG has been published on the Web and can be open access.

关键词： knowledge graph Ontology Fraud Detection knowledge graph embedding

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PRRL: Path Rotation based knowledge graph Representation Learning method 8

PRRL: Path Rotation based Knowledge Graph Representation Lea...

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8th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Bai, Changhao Wu, Peng Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781450391641

knowledge graph (KG) representation learning aims at embedding triples in the form of vectors. Their semantic similarity can be expressed through the distance of those vectors, and thus easily be computed for further application, such as knowledge completion. Early KG embedding methods, such as the TransE model, were mainly training with relationship of each individual triple, and ignore the relationships between multiple triples. Thus their representation result is subject to the integrity of the triples. Recently, some path-enhanced methods, such as PTransE and RPJE, adopt the path information composed of multiple triples as supplement relation information for training, which achieves better effect than those triples based methods. On the other hand, the performance of path-enhanced methods is still affected by the fact that they are hard to learn the symmetric relation pattern in both triples and paths. Thus we propose Path Rotation based KG Representation Learning method (PRRL), which maps entities and relations into complex vector space and defines both relations and paths (composed by sequence of relations) as a rotation from source entity to target entity. PRRL can model and infer a variety of relation patterns, including symmetry/antisymmetry, inversion and composition relations by representing the path with Hadamard product of relations. The results of experiment on multiple datasets show that PRRL is better than the baseline in the completion of the task of KG.

关键词： knowledge graph embedding path rotation deep learning

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Bridging Text Space and knowledge Space via Transference Methods 33

Bridging Text Space and Knowledge Space via Transference Met...

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IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Liu, Ming Wang, Bo Gao, Qiang Zhang, Li Lin, Xingchen Lang, Bo Beihang Univ Natl Comp Network Emergency Response Tech Team Coordinat Ctr China Beijing Peoples R China Beijing Informat Sci & Technol Univ Sch Publ Adm & Commun Beijing Peoples R China Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China

ISBN: (纸本)9781665408981

Introducing the words of texts, entities, and relations of a knowledge graph (KG) into the same semantic space has great significance in KG complement and knowledge computing. Current methods mainly utilize the "alignment constraint" of words and entities to construct uniform objective functions. However, the "alignment constraint" limits the joint representation space to specific KGs and texts. Meanwhile, the representation effect still suffers from the scale of the "alignment constraint". This paper propose a novel transference framework, the method firstly learns the text representation space and KG representation space independently, and then transfers the word representation in the text space to the knowledge space with projection models, and finally constructs a joint representation space. Our approach can decrease the dependency on"alignment constraint", and allow two spaces to be optimized and extended independently. Hence, it has better flexibility, general applicability and helps improve the capability of the joint representation space. Further more, to enhance the word transference performance, we incorporate the relation constraint into the mapping models. To the best of our knowledge, this is the first study using transference method to construct the joint semantic space. The experimental results show that linear mapping models are more suitable than nonlinear models during the projection process. The results of word analogy and relation extraction tasks illustrate the effectiveness of our method compared with state-of-the-art methods.

关键词： knowledge graph embedding word embedding joint representation cross space mapping

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AliCoCo2: Commonsense knowledge Extraction, Representation and Application in E-commerce 21

<i>AliCoCo2</i>: Commonsense Knowledge Extraction, Represent...

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

作者： Luo, Xusheng Bo, Le Wu, Jinhang Li, Lin Luo, Zhiy Yang, Yonghua Yang, Keping Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781450383325

Commonsense knowledge used by humans while doing online shopping is valuable but difficult to be captured by existing systems running on e-commerce platforms. While construction of commonsense knowledge graphs in e-commerce is non-trivial, representation learning upon such graphs poses unique challenge compared to well-studied open-domain knowledge graphs (e.g., Freebase). By leveraging the commonsense knowledge and representation techniques, various applications in e-commerce can be benefited. Based on AliCoCo[16], the large-scale e-commerce concept net assisting a series of core businesses in Alibaba, we further enrich it with more commonsense relations and present AliCoCo2, the first commonsense knowledge graph constructed for e-commerce use. We propose a multi-task encoder-decoder framework to provide effective representations for nodes and edges from AliCoCo2. To explore the possibility of improving e-commerce businesses with commonsense knowledge, we apply newly mined commonsense relations and learned embeddings to e-commerce search engine and recommendation system in different ways. Experimental results demonstrate that our proposed representation learning method achieves state-of-the-art performance on the task of knowledge graph completion (KGC), and applications on search and recommendation indicate great potential value of the construction and use of commonsense knowledge graph in e-commerce. Besides, we propose an e-commerce QA task with a new benchmark during the construction of AliCoCo2, for testing machine common sense in e-commerce, which can benefit research community in exploring commonsense reasoning.

关键词： Common Sense knowledge graph embedding E-commerce

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A shallow neural model for relation prediction 15

A shallow neural model for relation prediction

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15th IEEE International Conference on Semantic Computing (ICSC)

作者： Demir, Caglar Moussallem, Diego Ngomo, Axel-Cyrille Ngonga Paderborn Univ Data Sci Grp North Rhine Westphalia Germany

ISBN: (纸本)9781728188997

knowledge graph completion refers to predicting missing triples. Most approaches achieve this goal by predicting entities, given an entity and a relation. We predict missing triples via the relation prediction. To this end, we frame the relation prediction problem as a multi-label classification problem and propose a shallow neural model (SHALLOM) that accurately infers missing relations from entities. SHALLOM is analogous to C-BOW as both approaches predict a central token (p) given surrounding tokens ((s, o)). Our experiments indicate that SHALLOM outperforms state-of-the-art approaches on the FB15K-237 and WN18RR with margins of up to 3% and 8% (absolute), respectively, while requiring a maximum training time of 8 minutes on these datasets. We ensure the reproducibility of our results by providing an open-source implementation including training and evaluation scripts at https://***/dice-group/Shallom.

关键词： knowledge graph embedding Relation prediction Shallow neural network

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Generative adversarial networks based on Wasserstein distance for knowledge graph embeddings

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knowledge-BASED SYSTEMS 2020年 190卷 105165-105165页

作者： Dai, Yuanfei Wang, Shiping Chen, Xing Xu, Chaoyang Guo, Wenzhong Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Peoples R China Fuzhou Univ Key Lab Network Comp & Intelligent Informat Proc Fuzhou 350116 Peoples R China Putian Univ Sch Informat Engn Putian 351100 Peoples R China

knowledge graph embedding aims to project entities and relations into low-dimensional and continuous semantic feature spaces, which has captured more attention in recent years. Most of the existing models roughly construct negative samples via a uniformly random mode, by which these corrupted samples are practically trivial for training the embedding model. Inspired by generative adversarial networks (GANs), the generator can be employed to sample more plausible negative triplets, that boosts the discriminator to improve its embedding performance further. However, vanishing gradient on discrete data is an inherent problem in traditional GANs. In this paper, we propose a generative adversarial network based knowledge graph representation learning model by introducing the Wasserstein distance to replace traditional divergence for settling this issue. Moreover, the additional weak supervision information is also absorbed to refine the performance of embedding model since these textual information contains detailed semantic description and offers abundant semantic relevance. In the experiments, we evaluate our method on the tasks of link prediction and triplet classification. The experimental results indicate that the Wasserstein distance is capable of solving the problem of vanishing gradient on discrete data and accelerating the convergence, additional weak supervision information also can significantly improve the performance of the model. (C) 2019 Published by Elsevier B.V.

关键词： knowledge graph embedding Generative adversarial networks Wasserstein distance Weak supervision information

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FedE: embedding knowledge graphs in Federated Setting 10

FedE: Embedding Knowledge Graphs in Federated Setting

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10th International Joint Conference on knowledge graphs (IJCKG)

作者： Chen, Mingyang Zhang, Wen Yuan, Zonggang Jia, Yantao Chen, Huajun Zhejiang Univ Hangzhou Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China AZFT Joint Lab Knowledge Engine Hangzhou Peoples R China

ISBN: (纸本)9781450395656

knowledge graphs (KGs) become widespread and many organizations construct as well as maintain their own knowledge graphs. Same as the data isolation which has been a long-standing problem, knowledge graph isolation is common in real knowledge graph applications. Since the incompleteness of knowledge graphs obtained by different owners, they need to take advantage of other knowledge graphs to complete their own knowledge graphs, without exposing knowledge graphs explicitly since the consideration of data privacy, commercial interests and so on. knowledge graph embedding (KGE) methods represent components of a knowledge graph as vectors in continuous vector spaces (i.e., embeddings) and proved to be effective in conducting knowledge graph completion. However, current knowledge graph embedding methods focus on the scenario with only a single knowledge graph. To solve this problem, we introduce the federated setting for knowledge graphs and apply it in knowledge graph embedding. We propose a Federated knowledge graph embedding framework, FedE, focusing on learning knowledge graph embeddings by aggregating locallycomputed updates. In this framework, there is a client for each knowledge graph and a server for coordinating embedding aggregation. Specifically, entity embeddings are locally learned in clients and the server is responsible for aggregating entity embeddings from clients. Furthermore, a model fusion procedure blends the capability of learned embeddings based only on one client without using the federated setting and embeddings based on all the clients in the federated setting. Finally, we conduct extensive experiments on datasets derived from KGE benchmark datasets, and results show the effectiveness of our proposed FedE.

关键词： knowledge graph knowledge graph embedding Representation Learning Federated Learning

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Meta-Learning Based Hyper-Relation Feature Modeling for Out-of-knowledge-Base embedding 21

Meta-Learning Based Hyper-Relation Feature Modeling for Out-...

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30th ACM International Conference on Information and knowledge Management (CIKM)

作者： Zhang, Yufeng Wang, Weiqing Chen, Wei Xu, Jiajie Liu, An Zhao, Lei Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Monash Univ Dept Data Sci & AI Melbourne Australia

ISBN: (纸本)9781450384469

knowledge graph (KG) embedding aims to encode both entities and relations into a continuous vector space. Most existing methods require that all entities should be observed during training while ignoring the evolving nature of KG. Major recent efforts on this issue embed new entities by aggregating neighborhood information from existing entities and relations with graph Neural Network (GNN). However, these methods rely on the neighbors seen during training and suffer from the embedding of new entities with insufficient triplets or triplets with the unseen-to-unseen form. To relieve this problem, we propose a two-stage learning model referred as Hyper-Relation Feature Learning Network (HRFN) for effective out-of-knowledge-base embedding. For the first stage, HRFN learns pre-representations for emerging entities using hyper-relation features meta-learned from the training set. A novel feature aggregating network that involves an entity-centered graph Convolutional Network (GCN) and a relation-centered GCN is proposed to aggregate information from both new entities themselves and their neighbors. For stage two, a transductive learning network is employed to learn finer-grained embeddings based on above-mentioned pre-representations of new entities. Experimental results on the link prediction task demonstrate the superiority of our model. Further analysis is also done to validate the effectiveness and efficiency of pre-representing emerging entities with the hyper-relation feature.

关键词： knowledge graph embedding out-of-knowledge-base entities representation learning hyper-relation feature

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MQuadE: a Unified Model for knowledge Fact embedding 21

MQuadE: a Unified Model for Knowledge Fact Embedding

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30th World Wide Web Conference (WWW)

作者： Yu, Jinxing Cai, Yunfeng Sun, Mingming Li, Ping Baidu Res 10 Xibeiwang East Rd Beijing 100193 Peoples R China Baidu Res Cognit Comp Lab 10900 NE 8th St Bellevue WA 98004 USA

ISBN: (纸本)9781450383127

The task of knowledge graph embedding (KGE) tries to find appropriate representations for entities and relations and appropriate mathematical computations between the representations to approximate the symbolic and logical relationships between entities. One major challenge for KGE is that the relations in real-world knowledge bases exhibit complex behaviors: they can be injective (1-1) or non-injective (1-N, N-1, or N-N), symmetry or skew-symmetry;one relation may be the inversion of another relation;one relation may be the composition of other two relations (where the composition can be either Abelian or non-Abelian). To our knowledge, there has not been any theoretical guarantee that these complex behaviors can be modeled by existing KGE methods. This paper proposes a method called MQuadE to tackle the challenge in KGE modeling. In MQuadE, we represent a fact triple (h, r, t), that is, (head entity, relation, tail entity), in the knowledge graph with a matrix quadruple (H, R, (R) over cap, T), where H and T are the representations of h and t respectively and < R, <(R)over cap> > is the pair of representation of r. MQuadE projects the head entity into HR and the tail entity into (R) over capT, then assumes that HR and (R) over capT are similar for true facts and dissimilar for false facts. We prove that MQuadE, as a unified model for KGE, is able to model the generally concerned types of relations (symmetric, skew-symmetric, injective, non-injective, inversion, Abelian composition, non-Abelian composition). Experiments on link prediction and triple classification show that MQuadE outperforms many previous knowledge graph embedding methods, especially on 1-N, N-1, and N-N relations.

关键词： knowledge graph embedding link prediction triple classification

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Mixed-Curvature Multi-Relational graph Neural Network for knowledge graph Completion 21

Mixed-Curvature Multi-Relational Graph Neural Network for Kn...

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30th World Wide Web Conference (WWW)

作者： Wang, Shen Wei, Xiaokai dos Santos, Cicero Nogueira Wang, Zhiguo Nallapati, Ramesh Arnold, Andrew Xiang, Bing Yu, Philip S. Cruz, Isabel F. Univ Illinois Chicago IL 60607 USA AWS AI Seattle WA USA

ISBN: (纸本)9781450383127

knowledge graphs (KGs) have gradually become valuable assets for many AI applications. In a KG, a node denotes an entity, and an edge (or link) denotes a relationship between the entities represented by the nodes. knowledge graph completion infers and predicts missing edges in a KG automatically. knowledge graph embeddings have shed light on addressing this task. Recent research embeds KGs in hyperbolic (negatively curved) space instead of conventional Euclidean (zero curved) space and is effective in capturing hierarchical structures. However, as multi-relational graphs, KGs are not structured uniformly and display intrinsic heterogeneous structures. They usually contain rich types of structures, such as hierarchical and cyclic typed structures. embedding KGs in single-curvature space, such as Euclidean or hyperbolic space, overlooks the intrinsic heterogeneous structures of KGs, and therefore cannot accurately capture their structures. To address this issue, we propose Mixed-Curvature Multi-Relational graph Neural Network (M2GNN), a generic approach that embeds multi-relational KGs in a mixed-curvature space for knowledge graph completion. Specifically, we define and construct a mixed-curvature space through a product manifold combining multiple single-curvature spaces (e.g., spherical, hyperbolic, or Euclidean) with the purpose of modeling a variety of structures. However, constructing a mixed-curvature space typically requires manually defining the fixed curvatures, which needs domain knowledge and additional data analysis. Improperly defined curvature space also cannot capture the structures of KGs accurately. To address this problem, we set mixed-curvatures as trainable parameters to better capture the underlying structures of the KGs. Furthermore, we propose a graph Neural Updater by leveraging the heterogeneous relational context in mixed-curvature space to improve the quality of the embedding. Experiments on three KG datasets demonstrate that the proposed M

关键词： knowledge graph Completion knowledge graph embedding Multi-relational graph graph Neural Network Non-Euclidean Space embedding

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