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Knowledge graph Alignment with Entity-Pair Embedding

Knowledge Graph Alignment with Entity-Pair Embedding

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Conference on Empirical methods in natural language processing (EMNLP)

作者： Wang, Zhichun Yang, Jinjian Ye, Xiaoju Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China Minist Educ Engn Res Ctr Intelligent Technol & Educ Applicat Beijing Peoples R China

ISBN: (纸本)9781952148606

Knowledge graph (KG) alignment is to match entities in different KGs, which is important to knowledge fusion and integration. Recently, a number of embedding-based approaches for KG alignment have been proposed and achieved promising results. These approaches first embed entities in low-dimensional vector spaces, and then obtain entity alignments by computations on their vector representations. Although continuous improvements have been achieved by recent work, the performances of existing approaches are still not satisfactory. In this work, we present a new approach that directly learns embeddings of entity-pairs for KG alignment. Our approach first generates a pair-wise connectivity graph (PCG) of two KGs, whose nodes are entity-pairs and edges correspond to relation-pairs;it then learns node (entity-pair) embeddings of the PCG, which are used to predict equivalent relations of entities. To get desirable embeddings, a convolutional neural network is used to generate similarity features of entity-pairs from their attributes;and a graph neural network is employed to propagate the similarity features and get the final embeddings of entity-pairs. Experiments on five real-world datasets show that our approach can achieve the state-of-the-art KG alignment results.

关键词： Knowledge graph

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graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms

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2021年

作者： Claudio Stamile Aldo Marzullo Enrico Deusebio

Build machine learning algorithms using graph data and efficiently exploit topological information within your modelsKey FeaturesImplement machine learning techniques and algorithms in graph dataIdentify the relations...

ISBN: (数字)9781800206755

Build machine learning algorithms using graph data and efficiently exploit topological information within your models

Key Features

Implement machine learning techniques and algorithms in graph data
Identify the relationship between nodes in order to make better business decisions
Apply graph-based machine learning methods to solve real-life problems

Book Description

graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You’ll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You"ll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you’ll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You’ll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.

What you will learn

Write Python scripts to extract features from graphs
Distinguish between the main graph representation learning techniques
Learn how to extract data from social networks, financi

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Every Document Owns Its Structure: Inductive Text Classification via graph Neural Networks 58

Every Document Owns Its Structure: Inductive Text Classifica...

引用

58th Annual Meeting of the Association-for-Computational-Linguistics (ACL)

作者： Zhang, Yufeng Yu, Xueli Cui, Zeyu Wu, Shu Wen, Zhongzhen Wang, Liang Chinese Acad Sci Inst Automat Beijing Peoples R China Xi An Jiao Tong Univ Xian Peoples R China

ISBN: (纸本)9781952148255

Text classification is fundamental in natural language processing (NLP), and graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words. In this work, to overcome such problems, we propose TextING(1) for inductive text classification via GNN. We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document. Finally, the word nodes are incorporated as the document embedding. Extensive experiments on four benchmark datasets show that our method outperforms state-of-the-art text classification methods.

关键词： graph neural networks

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REPRESENTATION LEARNING OF REMOTE SENSING KNOWLEDGE graph FOR ZERO-SHOT REMOTE SENSING IMAGE SCENE CLASSIFICATION

REPRESENTATION LEARNING OF REMOTE SENSING KNOWLEDGE GRAPH FO...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Li, Yansheng Kong, Deyu Zhang, Yongjun Chen, Ruixian Chen, Jingdong Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Hubei Peoples R China Ant Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665403696

Although deep learning has revolutionized remote sensing image scene classification, current deep learning-based approaches highly depend on the massive supervision of the predetermined scene categories and have disappointingly poor performance on new categories which go beyond the predetermined scene categories. In reality, the classification task often has to be extended along with the emergence of new applications that inevitably involve new categories of remote sensing image scenes, so how to make the deep learning model own the inference ability to recognize the remote sensing image scenes from unseen categories becomes incredibly important. By fully exploiting the remote sensing domain characteristic, this paper proposes a novel remote sensing knowledge graph-guided deep alignment network to address zero-shot remote sensing image scene classification. To improve the semantic representation ability of remote sensing-oriented scene categories, this paper, for the first time, tries to generate the semantic representations of remote sensing scene categories by representation learning of remote sensing knowledge graph (SR-RSKG). In addition, this paper proposes a novel deep alignment network with a series of constraints (DAN) to conduct robust cross-modal alignment between visual features and semantic representations. Extensive experiments on one merged remote sensing image scene dataset, which is the integration of multiple publicly open remote sensing image scene datasets, show that the presented SR-RSKG obviously outperforms the existing semantic representation methods (e.g., the natural language processing models and manually annotated attribute vectors), and our proposed DAN shows better performance compared with the state- of-the-art methods under different kinds of semantic representations.

关键词： Deep alignment network (DAN) remote sensing knowledge graph (RSKG) remote sensing image scene classification zero-shot learning (ZSL)

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Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling

Better Combine Them Together! Integrating Syntactic Constitu...

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Joint Conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th International Joint Conference on natural language processing (IJCNLP) / 6th workshop on Representation Learning for NLP (RepL4NLP)

作者： Fei, Hao Wu, Shengqiong Ren, Yafeng Li, Fei Ji, Donghong Wuhan Univ Sch Cyber Sci & Engn Dept Key Lab Aerosp Informat Secur & Trusted Comp Minist Educ Wuhan Peoples R China Guangdong Univ Foreign Studies Lab Language & Artificial Intelligence Guangzhou Peoples R China

ISBN: (纸本)9781954085541

Structural syntax knowledge has been proven effective for semantic role labeling (SRL), while existing works mostly use only one singleton syntax, such as either syntactic dependency or constituency tree. In this paper, we explore the integration of heterogeneous syntactic representations for SRL. We first consider a TreeLSTM-based integration, collaboratively learning the phrasal boundaries from the constituency and the semantic relations from dependency. We further introduce a label-aware GCN solution for simultaneously modeling the syntactic edges and labels. Experimental results demonstrate that by effectively combining the heterogeneous syntactic representations, our methods yield task improvements on both span-based and dependency-based SRL. Also our system achieves new state-of-the-art SRL performances, meanwhile bringing explainable task improvements.

关键词： Syntactics

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AUEB-NLP at BioASQ 8: Biomedical Document and Snippet Retrieval 11th

AUEB-NLP at BioASQ 8: Biomedical Document and Snippet Retrie...

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11th Conference and Labs of the Evaluation Forum, CLEF 2020

作者： Pappas, Dimitris Stavropoulos, Petros Androutsopoulos, Ion Department of Informatics Athens University of Economics and Business Greece Institute for Language and Speech Processing Research Center ‘Athena’ Greece

We present the submissions of AUEB’s NLP group to the BIOASQ 8 document and snippet retrieval tasks. We relied mostly on JPDRMM, our top performing model of BIOASQ 7, but we also tested feeding JPDRMM with word embeddings obtained by applying a graph node embedding method to a biomedical co-occurrence graph;the latter approach was competitive to using biomedical WORD2VEC embeddings in JPDRMM. We also experimented with neural methods to encode, index, and directly retrieve snippets (sentences) and indirectly documents containing the retrieved snippets, instead of relying on conventional information retrieval to pre-fetch possibly relevant documents and invoking JPDRMM to re-rank the pre-fetched documents and their snippets;conventional BM25-based pre-fetching, however, was far better. Our JPDRMM-based document and snippet retrieval methods scored at the top or near the top for all test batches of BIOASQ 8. Copyright © 2020 for this paper by its authors.

关键词： natural language processing systems

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An Causal XAI Diagnostic Model for Breast Cancer based on Mammography Reports

An Causal XAI Diagnostic Model for Breast Cancer Based on Ma...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Chen, Dehua Zhao, Hongjin He, Jianrong Pan, Qiao Zhao, Weiliang School Computer Science and Technology Donghua University Shanghai China Comprehensive Breast Health Center Ruijin Hospital Shanghai China School Computer Science and Technology Macquarie University Sydney Australia

ISBN: (纸本)9781665401265

Breast cancer has become one of the most common malignant tumors in women worldwide, and it seriously threatens women's physical and mental health. In recent years, with the development of Artificial Intelligence(AI) and the accumulation of medical data, AI has begun to be deeply integrated with mammography, MRI, ultrasound, etc. to assist physicians in disease diagnosis. However, the existing breast cancer diagnosis model based on Computer Vision(CV) is greatly affected by the image quality;on the other hand, the breast cancer diagnosis model based on natural language processing(NLP) cannot effectively extract the semantic information of the mammography report. The lack of model interpretability also makes the existing diagnostic models have low confidence. In this paper, we proposed Breast Cancer Causal XAI Diagnostic Model(BCCXDM). Specifically, we first structured the mammography report. Then find the causal graph based on the structured table. We combine the existing tabular learning method TabNet with causal graphs(Causal-TabNet) to enable reasoning in the graphs to preserve the correlation between features. More importantly, we use GNN and node transition probability to aggregate node information. We evaluate our model on the real-world mammography report, and compare it with other popular interpretable methods. The experimental results show that our interpretable results are closer to the diagnostic criteria of clinicians. © 2021 IEEE.

关键词： graphic methods

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Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge graphs

Deep Cognitive Reasoning Network for Multi-hop Question Answ...

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作者： Cai, Jianyu Zhang, Zhanqiu Wu, Feng Wang, Jie Univ Sci & Technol China Inst Artificial Intelligence Hefei Comprehens Natl Sci Ctr Hefei Anhui Peoples R China

ISBN: (纸本)9781954085541

Knowledge graphs (KGs) provide human knowledge with nodes and edges being entities and relations among them, respectively. Multi-hop question answering over KGs-which aims to find answer entities of given questions through reasoning paths in KGs-has attracted great attention from both academia and industry recently. However, this task remains challenging, as it requires to accurately identify answers in a large candidate entity set, of which the size grows exponentially with the number of reasoning hops. To tackle this problem, we propose a novel Deep Cognitive Reasoning Network (DCRN), which is inspired by the dual process theory in cognitive science. Specifically, DCRN consists of two phases-the unconscious phase and the conscious phase. The unconscious phase first retrieves informative evidence from candidate entities by leveraging their semantic information. Then, the conscious phase accurately identifies answers by performing sequential reasoning according to the graph structure on the retrieved evidence. Experiments demonstrate that DCRN significantly outperforms state-of-the-art methods on benchmark datasets.

关键词： Knowledge graph

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Incorporating Temporal Information in Entailment graph Mining 14

Incorporating Temporal Information in Entailment Graph Minin...

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14th workshop on graph-based methods for natural language processing, Textgraphs 2020, in conjunction with the 28th International Conference on Computational Linguistics, COLING 2020

作者： Guillou, Liane De Vroe, Sander Bijl Hosseini, Mohammad Javad Johnson, Mark Steedman, Mark University of Edinburgh United Kingdom The Alan Turing Institute United Kingdom Macquarie University Australia

ISBN: (纸本)9781952148422

We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities. We focus on the sports domain in which the same pairs of teams play on different occasions, with different outcomes. We present an unsupervised model that aims to learn entailments such as win/lose → play, while avoiding the pitfall of learning non-entailments such as win 6→ lose. We evaluate our model on a manually constructed dataset, showing that incorporating time intervals and applying a temporal window around them, are effective strategies. © COLING *** rights reserved.

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Rule-Aware Reinforcement Learning for Knowledge graph Reasoning

Rule-Aware Reinforcement Learning for Knowledge Graph Reason...

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作者： Hou, Zhongni Jin, Xiaolong Li, Zixuan Bai, Long Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781954085541

Multi-hop reasoning is an effective and explainable approach to predicting missing facts in Knowledge graphs (KGs). It usually adopts the Reinforcement Learning (RL) framework and searches over the KG to find an evidential path. However, due to the large exploration space, the RL-based model struggles with the serious sparse reward problem and needs to make a lot of trials. Moreover, its exploration can be biased towards spurious paths that coincidentally lead to correct answers. To solve both problems, we propose a simple but effective RL-based method called RARL (Rule-Aware RL). It injects high quality symbolic rules into the model's reasoning process and employs partially random beam search, which can not only increase the probability of paths getting rewards, but also alleviate the impact of spurious paths. Experimental results show that it outperforms existing multi-hop methods in terms of Hit@1 and MRR.

关键词： Reinforcement learning

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