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检索条件"主题词=Knowledge Graph embedding"

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Veni, Vidi, Vici: Solving the Myriad of Challenges before knowledge graph Learning 18

Veni, Vidi, Vici: Solving the Myriad of Challenges before Kn...

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18th IEEE International Conference on Semantic Computing (ICSC)

作者： Sardina, Jeffrey Costabello, Luca Gueret, Christophe Trinity Coll Dublin Sch Comp Sci & Stat Dublin Ireland Accenture Labs Dublin Ireland Accenture BioInnovat Labs Dublin Ireland

ISBN: (纸本)9798350385359

knowledge graphs (KGs) have become increasingly common for representing large-scale linked data. However, their immense size has required graph learning systems to assist humans in analysis, interpretation, and pattern detection. While there have been promising results for researcher- and clinician-empowerment through a variety of KG learning systems, we identify four key deficiencies in state-of-the-art graph learning that simultaneously limit KG learning performance and diminish the ability of humans to interface optimally with these learning systems. These deficiencies are: 1) lack of expert knowledge integration, 2) instability to node degree extremity in the KG, 3) lack of consideration for uncertainty and relevance while learning, and 4) lack of explainability. Furthermore, we characterise state-of-the-art attempts to solve each of these problems and note that each attempt has largely been isolated from attempts to solve the other problems. Through a formalisation of these problems and a review of the literature that addresses them, we adopt the position that not only are deficiencies in these four key areas holding back human-KG empowerment, but that the divide-and-conquer approach to solving these problems as individual units rather than a whole is a significant barrier to the interface between humans and KG learning systems. We propose that it is only through integrated, holistic solutions to the limitations of KG learning systems that human and KG learning co-empowerment will be efficiently affected. We finally present our "Veni, Vidi, Vici" framework that sets a roadmap for effectively and efficiently shifting to a holistic co-empowerment model in both the KG learning and the broader machine learning domain.

关键词： knowledge graphs knowledge graph embedding Relational Learning Neuro-Symbolic Learning GNNs

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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...

引用

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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An Adaptive embedding Framework for Heterogeneous Information Networks 20

An Adaptive Embedding Framework for Heterogeneous Informatio...

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29th ACM International Conference on Information and knowledge Management (CIKM)

作者： Chen, Daoyuan Li, Yaliang Ding, Bolin Shen, Ying Alibaba Grp Hangzhou Zhejiang Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781450368599

Heterogeneous information networks (HINs) have been ubiquitous in the real-world. HIN embeddings, which encode various information of the networks into low-dimensional vectors, can facilitate a wide range of applications on graph-structured data. Existing HIN embedding methods include random walk based methods that may not fully utilize the edge semantics and knowledge graph embedding methods that restrict the expression ability of topological information. In this paper, we propose a novel adaptive embedding framework, which integrates these two kinds of methods to preserve both topological information and relational information. By incorporating an assistant knowledge graph embedding model, the proposed framework performs efficient biased random walk under the guidance of edge semantics. Meanwhile, the short and long dependency information can be adaptively preserved for diverse networks. Furthermore, a patient joint training strategy is proposed to make the framework flexible and adapt to different assistant knowledge graph embedding models. Extensive experiments on five real-world datasets demonstrate that the proposed method can adaptively sample node contexts and outperforms several state-of-the-art methods for node classification and link prediction tasks.

关键词： Heterogeneous Network embedding knowledge graph embedding Network Representation Learning

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A Survey on knowledge graph-Based Methods for Automated Driving 1

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4th Iberoamerican Conference and 3rd Indo-American Conference knowledge graphs and Semantic Web Conference (KGSWC)

作者： Luettin, Juergen Monka, Sebastian Henson, Cory Halilaj, Lavdim Bosch Ctr AI Renningen Germany Bosch Ctr AI Pittsburgh PA USA

ISBN: (数字)9783031214226

ISBN: (纸本)9783031214219;9783031214226

Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD) in particular. However, there are still unsolved problems to guarantee reliability and safety of automated systems, especially to effectively incorporate all available information and knowledge in the driving task. knowledge graphs (KG) have recently gained significant attention from both industry and academia for applications that benefit by exploiting structured, dynamic, and relational data. The complexity of graph-structured data with complex relationships and inter-dependencies between objects has posed significant challenges to existing machine learning algorithms. However, recent progress in knowledge graph embeddings and graph neural networks allows to applying machine learning to graph-structured data. Therefore, we motivate and discuss the benefit of KGs applied to AD. Then, we survey, analyze and categorize ontologies and KG-based approaches for AD. We discuss current research challenges and propose promising future research directions for KG-based solutions for AD.

关键词： knowledge graph Automated driving Automotive ontology knowledge representation learning knowledge graph embedding knowledge graph neural network

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TOWARDS GEOSPATIAL knowledge graph INFUSED NEURO-SYMBOLIC AI FOR REMOTE SENSING SCENE UNDERSTANDING

TOWARDS GEOSPATIAL KNOWLEDGE GRAPH INFUSED NEURO-SYMBOLIC AI...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Potnis, Abhishek Lunga, Dalton Sorokine, Alexandre Dias, Philipe Yang, Lexie Arndt, Jacob Bowman, Jordan Wohlgemuth, Jason Oak Ridge Natl Lab Geospatial Sci & Human Secur Div Oak Ridge TN 37830 USA

ISBN: (纸本)9798350320107

Deep learning has proven its effectiveness in numerous tasks for remote sensing scene understanding. However there is an increasing interest to explore fusion of domain-specific background information to the deep neural network to further improve its performance. Remote sensing researchers are also working towards developing models that generalize and adapt to multiple applications. Generalization challenges coupled with the scarcity of large corpora of high-quality noise-free labelled data, have together fueled an interest for leveraging background information. knowledge graphs serve as excellent choice to represent domain-specific information in a structured, standardized and extensible manner. Integrating symbolic knowledge representations in the form of knowledge graph embedding (KGE) to perform neuro-symbolic reasoning is an emerging research direction promising significant impacts. This vision paper seeks to position ideas and provoke early thoughts toward advancing neuro-symbolic artificial intelligence in the context of geospatial challenges. Specifically, it conceptualizes and elaborates on an architecture for infusing geospatial knowledge from knowledge graph in a deep neural network pipeline. As guiding case studies - land-use land-cover classification, object detection and instance segmentation can benefit from infusing spatio-contextual information with remote sensing imagery. The discussion further reflects on and articulates the challenges and explainable AI opportunities anticipated when scaling and maintaining large-scale geospatial knowledge graphs.

关键词： geospatial knowledge graphs neuro-symbolic knowledge-infused learning remote sensing scene understanding deep learning representation learning knowledge graph embedding

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Enhancing Query Answer Completeness with Query Expansion based on Synonym Predicates 22

Enhancing Query Answer Completeness with Query Expansion bas...

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31st ACM Web Conference (WWW)

作者： Niazmand, Emetis Leibniz Univ Hannover TIB Leibniz Informat Ctr Sci & Technol Hannover Germany

ISBN: (纸本)9781450391306

Community-based knowledge graphs are generated following hybrid approaches, where human intelligence empowers computational methods to effectively integrate encyclopedic knowledge or provide a common understanding of a domain. Existing community-based knowledge graphs represent essential sources of knowledge for enhancing the accuracy of data mining, information retrieval, question answering, and multimodal processing. However, despite the enormous effort conducted by contributing communities, community-based knowledge graphs may be incomplete and integrate duplicated data and metadata. We tackle the problem of enhancing query answering against incomplete community-based knowledge graphs by proposing an efficient query processing approach to estimate answer completeness and increase the results. It assumes that community-based knowledge graphs comprise synonym predicates that complement knowledge graph triples required to raise query answering completeness. The aim is proposing a novel query expansion method based on synonym predicates identified using embeddings built on a knowledge graph. Our preliminary analysis shows that our approach improves query answer completeness. However, queries can be expanded with some similar predicates which do not lead to complete answers. This shows that more work is required for query expansion with the minimum synonym predicates that maximize answer completeness.

关键词： knowledge graph Synonym Detection knowledge graph embedding Query Expansion Query Processing

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AsyncET: Asynchronous Representation Learning for knowledge graph Entity Typing 24

AsyncET: Asynchronous Representation Learning for Knowledge ...

引用

30th ACM SIGKDD Conference on knowledge Discovery and Data Mining

作者： Wang, Yun-Cheng Ge, Xiou Wang, Bin Kuo, C. -C. Jay Univ Southern Calif Los Angeles CA 90007 USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798400704901

knowledge graph entity typing (KGET) aims to predict the missing entity types in knowledge graphs (KG). The relationship between entities and their corresponding types is often expressed using a single relation, hasType. However, hasType has a limited capability for modeling diverse entity-type relationships in the embedding space. In this paper, we first introduce multiple auxiliary relations to model the complex entity-type relationship. We propose an efficient and robust algorithm to group similar entity types together and assign a unique auxiliary relation to each group. Then, with the auxiliary relations, we propose an Asynchronous representation learning framework for KGET, named AsyncET, where entity and type embeddings are updated alternatively. Consequently, the quality of entity embeddings is gradually improved during training by infusing type information. In addition, entity types with different granularities and semantics can be properly modeled in the embedding space. Experimental results show that AsyncET can substantially improve the performance of embedding-based methods on the KGET task and has a significant advantage over state-of-the-art neural network-based methods in terms of model sizes and inference time.

关键词： knowledge graph embedding knowledge graph Entity Typing Representation Learning Taxonomy

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An Ontology-Based Deep Learning Approach for knowledge graph Completion with Fresh Entities 16th

An Ontology-Based Deep Learning Approach for Knowledge Graph...

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16th International Symposium on Distributed Computing and Artificial Intelligence (DCAI)

作者： Amador-Dominguez, Elvira Hohenecker, Patrick Lukasiewicz, Thomas Manrique, Daniel Serrano, Emilio Univ Politecn Madrid Dept Artificial Intelligence Madrid Spain Univ Oxford Dept Comp Sci Oxford England

ISBN: (纸本)9783030238872;9783030238865

This paper introduces a new initialization method for knowledge graph (KG) embedding that can leverage ontological information in knowledge graph completion problems, such as link classification and link prediction. Although the initialization method is general and applicable to different KG embedding approaches in the literature, such as TransE or RESCAL, this paper experiments with deep learning and specifically with the neural tensor network (NTN) model. The experimental results show that the proposed method can improve link classification for a given relation by up to 15%. In a second contribution, the proposed method allows for addressing a problem not studied in the literature and introduced here as "KG completion with fresh entities". This is the use of KG embeddings for KG completion when one or several of the entities in a triple (head, relation, tail) has not been observed in the training phase.

关键词： Statistical relational learning Ontological knowledge base knowledge graph embedding Latent feature model

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HIAE: Hyper-Relational Interaction Aware embedding for Link Prediction 34

HIAE: Hyper-Relational Interaction Aware Embedding for Link ...

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34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Li, Lijie Yuan, Peikai Wang, Ye Li, Jiahang Harbin Engn Univ Coll Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9798350397444

Hyper-relational knowledge graph contains the main triple and additional information(qualifiers). The additional information can assist the main triple to predict missing entities(relations). In the previous methods, people usually combine the main triple and additional information through special methods to predict the missing entities(relations). However, not all additional information is equally essential for predicting missing entities(relations). Some additional information plays a more critical role in predicting missing entities (relations), so how to extract and use important additional information effectively in the link prediction (LP) task is very important. This paper proposes a novel Hyper-relational Interaction Aware embedding (HIAE) model. Specifically, HIAE learns semantic features between the additional information and fuses the important additional information into the main triple through the attention mechanism to complete the prediction task. We have conducted many experiments to show that HIAE significantly outperforms the state-of-the-art models.

关键词： knowledge graph embedding hyper-relation link prediction neural network

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KEEN: knowledge graph-Enabled Governance System for Biological Assets 1

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17th International Conference on knowledge Science, Engineering and Management (KSEM)

作者： Fang, Zhengkang Gai, Keke Yu, Jing Wei, Yihang Wei, Zhentao Chan, Weilin Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing Peoples R China Beijing Muguo Technol Co Ltd Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

ISBN: (数字)9789819754984

ISBN: (纸本)9789819754977;9789819754984

With the development of the biological industry and the need for improved productivity in agriculture and animal husbandry, there are higher demands for the governance of biological assets in the biological assets governance system. In order to harness existing knowledge and enhance the effectiveness and scalability of biological asset supervision, this research proposes a knowledge graph-Enabled govErNance system for biological assets (KEEN). The system leverages textual data and employs techniques such as entity recognition, relation extraction, and knowledge graph embedding to automatically generate a knowledge graph. This knowledge graph establishes relationships between biological behaviors and target identification, thereby expanding the capabilities of behavior recognition models to perform a wider range of biological asset supervision tasks. By enhancing the scalability of biological asset identification models, better decision-making and management can be achieved. Our evaluations demonstrate that the proposed method exhibits superior performance in terms of accuracy, scalability, and maintenance in the supervision of biological assets.

关键词： knowledge graph knowledge graph embedding Biological assets

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