[深度学习] TuckER：Tensor Factorization for Knowledge Graph Completion.pdf

说明： Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relativ
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[深度学习] Towards Data Poisoning Attack against Knowledge Graph Embedding.pdf

说明： Knowledge graph embedding (KGE) is a technique for learning continuousembeddingsfor entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommen
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[深度学习] Text Generation from Knowledge Graphs with Graph Transformers.pdf

说明： Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce.
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[深度学习] An Approach for Determining Fine-grained Relations for Wikipedia Tables.pdf

说明： Wikipedia tables represent an important resource, where informationisorganizedw.r.ttableschemasconsistingofcolumns.In turneachcolumn,maycontaininstance values thatpointtoother Wikipediaarticlesorprimitive values (e.g.numbers,stringsetc.).
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[深度学习] Soft Marginal TransE for Scholarly Knowledge Graph Completion.pdf

说明： Abstract. Knowledge graphs (KGs), i.e. representation of information as a semantic graph, provide a significant test bed for many tasks including question answering, recommendation, and link prediction. Various amount of scholarly metadata have been
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[深度学习] RotatE：Knowledge Graph Embedding by Relational Rotation in Complex Space.pdf

说明： We study the problem of learning representations of entities and relations in knowledgegraphsforpredictingmissinglinks. Thesuccessofsuchataskheavily relies on the ability of modeling and inferring the patterns of (or between) the relations. Inthispa
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[深度学习] Relation Extraction using Deep Learning approaches Knowledge Graph.pdf

说明： Abstract—Security Analysts that work in a ‘Security Operations Center’ (SoC) play a major role in ensuring the security of the organization. The amount of background knowledge they have about the evolving and new attacks makes a significant differenc
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[深度学习] Recurrent Event Network for Reasoning over Temporal Knowledge Graphs.pdf

说明： Recently, there has been a surge of interest in learning representation of graphstructured data that are dynamically evolving. However, current dynamic graph learning methods lack a principled way in modeling temporal, multi-relational, and concurre
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[深度学习] Label Efficient Semi-Supervised Learning via Graph Filtering.pdf

说明： Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning,astheycanexploittheconnectivitypatternsbetweenlabeled and unlabeled data samples to improve learning performance. However, existing graph
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[深度学习] Knowledge-Embedded Routing Network for Scene Graph Generation.pdf

说明： To understand a scene in depth not only involves locating/recognizing individual objects, but also requires to infer the relationships and interactions among them. However, sincethedistributionofreal-worldrelationshipsisseriously unbalanced, existin
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[深度学习] Knowledge-driven Encode, Retrieve, Paraphrase for MedicalImageReport.pdf

说明： Generating long and semantic-coherent reports to describe medical images poses great challenges towards bridging visual and linguistic modalities, incorporating medical domain knowledge, and generating realistic and accurate descr iptions. We propos
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[深度学习] Knowledge Representation Learning：A Quantitative Review.pdf

说明： Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks. In this article, we introduce the reader to the mot
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