FB15K-237
PulseAugur coverage of FB15K-237 — every cluster mentioning FB15K-237 across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New research explores LLM-based and spectral methods for knowledge graph completion · 3 sources tracked
Three new research papers introduce novel methods for knowledge graph completion (KGC), a task focused on predicting missing links in knowledge graphs. PEARL, presented on arXiv, uses a path-entity aligned relational le…
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New model LitEm enhances knowledge graph embeddings with numerical attributes
Researchers have developed a new neural regression model called LitEm to enhance knowledge graph embeddings by incorporating numerical attributes. This model aims to improve the representation of real-world knowledge gr…
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FlowNeg method enhances knowledge graph embedding with diverse negative sampling
Researchers have developed FlowNeg, a novel method for generating diverse and informative negative samples in knowledge graph embedding (KGE) models. This approach utilizes a context-conditioned hierarchical generative …
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FlowNeg method enhances knowledge graph embedding with diverse negative sampling
Researchers have developed FlowNeg, a novel method for generating diverse and informative negative samples in knowledge graph embedding (KGE) models. This approach utilizes a context-conditioned hierarchical generative …
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InductWave paper introduces inductive logical query answering for large knowledge graphs
Researchers have introduced InductWave, a novel wavelet-based embedding method designed for inductive multi-hop logical query answering on large knowledge graphs. Unlike transductive methods that require all entities to…
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Holographic Memory Fails Zero-Shot Compositional Reasoning in Knowledge Graphs
Researchers have investigated the effectiveness of Holographic Reduced Representations (HRR) and Fourier HRR (FHRR) for zero-shot compositional reasoning in knowledge graphs. While these methods show promise and are com…
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New framework uses LLMs to explain complex knowledge graph rules
Researchers have developed Rule2Text, a framework designed to make knowledge graph rules more understandable by using large language models to generate natural language explanations. The framework was tested on various …