knowledge graph embedding
PulseAugur coverage of knowledge graph embedding — every cluster mentioning knowledge graph embedding across labs, papers, and developer communities, ranked by signal.
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Thesis investigates uncertainty in knowledge graph embeddings
This thesis explores uncertainty in knowledge graph embedding (KGE) methods, which represent entities and predicates in vector spaces to infer missing knowledge. It addresses three sources of uncertainty: knowledge unce…
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New framework fuses diverse techniques for improved ontology alignment
Researchers have developed OntoAligner-Ensemble, a framework designed to improve ontology alignment by combining predictions from various techniques. This ensemble approach uses a two-stage process of voting-based fusio…
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New framework enhances knowledge graph embedding negative sampling
Researchers have developed PyKEEN-NSX, a new modular framework designed to enhance negative sampling strategies within the PyKEEN knowledge graph embedding library. This extension addresses the limitations of existing K…
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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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Pinterest Ads leverage graph embeddings for improved CTR and CVR
Researchers have developed a novel approach to enhance advertising models by integrating user onsite and offsite conversion data into a large-scale heterogeneous graph. This method utilizes a Knowledge Graph Embedding (…
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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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New framework infuses semantic knowledge into traffic forecasting models
Researchers have developed a new framework for spatio-temporal traffic forecasting that enhances Graph Neural Networks (GNNs) by integrating external semantic knowledge. This approach uses general-purpose knowledge grap…
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New HydroAgent framework formalizes forecaster expertise for flood prediction
Researchers have developed HydroAgent, a novel framework that integrates Large Language Models (LLMs) into flood forecasting workflows. This system aims to formalize the tacit expertise of human forecasters by embedding…
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New method uses And-Inverter Graphs for scalable hardware Trojan detection
Researchers have developed a novel method for detecting hardware Trojans in large-scale System-on-Chip (SoC) designs by representing them as And-Inverter Graphs (AIGs). This approach utilizes knowledge graph embeddings …
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AI framework prioritizes biomedical annotations using knowledge graphs
Researchers have developed a new framework to improve the efficiency of biomedical curation by prioritizing candidate annotations using knowledge graphs. This approach leverages machine learning and knowledge graph embe…
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New framework ReaLM fuses KG embeddings with LLMs; study finds KG embedding models unstable
Researchers have developed ReaLM, a new framework that bridges the gap between knowledge graph embeddings and large language models by discretizing KG embeddings into learnable tokens. This approach allows for a more ef…
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Knowledge graph embeddings approximate probabilistic inference in SEL
Researchers have developed a method to approximate probabilistic inference in Statistical EL (SEL) by leveraging knowledge graph embeddings. This approach aims to make drawing conclusions from statistical information mo…
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New framework tackles privacy risks in knowledge graph embeddings
Researchers have developed a framework to identify and mitigate privacy risks in knowledge graph embeddings (KGEs). The study demonstrates how adversaries can infer sensitive user attributes from KGE outputs, even when …
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KrausKGE model advances knowledge graph embeddings with new mathematical framework
Researchers have introduced a new framework for knowledge graph embedding (KGE) called KrausKGE, which leverages Kraus channel structures derived from mathematical axioms. This approach provides a principled foundation …
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FixV2W uses knowledge graph embeddings to improve CVE-CWE mapping accuracy
Researchers have developed FixV2W, a novel method to enhance the accuracy of mappings between Common Vulnerabilities and Exposures (CVE) and Common Weakness Enumeration (CWE) entries. This approach utilizes knowledge gr…
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Study reveals catastrophic forgetting in knowledge graph embeddings is underestimated
Researchers have identified a significant issue in evaluating Continual Knowledge Graph Embedding (CKGE) methods, termed 'entity interference.' This phenomenon occurs when new entities introduced into a knowledge graph …