Autonomous FAIR Digital Objects augment FDOs with Semantic Web-based policy, announcement, and reputation-weighted agreement layers, resolving 56.3% of ClinVar conflicts in evaluation while tolerating bounded attacks.
ACM Computing Surveys54(4), 71:1– 71:37 (2021)
7 Pith papers cite this work. Polarity classification is still indexing.
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Action Units are introduced as typed, composable components in knowledge graphs that encode epistemic, transformational, and intervention operations with explicit applicability conditions, enabling post-FAIR infrastructures via the TripleA principle.
Learned policies for short-term-to-long-term memory transfer in temporal knowledge graphs outperform baselines on the RoomKG benchmark with capacity 128.
IdeaForge combines multiple innovation methodologies through specialist agents on a persistent knowledge graph, using cross-methodology convergent claim linkages to rank and draft patent claims with higher traceability than single-method baselines.
A phenotype-driven framework integrates GNNs, causal inference, probabilistic reasoning, and LLMs to expand knowledge graphs via multi-objective optimization that balances novelty, relevance, and evidence validation.
BifrostRAG combines dual knowledge graphs with hybrid retrieval to improve multi-hop question answering on construction safety regulations, reporting 87.3% F1 on a custom dataset.
The thesis proposes specialized algebraic, logical, and geometric methods to enable scalable reasoning over imprecise attributes, probabilistic triples, and incomplete schemas in knowledge graphs.
citing papers explorer
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Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge
Autonomous FAIR Digital Objects augment FDOs with Semantic Web-based policy, announcement, and reputation-weighted agreement layers, resolving 56.3% of ClinVar conflicts in evaluation while tolerating bounded attacks.
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Actionable Understanding: Action Units for Bridging the Knowledge-Action Gap in Post-FAIR Knowledge Infrastructures
Action Units are introduced as typed, composable components in knowledge graphs that encode epistemic, transformational, and intervention operations with explicit applicability conditions, enabling post-FAIR infrastructures via the TripleA principle.
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Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability
Learned policies for short-term-to-long-term memory transfer in temporal knowledge graphs outperform baselines on the RoomKG benchmark with capacity 128.
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IdeaForge: A Knowledge Graph-Grounded Multi-Agent Framework for Cross-Methodology Innovation Analysis and Patent Claim Generation
IdeaForge combines multiple innovation methodologies through specialist agents on a persistent knowledge graph, using cross-methodology convergent claim linkages to rank and draft patent claims with higher traceability than single-method baselines.
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A phenotype-driven and evidence-governed framework for knowledge graph enrichment and hypotheses discovery in population data
A phenotype-driven framework integrates GNNs, causal inference, probabilistic reasoning, and LLMs to expand knowledge graphs via multi-objective optimization that balances novelty, relevance, and evidence validation.
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Bridging Dual Knowledge Graphs for Multi-Hop Question Answering in Construction Safety
BifrostRAG combines dual knowledge graphs with hybrid retrieval to improve multi-hop question answering on construction safety regulations, reporting 87.3% F1 on a custom dataset.
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Scalable Uncertainty Reasoning in Knowledge Graphs
The thesis proposes specialized algebraic, logical, and geometric methods to enable scalable reasoning over imprecise attributes, probabilistic triples, and incomplete schemas in knowledge graphs.