A collection of research on knowledge graphs
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Updated
Oct 7, 2022 - JavaScript
A collection of research on knowledge graphs
AKGR: Awesome Knowledge Graph Reasoning is a collection of knowledge graph reasoning works, including papers, codes and datasets
We build KGs the way nature builds matter
Archive of Temporal Knowledge Reasoning in Social Network and Knowledge Graph
Some papers on Temporal Knowledge Graph Embedding and Reasoning
[WSDM 2022] EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs
The official repo of TimeLlama, an instruction-finetuned Llama2 series that improve complex temporal reasoning ability.
[NeurIPS 2023] TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph
[WWW 2023] The source code of "Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning"
Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion
Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
DreamGraph is a graph-first cognitive layer (graph → MCP → CLI → dashboard → extension) that builds a persistent knowledge graph to reason, validate changes, and generate docs.
ChronoKGE - A knowledge graph embedding framework for time-focused representation learning (RepL4NLP @ ACL 2022)
Open-source, multi-provider research automation with persistent experts, eval-based routing, and cost guardrails.
EMNLP 2018: HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding
Zero-install persistent memory brain for Claude Code and Claude Desktop. Stops re-asking and re-deriving across sessions. SQLite FTS5 default, ChromaDB + Ollama on the semantic branch.
Code for https://arxiv.org/abs/1907.03143
Document-to-knowledge-graph platform: define ontology → upload docs → extract knowledge → query in natural language. Powered by Graphiti (temporal KG) + Eino (Go agent).
Temporiki — Lightweight temporal Context Graph + Obsidian-native LLM Wiki with real memory (SQLite + Chroma). Zero-command daily flow.
A study to find a suitable temporal-based embedding model in detecting IoT malware through network analysis
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