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FITTER model enables vocabulary-agnostic inference on temporal knowledge graphs

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced FITTER, a novel model designed for temporal knowledge graph link prediction. Unlike previous methods that require known entities and relation names from a single graph, FITTER is vocabulary-agnostic and can perform cross-domain transfer. It achieves this by representing predicates through interaction patterns and time via relative ordering encodings, enabling it to infer links even with unseen entities and relation names. AI

RANK_REASON The item describes a new research paper introducing a novel model for knowledge graph inference. [lever_c_demoted from research: ic=1 ai=1.0]

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FITTER model enables vocabulary-agnostic inference on temporal knowledge graphs

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jiaxin Pan, Mojtaba Nayyeri, Osama Mohammed, Daniel Hernandez, Rongchuan Zhang, Cheng Cheng, Steffen Staab ·

    FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs

    arXiv:2608.10668v1 Announce Type: new Abstract: Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timestamps to be reasoned about are already known at training time, restricting each mo…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs

    Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timestamps to be reasoned about are already known at training time, restricting each model to a single graph and vocabulary. We propose…

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