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

• URL: http://arxiv.org/abs/2608.10668v1
• Date: Tue, 11 Aug 2026 08:47:58 GMT
• Title: FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs
• Abstract summary: We propose FITTER, the first fully-inductive structural model for temporal knowledge graph link prediction.<n> FITTER represents each predicate by its interaction patterns with others and time through encodings of relative rather than absolute ordering.<n>We prove the temporal encoding is time-shift invariant and evaluate FITTER on cross-domain, cross-graph transfer over six temporal knowledge graph benchmarks.
• Score: 20.962253638494385
• License: http://creativecommons.org/licenses/by/4.0/
• 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 model to a single graph and vocabulary. We propose FITTER, the first fully-inductive structural model for temporal knowledge graph link prediction that supports cross-domain transfer: the inference graph may contain entirely unseen entities, relation names, and timestamps drawn from a different domain. FITTER represents each predicate by its interaction patterns with others and time through encodings of relative rather than absolute ordering; message-passing fuses local and global temporal context to produce vocabulary-agnostic embeddings. We prove the temporal encoding is time-shift invariant and evaluate FITTER on cross-domain, cross-graph transfer over six temporal knowledge graph benchmarks of diverse domains, granularities, and time spans. FITTER consistently outperforms inductive baselines without retraining, indicating that vocabulary-agnostic structural learning is a viable foundation for inference over the heterogeneous knowledge graphs of the Semantic Web.

Related papers

• Towards Foundation Model on Temporal Knowledge Graph Reasoning [17.165969719351125]
  Temporal Knowledge Graphs (TKGs) store temporal facts with quadruple formats (s, p, o, t)<n>New model employs sinusoidal positional encodings to capture fine-grained temporal patterns.<n>PostRA demonstrates strong zero-shot performance on unseen temporal knowledge graphs.
  arXiv  Detail & Related papers  (2025-06-04T09:19:49Z)
• Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs [1.4916971861796382]
  We propose a novel framework called TEMT that exploits the power of pre-trained language models (PLMs) for text-enhanced temporal knowledge graph completion. Unlike previous approaches, TEMT effectively captures dependencies across different time points and enables predictions on unseen entities.
  arXiv  Detail & Related papers  (2023-09-28T11:43:49Z)
• Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding [143.5927158318524]
  Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence. We introduce a new Compositional Temporal Grounding task and construct two new dataset splits. We argue that the inherent structured semantics inside the videos and language is the crucial factor to achieve compositional generalization.
  arXiv  Detail & Related papers  (2023-01-22T08:02:23Z)
• Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification [68.06496970320595]
  Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. We present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs.
  arXiv  Detail & Related papers  (2022-05-18T07:47:01Z)
• TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs [13.085620598065747]
  In temporal knowledge graphs, time information is integrated into the graph by equipping each edge with a timestamp or a time range. We introduce TLogic, an explainable framework that is based on temporal logical rules extracted via temporal random walks.
  arXiv  Detail & Related papers  (2021-12-15T10:46:35Z)
• Time Series Analysis via Network Science: Concepts and Algorithms [62.997667081978825]
  This review provides a comprehensive overview of existing mapping methods for transforming time series into networks. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified notation and language. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic.
  arXiv  Detail & Related papers  (2021-10-11T13:33:18Z)
• Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer [140.72439827136085]
  We propose a graph reasoning and transfer learning framework named "Graphonomy" It incorporates human knowledge and label taxonomy into the intermediate graph representation learning beyond local convolutions. It learns the global and structured semantic coherency in multiple domains via semantic-aware graph reasoning and transfer.
  arXiv  Detail & Related papers  (2021-01-26T08:19:03Z)
• Learning the Implicit Semantic Representation on Graph-Structured Data [57.670106959061634]
  Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole. We propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs.
  arXiv  Detail & Related papers  (2021-01-16T16:18:43Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)
• Distributional semantic modeling: a revised technique to train term/word vector space models applying the ontology-related approach [36.248702416150124]
  We design a new technique for the distributional semantic modeling with a neural network-based approach to learn distributed term representations (or term embeddings) Vec2graph is a Python library for visualizing word embeddings (term embeddings in our case) as dynamic and interactive graphs.
  arXiv  Detail & Related papers  (2020-03-06T18:27:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.