Temporal Event Knowledge Base Overview

Updated 9 December 2025

Temporal Event Knowledge Base is a structured system that models events, time expressions, and causal interrelations to support advanced temporal reasoning.
It integrates multi-source data using techniques like temporal relation extraction and LLM-assisted methods to enhance narrative comprehension and prediction.
TEKBs power practical applications such as timeline generation, temporal QA, forecasting, and counterfactual reasoning through robust graph and logic-based models.

A Temporal Event Knowledge Base (TEKB) is a structured repository that canonically encodes events, their temporal expressions (points, intervals), non-event entities, and a rich set of temporal and causal interrelations. TEKBs enable temporal reasoning, event-centric retrieval, timeline construction, and downstream applications such as temporal QA, forecasting, and narrative comprehension. The construction, representation, and use of TEKBs have been advanced across multiple paradigms including knowledge graph (KG) formalism, temporal event relation extraction, logic-guided temporal reasoning, and retrieval-augmented frameworks.

1. Formal Foundations and Knowledge Representation

A TEKB is commonly modeled as a graph- or RDF-based structure:

$K = (E,\, C,\, T,\, P,\, R,\, F)$

where:

$E$ : finite set of event instances
$C$ : finite set of non-event entities (actors, places, etc.)
$T$ : set of time expressions (time points or intervals)
$P$ : vocabulary of predicates (roles, event relations)
$R \subseteq (E \cup C) \times P \times (E \cup C) \times T \times T$ : temporal relations, each valid over an interval $[t_{\text{begin}}, t_{\text{end}}]$
$F$ : provenance and scoring functions, such as link strength or popularity

The EventKG data model, for instance, extends the Simple Event Model (SEM) to support rich temporal anchoring, reified relations, provenance, and interlinking across sources. Each event is assigned normalized timestamps, typed roles, and, where possible, locations, labels, and cross-lingual mappings (Gottschalk et al., 2018, Gottschalk et al., 2019).

Temporal event KGs also support n-ary relations (event–actor–role with time), provenance via named graphs (allowing source distinction), and popularity/strength via hyperlink and co-mention counts.

2. Methodologies for Temporal Knowledge Base Construction

TEKBs derive from several distinct, technically rigorous methodologies:

1. Multi-Source Integration: EventKG integrates events and temporal relations from Wikidata, DBpedia, YAGO, Wikipedia event lists, and Current Events Portals. Temporal fusion uses majority voting or trusted-source priority to reconcile timestamp conflicts (Gottschalk et al., 2018).

2. Temporal Relation Extraction (ETRE): Approaches such as MulCo distill multi-scale temporal knowledge by combining local context encoders (BERT variants) with global document-aware GNNs over both syntactic- and time-aware graph views. Event pairs $(e_i^1, e_i^2)$ are classified over a palette of temporal relations $R$ (e.g., BEFORE, AFTER, SIMULTANEOUS, INCLUDES, IS_INCLUDED, VAGUE). Proximity bands—short versus long—dictate extraction strategies, with knowledge distilled across bands using contrastive multi-scale loss (Yao et al., 2022).

3. Narrative Induction: Narrative-driven methods exploit the “double temporality” principle—textual event order matches real-world event chronology in narrative passages. Weakly supervised graph induction identifies narrative paragraphs, from which event chains and pairwise temporal orderings are extracted using Causal Potential scoring (Yao et al., 2018).

4. LLM-Assisted Extraction: LLMs are harnessed to extract and quantify unstructured event reports (e.g., traffic accidents, disruptions), converting them into structured event tuples with time, location, type, severity, and causal impact attributes. These feed downstream analytical pipelines, such as propensity-score matching for average treatment effect estimation (Niu et al., 16 Nov 2025).

5. Retrieval-Augmented Temporal Relation Extraction: Systems like PETR leverage LLMs to generate or suggest prompt and verbalizer templates for event temporal relation extraction, greatly improving identification especially of ambiguous or rare temporal relations (Zhang et al., 22 Mar 2024).

3. Temporal and Causal Relational Schemas

TEKBs widely adopt interval temporal logic (e.g., Allen's interval algebra) to encode event-event and event-entity interval-based relationships. Core relation types include:

Before, After: $t_{\text{end}}(e_1) < t_{\text{start}}(e_2)$
During, Includes, Included-in, Overlaps, Meets, Simultaneous
Causal (causes, enables, prevents): Explicit event causation, optionally constrained by temporal precedence $t(e_x) < t(e_y)$ (Zhang et al., 6 Jun 2025)

Complex temporal queries can be supported over these schemas (e.g., "find all events overlapping 1941–1945 involving a specific entity").

Causal knowledge is increasingly represented explicitly. Event-CausNet infers average treatment effects of event types on outcomes via matched controls, encoding this in a causal DAG or relational table for query and real-time forecasting adjustment (Niu et al., 16 Nov 2025).

4. Temporal KG Completion and Temporal Logic Reasoning

The completion and enrichment of TEKBs require advanced reasoning in the presence of missing or incomplete temporal scopes and causal links.

1. Knowledge Graph Embedding Frameworks: TIME2BOX represents entity/relation sets and temporal constraints as axis-aligned boxes in $\mathbb{R}^d$ . Intersection of boxes filters answer sets for interval or timestamp-scoped queries. The framework supports mixed atemporal and temporal facts and can infer missing validity intervals for atemporal triples (Cai et al., 2021).

2. Attention Masking and Contrastive Architectures: AMCEN distinguishes recurring from novel events via attention masking and contrastive classification, ensuring balanced, precise prediction of both historic and new future events. Multi-hop message-passing captures local and global temporal dependencies (Yang et al., 16 May 2024).

3. Logic-Guided Temporal Reasoning: LCGE jointly learns temporal and causal event embeddings, incorporating temporal Horn rules automatically mined from the KG, and uses commonsense static KG regularization to enhance and constrain prediction (Niu et al., 2022).

4. Differentiable Rule-Based Inference: TEILP transforms KGs into explicit TEKGs with event and timestamp nodes, enabling differentiable random walk logical inference and probabilistic mixture models for time interval prediction, robust to low-data and long-range, multi-hop dependencies (Xiong et al., 2023).

5. Analytics, Applications, and Query Interfaces

TEKBs underpin diverse applications:

Temporal Question Answering: Structured queries over TEKBs (via SPARQL or bespoke APIs) enable answering "Who held office during...?", "Which events took place in ..." etc., with temporal and possibly cross-lingual restrictions (Gottschalk et al., 2018, Kannen et al., 2022).
Timeline Construction and Biographical Summarization: Timeline algorithms rank and select events according to cross-lingual popularity, relevance, and temporal coverage for entities or topics (Gottschalk et al., 2018, Gottschalk et al., 2019).
Exploratory Analytics and Visualization: Cross-cultural event prominence (via multilingual TEKBs), provenance tracking, temporal network visualization, and timeline generation are supported (Gottschalk et al., 2018).
Forecasting and Counterfactual Reasoning: Event-CausNet and logic-enhanced forecasting frameworks dynamically inject causal knowledge from the TEKB into real-time prediction for applications such as traffic management, supporting both predictive accuracy and interpretability (Niu et al., 16 Nov 2025).
LLM Retrieval-Augmentation: Dual subgraph architectures (entity+event) and bipartite mappings enable temporally- and causally-aware RAG over structured event KGs, improving fine-grained QA over narratives and documents (Zhang et al., 6 Jun 2025).

6. Limitations, Scalability, and Future Directions

Schema Scope: Current TEKBs exhibit limited spatial coverage (location recall), event-type granularity, and rarely model cross-document temporal links or true n-ary event schemas (Gottschalk et al., 2018, Yao et al., 2022).
Data Integration Bottlenecks: Integration of non-structured sources (news text, social media) remains challenging; dependency on curated semi-structured data restricts relation recall (Gottschalk et al., 2018).
Disambiguation and Consistency: Sense disambiguation for event mentions and temporal consistency (acyclicity, transitivity) enforcement require post-processing, often via ILP or global optimization (Yao et al., 2022, Gottschalk et al., 2019).
Compute and Scalability: GNN, LLM, or joint multi-scale models are computationally intensive, with inference and batch graph construction as primary bottlenecks. Engineering optimizations include batch sharding, precomputed parses, and windowed or heuristic event-pair sampling (Yao et al., 2022).

Future directions include more robust integration with upstream extraction pipelines, extension to more languages and event ontologies, temporal closure and global constraint satisfaction, streaming and incremental TEKB updates, and hybrid logic–embedding or logic–LLM frameworks for explainable causal and temporal reasoning (Sharma, 15 Jan 2025, Xiong et al., 2023, Niu et al., 2022).

7. Benchmarking, Datasets, and Evaluation Protocols

TEKB research is anchored by rich benchmark datasets and evaluation protocols:

Dataset	#Events	Temporal Span	Key Focus/Notes
EventKG	690K	1900–2020 (main)	Multilingual, 2.3M temporal rel
TB-Dense	~6K	Documents	Short-range event ETRE
MATRES	~300	Documents	Short-range, 4 labels
ICEWS14/18	70–370K	News, 2005–18	Event forecasting, TKG
Wikidata12k	~32K	1479–2018	Interval-oriented, evaluation
GDELT	~2M	News, sub-hourly	Fine-grained, high-volume

Evaluation metrics include F1 (macro/micro), MRR, Hits@1/3/10, interval IOU (aeIOU, gaeIOU), counterfactual query response accuracy, and user satisfaction for timeline outputs. Ablation analyses dissect contributions of architectural components (multiscale, contrastive, logic/casual reasoning) (Cai et al., 2021, Niu et al., 2022, Yang et al., 16 May 2024, Xiong et al., 2023).

In summary, a Temporal Event Knowledge Base is the result of rigorous graph- and logic-based modeling, robust multi-source integration, and contemporary advances in temporal relation extraction, embedding, and LLM-augmented frameworks. TEKBs serve as essential infrastructure for time-aware and causality-sensitive computation across information retrieval, QA, forecasting, and knowledge-intensive language tasks (Gottschalk et al., 2018, Yao et al., 2022, Yang et al., 16 May 2024, Niu et al., 2022, Niu et al., 16 Nov 2025, Zhang et al., 6 Jun 2025).