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<p>Temporal knowledge graph (TKG) representation learning is a pivotal task aimed at transforming entities and relations within TKG from a high-dimensional vector space to a lower-dimensional vector space, while preserving the relational features inherent in TKG. TKG comprises a sequence of knowledge graphs (KGs) at various timestamps. Presently, existing methodologies tend to either focus solely on learning historical event characteristics or exclusively model time-dependent relationships. There is a notable dearth of research concerning incomplete data, posing significant challenges to comprehending and capturing the intricate relationship characteristics within TKG. In response to this challenge, a novel method named TFCE is introduced to address the challenges posed by temporal evolution and incomplete data in TKGs. TFCE encompasses three core components: a <b>T</b>emporal <b>F</b>eature Module, a <b>C</b>omplex <b>E</b>volution Module, and a Temporally Embedded Decoder. TFCE incorporates a temporal feature module, enabling the temporal encoding of entities and relations within KGs. This module seamlessly integrates temporal information into the representation learning process. By discerning patterns of entities and relations across time, TFCE facilitates the comprehension and discovery of temporal order relations within KGs. The complex evolution module adeptly learns the evolutionary representation of entities and relationships at each timestamp through recursive modeling of the KG sequence. By systematically analyzing the KG sequence, this module captures the nuanced evolution of entities and relationships over time, enhancing the understanding of temporal dependencies between events. To accommodate incomplete temporal data, TFCE employs a temporally embedded decoder. This decoder effectively processes incomplete temporal data, facilitating the inference of representation learning. Experimental validation conducted across three real-world datasets, namely ICEWS14s, ICEWS 05-15, and ICEWS18, underscores the superiority of TFCE over baseline methods. The TFCE framework demonstrates remarkable efficacy in capturing temporal relationships within TKG, thus showcasing its potential for advancing temporal knowledge graph representation learning methodologies.</p>

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Temporal knowledge graph representation learning with temporal feature and complex evolution

  • 期刊论文

  • 发表日期：

    2025年5月7日

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  • Qian Liu,
  • Siling Feng,
  • Mengxing Huang,
  • Uzair Aslam Bhatti

摘要

Temporal knowledge graph (TKG) representation learning is a pivotal task aimed at transforming entities and relations within TKG from a high-dimensional vector space to a lower-dimensional vector space, while preserving the relational features inherent in TKG. TKG comprises a sequence of knowledge graphs (KGs) at various timestamps. Presently, existing methodologies tend to either focus solely on learning historical event characteristics or exclusively model time-dependent relationships. There is a notable dearth of research concerning incomplete data, posing significant challenges to comprehending and capturing the intricate relationship characteristics within TKG. In response to this challenge, a novel method named TFCE is introduced to address the challenges posed by temporal evolution and incomplete data in TKGs. TFCE encompasses three core components: a Temporal Feature Module, a Complex Evolution Module, and a Temporally Embedded Decoder. TFCE incorporates a temporal feature module, enabling the temporal encoding of entities and relations within KGs. This module seamlessly integrates temporal information into the representation learning process. By discerning patterns of entities and relations across time, TFCE facilitates the comprehension and discovery of temporal order relations within KGs. The complex evolution module adeptly learns the evolutionary representation of entities and relationships at each timestamp through recursive modeling of the KG sequence. By systematically analyzing the KG sequence, this module captures the nuanced evolution of entities and relationships over time, enhancing the understanding of temporal dependencies between events. To accommodate incomplete temporal data, TFCE employs a temporally embedded decoder. This decoder effectively processes incomplete temporal data, facilitating the inference of representation learning. Experimental validation conducted across three real-world datasets, namely ICEWS14s, ICEWS 05-15, and ICEWS18, underscores the superiority of TFCE over baseline methods. The TFCE framework demonstrates remarkable efficacy in capturing temporal relationships within TKG, thus showcasing its potential for advancing temporal knowledge graph representation learning methodologies.