Deep Outdated Fact Detection in Knowledge Graphs
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913224638595072 |
|---|---|
| author | Tu, Huiling Yu, Shuo Saikrishna, Vidya Xia, Feng Verspoor, Karin |
| author_facet | Tu, Huiling Yu, Shuo Saikrishna, Vidya Xia, Feng Verspoor, Karin |
| contents | Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains. However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. In response, this paper presents DEAN (Deep outdatEd fAct detectioN), a novel deep learning-based framework designed to identify outdated facts within KGs. DEAN distinguishes itself by capturing implicit structural information among facts through comprehensive modeling of both entities and relations. To effectively uncover latent out-of-date information, DEAN employs a contrastive approach based on a pre-defined Relations-to-Nodes (R2N) graph, weighted by the number of entities. Experimental results demonstrate the effectiveness and superiority of DEAN over state-of-the-art baseline methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03732 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Outdated Fact Detection in Knowledge Graphs Tu, Huiling Yu, Shuo Saikrishna, Vidya Xia, Feng Verspoor, Karin Artificial Intelligence Computation and Language Digital Libraries Machine Learning 68T09, 68T30, 68P20 I.2.6; I.2.4; H.3.7; H.3.3 Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains. However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. In response, this paper presents DEAN (Deep outdatEd fAct detectioN), a novel deep learning-based framework designed to identify outdated facts within KGs. DEAN distinguishes itself by capturing implicit structural information among facts through comprehensive modeling of both entities and relations. To effectively uncover latent out-of-date information, DEAN employs a contrastive approach based on a pre-defined Relations-to-Nodes (R2N) graph, weighted by the number of entities. Experimental results demonstrate the effectiveness and superiority of DEAN over state-of-the-art baseline methods. |
| title | Deep Outdated Fact Detection in Knowledge Graphs |
| topic | Artificial Intelligence Computation and Language Digital Libraries Machine Learning 68T09, 68T30, 68P20 I.2.6; I.2.4; H.3.7; H.3.3 |
| url | https://arxiv.org/abs/2402.03732 |