Entity alignment between knowledge graphs via contrastive learning

Chen, Xiaohui; Hu, Jie; Du, Shengdong; Teng, Fei

doi:10.1142/9789811269264_0025

Cited by 1 publication

(2 citation statements)

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“…To address this issue, Han et al (2023) introduce logical entity representation in knowledge graphs for differentiable rule learning (LERP). This method incorporates contextual information into rules by learning logical functions.…”

Section: Rule Learningmentioning

confidence: 99%

“…As a hybrid approach, rule mining methods extract meaningful patterns from data (Han et al, 2023;Cheng et al, 2022;Sadeghian et al, 2019;Lajus et al, 2020). By combining the flexibility of the data-driven approach and the interpretability of the knowledge-driven method, these techniques can enhance the performance of intention recognition while preserving explainability.…”

Section: Introductionmentioning

confidence: 99%

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Ontology-based Activity Intention Recognition via Human-in-the-loop Framework

Heo,

Hwang,

Gong

et al. 2024

Preprint

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Human intention recognition is crucial for various human-centered settings.Existing approaches can be categorized into data-driven and knowledge-drivenmethods. While data-driven methods have shown promising performance withlarge training sets, they face challenges in data collection and limited interpretability.In contrast, knowledge-driven methods have the advantage of notrequiring training data and being interpretable, but acquiring high-qualitydomain knowledge is time-consuming and costly. To address these challenges,we propose OntoIR, a novel Ontology-based human activity Intention Recognitionsystem with a human-in-the-loop rule generation and refinement framework.OntoIR involves an ontology and human-guided logical rules to recognize humanactivities and intentions. By Leveraging rule mining model, the OntoIR extractscandidate rules enriches them with contextual information. Human experts thencurate meaningful rules and supplement insufficient context information. Thisallows us to obtain explainable human intention recognition results throughexpressive rules in situations where data is scarce. To evaluate our system,we collect an Activity/Intention recognition dataset, AICar, in a car showroomenvironment. Comprehensive experiments are conducted on the AICar,demonstrating that the collaboration of rule-mining models and domain expertsimproves the performance of activity intention recognition without lowering theinterpretability of recognition results.

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Section: Rule Learningmentioning

confidence: 99%