The increasing specialization and segmentation of modern medical practice, while improving expertise, pose significant challenges in efficiently connecting patients with the right healthcare professionals. The vast array of medical specializations, coupled with sparse data on doctor profiles, overwhelms traditional recommendation algorithms. This study introduces CLEAR-Med: A Contrastive Learning-Enhanced knowledge grAph Recommender designed to match patients with healthcare providers in specific Medical subfields. CLEAR-Med leverages a domain-specific Knowledge Graph (KG) and advanced Contrastive Learning (CL) techniques to capture the nuanced expertise and preferences of doctors, effectively addressing data sparsity and information overload in Online Healthcare Communities (OHCs). The system constructs a comprehensive KG enriched with diverse information, including doctors’ social relationships, professional networks, and specialized attributes derived from OHC data. By embedding key entities and attributes through CL, CLEAR-Med generates robust representations, supported by a flexible attribute encoding module that integrates both efficient LSTMs and powerful Transformer-based models. Its modular prediction layer, featuring options from a stable Multilayer Perceptron (MLP) to an advanced generative diffusion model, then produces highly accurate and personalized recommendation sequences. CLEAR-Med demonstrates superior recommendation performance in baseline comparison experiments, excelling in adaptability and accuracy within OHC settings. Ablation studies confirm the effectiveness of individual components, while further experiments exploring advanced architectures like Transformers and diffusion models highlight the strong balance our framework strikes between performance and computational efficiency. Beyond addressing data sparsity challenges, CLEAR-Med establishes a strong foundation for future advancements in specialized medical matching systems, filling critical research gaps in the domain.

Driving risk evaluation is a critical issue in driving safety analysis. In traditional driving risk evaluation models, vehicles are analyzed as isolated units. Nevertheless, vehicles are surrounded by other vehicles during driving in a real setting, and therefore, the driving patterns of target vehicles are inevitably affected by surrounding vehicles. In this paper, we proposed a new driving risk evaluation model incorporating the driving patterns of the target vehicle, the driving patterns of surrounding vehicles, and the interactions between the target vehicle and surrounding vehicles to improve driving safety in situations like car-following and merging situations. According to experiments on real data, our proposed method outperformed the state-of-the-art methods by at least 14.2% in terms of precision. Our work verified that factors like the driving patterns of surrounding vehicles and the interactions between a target vehicle and surrounding vehicles can enhance the performance of driving risk evaluation. The experimental results also showed that the driving patterns of surrounding vehicles in different positions vary in evaluating the driving risk of the target vehicle. More specifically, the surrounding vehicles in cross positions casted the strongest influence, followed by the surrounding vehicles in the diagonal cross positions. In summary, our study provided a novel way to enhance driving risk evaluation performance for driving safety improvement.

Ontology is the backbone of the Semantic Web, helping users search for relevant resources from the Web of linked data. The existing context-free mapping approach between tags and concepts fails to address the problems of social synonymy and social polysemy when ontologies are induced from folksonomies. The novel contributions of this paper are threefold. First, grounded in the cognitively motivated category utility measure, a novel basic-level concept mining algorithm is developed to construct semantically rich concept vectors to alleviate the problem of social synonymy. Second, contextual aspects of ontology learning are exploited via probabilistic topic modeling to address the problem of social polysemy. Third, a novel context-sensitive domain ontology learning algorithm that combines link- and content-based semantic analysis is developed to identify both taxonomic and associative relations among concepts. To the best of our knowledge, this is the first successful research that exploits a cognitively motivated method to learn context-sensitive domain ontologies from folksonomies. By using the Open Directory Project ontology as a benchmark, we examined the effectiveness of the proposed algorithms based on social annotations crawled from three different folksonomy sites. Our experimental results show that the proposed ontology learning system significantly outperforms the best baseline system by 13.83% in terms of taxonomic F-measure. The practical implication of our research is that high-quality ontologies are constructed with minimal human intervention to facilitate concept-driven retrieval of linked data and the knowledge-based interoperability among enterprises.

Though numerous research has been devoted to social network discovery and analysis, relatively little research has been conducted on business network discovery. The main contribution of our research is the development of a novel probabilistic generative model for latent business networks mining. Our experimental results confirm that the proposed method outperforms the well-known vector space based model by 24% in terms of AUC value.