2010/11/16 by Anon Plangprasopchok, Kristina Lerman, Plangprasopchok, Anon +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Clustering Algorithms Research #Artificial Intelligence (cs.AI) #Biomedical Text Mining and Ontologies #Computers and Society (cs.CY) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #cs.AI #cs.CY #cs.LG
paper · pdf · doi:10.48550/arxiv.1011.3557
In Proceedings of the 4th ACM Web Search and Data Mining Conference (WSDM)
arxiv created 2010/11/16 · openalex publication_date 2010/11/16 · arxiv updated 2015/03/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach to learning complex structures is to integrate many smaller, incomplete and noisy structure fragments. In this work, we present an unsupervised probabilistic approach that extends affinity propagation to combine the small ontological fragments into a collection of integrated, consistent, and larger folksonomies. This is a challenging task because the method must aggregate similar structures while avoiding structural inconsistencies and handling noise. We validate the approach on a real-world social media dataset, comprised of shallow personal hierarchies specified by many individual users, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures, compared to an approach using only the standard affinity propagation algorithm. Additionally, the approach yields better overall integration quality than a state-of-the-art approach based on incremental relational clustering.