{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ensemble-of-neural-classifiers-for-scoring","title":"Ensemble of Neural Classifiers for Scoring Knowledge Base Triples","arxiv_id":"1703.04914","date":"2017-03-15","proceeding":null,"authors":["Ikuya Yamada","Motoki Sato","Hiroyuki Shindo"],"abstract":"This paper describes our approach for the triple scoring task at the WSDM Cup\n2017. The task required participants to assign a relevance score for each pair\nof entities and their types in a knowledge base in order to enhance the ranking\nresults in entity retrieval tasks. We propose an approach wherein the outputs\nof multiple neural network classifiers are combined using a supervised machine\nlearning model. The experimental results showed that our proposed method\nachieved the best performance in one out of three measures (i.e., Kendall's\ntau), and performed competitively in the other two measures (i.e., accuracy and\naverage score difference).","url_abs":"http://arxiv.org/abs/1703.04914v2","url_pdf":"http://arxiv.org/pdf/1703.04914v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ensemble-of-neural-classifiers-for-scoring","repo_url":"https://github.com/wsdm-cup-2017/lettuce","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"entity-retrieval","task_name":"Entity Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}