{"id":{"repo_id":"maynooth","oai_identifier":"oai:mural.maynoothuniversity.ie:13608"},"canonical_url":"https://search.dev.ndltd.org/etd/maynooth/oai:mural.maynoothuniversity.ie:13608","repository":{"repo_id":"maynooth","name":"National University of Ireland - Maynooth","base_url":"http://mural.maynoothuniversity.ie/cgi/oai2"},"display":{"title":"Patent Collaboration and Team Formation","abstract":"The team formation problem has existed for many years in various guises. One important problem in the team formation problem is to produce small teams that have a required set of skills. We propose a framework that incorporates machine learning to predict unobserved links between collaborators, alongside Steiner tree problem solutions to form small teams to cover given tasks. Our framework not only considers size of the team but also how likely team members are to collaborate with each other. The framework is tested on sets of data from two different companies. The results show that this model consistently returns smaller collaborative teams.","abstract_html":"The team formation problem has existed for many years in various guises. One important problem in the team formation problem is to produce small teams that have a required set of skills. We propose a framework that incorporates machine learning to predict unobserved links between collaborators, alongside Steiner tree problem solutions to form small teams to cover given tasks. Our framework not only considers size of the team but also how likely team members are to collaborate with each other. The framework is tested on sets of data from two different companies. 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One important problem in the team formation problem is to produce small teams that have a required set of skills. We propose a framework that incorporates machine learning to predict unobserved links between collaborators, alongside Steiner tree problem solutions to form small teams to cover given tasks. Our framework not only considers size of the team but also how likely team members are to collaborate with each other. The framework is tested on sets of data from two different companies. The results show that this model consistently returns smaller collaborative teams."]},{"key":"dc:format","label":"Dc Format","values":["text"]},{"key":"dc:title","label":"Title","values":["Patent Collaboration and Team Formation"]}]}],"canonical_facts":{"dc:creator":["Keane, Peter"],"dc:date":["2020"],"dc:date.issued":["2020"],"dc:description.abstract":["The team formation problem has existed for many years in various guises. One important problem in the team formation problem is to produce small teams that have a required set of skills. We propose a framework that incorporates machine learning to predict unobserved links between collaborators, alongside Steiner tree problem solutions to form small teams to cover given tasks. Our framework not only considers size of the team but also how likely team members are to collaborate with each other. The framework is tested on sets of data from two different companies. 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