AutoBayesian: Developing Bayesian Networks Based on Text Mining

Autor:	Sandeep Raghuram, Jiaqi Ge, Eric Tinsley, Mathew J. Palakal, Josette Jones, Dave Pecenka, Yuni Xia, Jerry Geesaman, Jean Bandos
Rok vydání:	2011
Předmět:	Decision support system Process (engineering) business.industry Computer science Bayesian network Construct (python library) computer.software_genre Variable-order Bayesian network Text mining Influence diagram Data mining business computer Dynamic Bayesian network
Zdroj:	Database Systems for Advanced Applications ISBN: 9783642201516 DASFAA (2)
DOI:	10.1007/978-3-642-20152-3_37
Popis:	Bayesian network is a widely used tool for data analysis, modeling and decision support in various domains. There is a growing need for techniques and tools which can automatically construct Bayesian networks from massive text or literature data. In practice, Bayesian networks also need be updated when new data is observed, and literature mining is a very important source of new data after the initial network is constructed. Information closely related to Bayesian network usually includes the causal associations, statistics information and experimental results. However, these associations and numerical results cannot be directly integrated with the Bayesian network. The source of the literature and the perceived quality of research needs to be factored into the process of integration. In this demo, we will present a general methodology and toolkit called AutoBayesian that we developed to automatically build and update a Bayesian network based on the casual relationships derived from text mining.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c3c07353d0acb5bcb4ac66f4c7394086 https://doi.org/10.1007/978-3-642-20152-3_37 Zobrazit plný text záznamu