%0 Conference Paper %1 perera2012data %A Perera, Sujan %A Henson, Cory %A Thirunarayan, Krishnaprasad %A Sheth, Amit %A Nair, Suhas %B Bioinformatics and Biomedicine (BIBM), 2012 IEEE International Conference on %D 2012 %K imported %P 1--8 %T Data driven knowledge acquisition method for domain knowledge enrichment in the healthcare %X Semantic computing technologies have matured to ogists and domain experts. An ontologist may be unaware be applicable to many critical domains, such as life sciences and of the kind of knowledge to gather from a domain expert, health care. However, the key to their success is the rich domain and a domain expert may be unaware of the knowledge they knowledge which consists of domain concepts and relationships, whose creation and refinement remains a challenge. In this paper, should provide to solve the problem in a technically sound we develop a technique for enriching domain knowledge, focusing manner. Furthermore, the resulting domain knowledge is often on populating the domain relationships. We determine missing subjective, as it largely depends on the experiential knowledge relationships between the domain concepts by validating domain of a small group. It is not reasonable to assume that a single knowledge against real world data sources. We evaluate our person or a small group of people would have complete approach in the healthcare domain using Electronic Medical Record(EMR) data, and demonstrate that semantic techniques knowledge of a domain like healthcare, or even one of its can be used to semi-automate labour intensive tasks without subareas (e.g., cardiology). Therefore, the DKB built with the sacrificing fidelity of domain knowledge. inputs from experts will suffer from shortcomings such as Index Terms—knowledge acquisition, semantic technology, inefficiency in capturing expert knowledge, subjectivity, and Electronic Medical Record incompleteness.

@inproceedings{perera2012data, abstract = {Semantic computing technologies have matured to ogists and domain experts. An ontologist may be unaware be applicable to many critical domains, such as life sciences and of the kind of knowledge to gather from a domain expert, health care. However, the key to their success is the rich domain and a domain expert may be unaware of the knowledge they knowledge which consists of domain concepts and relationships, whose creation and refinement remains a challenge. In this paper, should provide to solve the problem in a technically sound we develop a technique for enriching domain knowledge, focusing manner. Furthermore, the resulting domain knowledge is often on populating the domain relationships. We determine missing subjective, as it largely depends on the experiential knowledge relationships between the domain concepts by validating domain of a small group. It is not reasonable to assume that a single knowledge against real world data sources. We evaluate our person or a small group of people would have complete approach in the healthcare domain using Electronic Medical Record(EMR) data, and demonstrate that semantic techniques knowledge of a domain like healthcare, or even one of its can be used to semi-automate labour intensive tasks without subareas (e.g., cardiology). Therefore, the DKB built with the sacrificing fidelity of domain knowledge. inputs from experts will suffer from shortcomings such as Index Terms—knowledge acquisition, semantic technology, inefficiency in capturing expert knowledge, subjectivity, and Electronic Medical Record incompleteness. }, added-at = {2015-01-14T21:25:14.000+0100}, author = {Perera, Sujan and Henson, Cory and Thirunarayan, Krishnaprasad and Sheth, Amit and Nair, Suhas}, biburl = {https://www.bibsonomy.org/bibtex/22ee0949126a808ac268d7bf79d6a2502/diverzulu}, booktitle = {Bioinformatics and Biomedicine (BIBM), 2012 IEEE International Conference on}, file = {:D\:\\ASUS-FINO\\Inkhognito_M\\David\\PhD\\Web Semântica\\Biomedicine Ontology\\Automated Acquisition\\2012-Data Driven Knowledge Acquisition Method for Domain Knowledge Enrichment in the Healthcare.pdf:PDF}, interhash = {c0a83879b8f5b250644bded89c6d7e0f}, intrahash = {2ee0949126a808ac268d7bf79d6a2502}, keywords = {imported}, organization = {IEEE}, pages = {1--8}, timestamp = {2015-01-14T21:25:14.000+0100}, title = {Data driven knowledge acquisition method for domain knowledge enrichment in the healthcare}, year = 2012 }