Automated Knowledge Extraction from the Federal Acquisition Regulations System (FARS)
| dc.contributor.author | Saha, Srishty | |
| dc.contributor.author | Joshi, Karuna Pande | |
| dc.contributor.author | Frank, Renee | |
| dc.contributor.author | Aebig, Michael | |
| dc.contributor.author | Lin, Jiayong | |
| dc.date.accessioned | 2018-10-17T16:37:22Z | |
| dc.date.available | 2018-10-17T16:37:22Z | |
| dc.date.issued | 2017-12-11 | |
| dc.description | 2017 IEEE International Conference on Big Data (BIGDATA) | en |
| dc.description.abstract | With increasing regulation of Big Data, it is becoming essential for organizations to ensure compliance with various data protection standards. The Federal Acquisition Regulations System (FARS) within the Code of Federal Regulations (CFR) includes facts and rules for individuals and organizations seeking to do business with the US Federal government. Parsing and gathering knowledge from such lengthy regulation documents is currently done manually and is time and human intensive. Hence, developing a cognitive assistant for automated analysis of such legal documents has become a necessity. We have developed semantically rich approach to automate the analysis of legal documents and have implemented a system to capture various facts and rules contributing towards building an efficient legal knowledge base that contains details of the relationships between various legal elements, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules. In this paper, we describe our framework along with the results of automating knowledge extraction from the FARS document (Title 48, CFR). Our approach can be used by Big Data Users to automate knowledge extraction from Large Legal documents | en |
| dc.description.uri | https://ieeexplore.ieee.org/document/8258353 | en |
| dc.format.extent | 8 pages | en |
| dc.genre | conference paper pre-print | en |
| dc.identifier | doi:10.13016/M26M3373P | |
| dc.identifier.citation | Srishty Saha, Karuna Pande Joshi, Renee Frank, Michael Aebig, Jiayong Lin, Automated Knowledge Extraction from the Federal Acquisition Regulations System (FARS), December 11, 2017, DOI: 10.1109/BigData.2017.8258353 | en |
| dc.identifier.uri | 10.1109/BigData.2017.8258353 | |
| dc.identifier.uri | http://hdl.handle.net/11603/11577 | |
| dc.language.iso | en | en |
| dc.publisher | IEEE | en |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Information Systems Department | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | © 2017 IEEE | |
| dc.subject | Deep Learning | en |
| dc.subject | Information Retrieval | en |
| dc.subject | NLP | en |
| dc.subject | Code of Federal Regulations | en |
| dc.subject | UMBC Ebiquity Research Group | en |
| dc.title | Automated Knowledge Extraction from the Federal Acquisition Regulations System (FARS) | en |
| dc.type | Text | en |