UMBC ebiquity

Automated Knowledge Extraction from the Federal Acquisition Regulations System (FARS)

Authors: Srishty Saha, Karuna Pande Joshi, Renee Frank, Michael Aebig, and Jiayong Lin

Book Title: 2nd International Workshop on Enterprise Big Data Semantic and Analytics Modeling at IEEE International Conference on Big Data 2017

Date: December 11, 2017

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 (Title48, CFR). Our approach can be used by Big Data Users to automate knowledge extraction from Large Legal documents.

Type: InProceedings

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 ALDA: Automated Legal Document Analytics.