Cognitively Rich Framework to Automate Extraction and Representation of Legal Knowledge
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Abstract
With the explosive growth in cloud based services, businesses are
increasingly maintaining large datasets containing information about their
consumers to provide a seamless user experience. To ensure privacy and secu-
rity of these datasets, regulatory bodies have speci ed rules and compliance
policies that must be adhered to by organizations. These regulatory policies
are currently available as text documents that are not machine processable
and so require extensive manual e ort to monitor them continuously to ensure
data compliance. We have developed a cognitive framework to automatically
parse and extract knowledge from legal documents and represent it using an
Ontology. The framework captures knowledge in form of key terms, rules, topic
summaries, relationships between various legal terms, semantically similar ter-
minologies, deontic expressions and cross-referenced legal facts and rules. We
built the framework using Deep Learning technologies like Tensor
ow, for word
embeddings and text summarization, Gensim for topic modeling and Se- man-
tic Web technologies for building the knowledge graph. We have applied this
framework to the United States government's Code of Federal Regulations
(CFR) which includes facts and rules for individuals and organizations seek-
ing to do business with the US Federal government. In this paper we describe
our framework in detail and present results of the CFR legal knowledge base
that we have built using this framework. Our framework can be adopted by
businesses to build their automated compliance monitoring system.