=Paper=
{{Paper
|id=Vol-2721/paper508
|storemode=property
|title=Adventures in the Art of Enterprise Artificial Intelligence Transformation
|pdfUrl=https://ceur-ws.org/Vol-2721/paper508.pdf
|volume=Vol-2721
|authors=Mary Parmelee
|dblpUrl=https://dblp.org/rec/conf/semweb/Parmelee20
}}
==Adventures in the Art of Enterprise Artificial Intelligence Transformation==
<pdf width="1500px">https://ceur-ws.org/Vol-2721/paper508.pdf</pdf>
<pre>
Adventures in the Art of Enterprise Artificial Intelligence
                   Transformation*

                             Mary C. Parmelee1[0000-0002-7680-6997]
                      1The MITRE Corporation, Bedford MA 01730, USA

                                  mparmelee@mitre.org



        Abstract. Organizations are looking for ways to implement Artificial Intelli-
        gence (AI) in a scalable, sustainable, cost-effective way without retooling. The
        MITRE Embedded Intelligence Framework represents our experience and les-
        sons learned from a three-year, enterprise AI adventure. It provides a tested,
        end-to-end approach to implementing a semantic transformation ecosystem that
        infuses AI into enterprise systems without disrupting current processes, or prac-
        tices. We will demonstrate how we combine commercial AI cloud services
        with best of breed open source tools and Semantic Web technologies to manage
        the AI lifecycle, share AI artifacts and deliver AI services that can converse
        with us in natural language, interpret our needs, and provide quick answers our
        questions. Examples include AI-driven search, a publication recommender, an
        intelligent chatbot, and a voice-enabled virtual assistant with robotic process au-
        tomation.

        Keywords: Knowledge Graphs, SHACL, NLP, Machine Learning, RDF, En-
        terprise AI, SKOS, RDFS.


1       Description

AI-driven applications can streamline customer service, increase staff productivity,
and improve customer satisfaction. However, a major barrier to adoption of AI in the
enterprise is the lack of holistic approaches and best practices for implementing it in a
scalable, sustainable, and cost-effective way. Organizations need new, non-intrusive
AI transformation approaches that maximize flexibility, reuse, and automation while
fitting into the context of current operations and budget constraints.

   The MITRE Embedded Intelligence Framework, depicted in Figure 1, represents
our experience and lessons learned from a three-year, enterprise AI implementation
adventure. We will describe how we combine commercial AI cloud services with best
of breed open source tools and Semantic Web technologies to extract insights from
unstructured text, manage the AI lifecycle, share AI artifacts and deliver AI services
that converse with us in natural language, interpret our needs, and provide quick an-

*   Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
    License Attribution 4.0 International (CC BY 4.0)
swers our questions. Examples of AI-embedded applications include AI-driven
search, a publication recommender, an intelligent chatbot, and a voice-enabled virtual
assistant with robotic process automation.




    Fig. 1. MITRE Embedded Intelligence Framework, a semantic transformation eco-
    system for embedding artificial intelligence in enterprise applications.


2      Why Semantic Web Technologies?

Semantic Web technologies are unique in their ability to represent rich relations in
powerful, easily extensible W3C standards, like the Simple Knowledge Organization
System (SKOS) [1], Resource Description Framework (RDF) [2] and RDF Schema
(RDFS) [3]. We use these technologies to link data that is extracted from unstructured
MITRE content and labeled using machine learning and natural language processing.
By capturing relations between entities like organizations, people, topic areas, and
locations, this highly interconnected graph provides a cross-sectional view of the
enterprise uncovering new insights not previously possible without locating and read-
ing the content from multiple sources. Encoding our graphs using W3C standards also
makes them compatible with a large cache of shared vocabularies, data sets, and tools
that can further enrich potential use cases and facilitate reuse.


References
 1. RDF Homepage. https://www.w3.org/2004/02/skos/, last accessed 2020/08/10.
 2. RDFS Specification. https://www.w3.org/TR/rdf-schema/, last accessed 2020/08/10.
 3. SKOS        Simple        Knowledge         Organization      System       Homepage.
    https://www.w3.org/2004/02/skos/, last accessed 2020/08/10.

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