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Concept Maps from RDF (Resource DescriptionFramework)Navy SBIR 2013.2 - Topic N132-128ONR - Ms. Lore Anne Ponirakis [email protected]

Opens: May 24, 2013- Closes: June 26, 2013

N132-128 TITLE: Concept Maps from RDF (Resource Description Framework)

TECHNOLOGY AREAS: Information Systems, Human Systems

ACQUISITION PROGRAM: PMW-120, PMMI (DCGS-N, DCGS-MC); FNT 14-03 Exchange of ActionableIntelligence

RESTRICTION ON PERFORMANCE BY FOREIGN CITIZENS (i.e., those holding non-U.S. Passports):

This topic is "ITAR Restricted". The information and materials provided pursuant to or resulting from thistopic are restricted under the International Traffic in Arms Regulations (ITAR), 22 CFR Parts 120 - 130,which control the export of defense-related material and services, including the export of sensitivetechnical data. Foreign Citizens may perform work under an award resulting from this topic only if theyhold the "Permanent Resident Card", or are designated as "Protected Individuals" as defined by 8 U.S.C.1324b(a)(3). If a proposal for this topic contains participation by a foreign citizen who is not in one of theabove two categories, the proposal will be rejected.

OBJECTIVE: The objective of this topic is to develop a capability to propose concept maps from verylarge RDF data stores. To meet this objective, the need exists to construct visual graphs, reorganizenodes/edges to increase readability, remove irrelevant data and prioritize content with respect to userneeds.

DESCRIPTION: The military requires affordable means to convert text and image based data toknowledge. Commercial tools exist to semantically tag entities and relationships. The focus of this topic isto take the next step and automate building of concept maps from large RDF data stores to clearly showmeaningful relationships such as human networks and behaviors/activities. The goal of this topic is todevelop machine based processes to assist human operators in making sense of large graphs derivedfrom the content of documents and video. A concept map is a graphic tool for exploring knowledge andalso gathering and sharing information [1]. They can include concepts, shown as component entitiesenclosed in circles, and relationships between concepts indicated by a connecting line. Products can takethe form of a graph, graph with hyperlinks or website pages [2]. Concept map structures are dependenton a user supplied context frame or focus question. Of particular interest for this topic is assisting militaryoperators with handling large quantities of data through automated visual representations, with reducedclutter and prioritize content to meet the needs of specific users.

Thought has to be given to the knowledge desired to meet the needs of user based on mission andtasking. Intelligence knowledge desired can take the form of know-what, know-how, know-who, andknow-why questions [3]. Structured Models, Approaches and Techniques (SMATs) can be used byintelligence analysts to identify elite leaders, locate high value individuals, map organizational structures,filter raw data for semantic content and read messages to track incidents. Automated construction ofconcept maps would provide a valuable tool to assist intelligence analysts in answering these types ofquestions. The topics research objective is to automate construction of concept maps to show thesignificance of entities and relationships extracted from series of structured and unstructured text reportsand video. Entity and association extraction has evolved to the point that the large data problem hasbecome a large graph challenge. Each document and video of an already large corpus, once structured,
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is represented by a graph containing hundreds or thousands of nodes through entity and associationextraction. A capability to move from large graphs to meaningful concept maps is critically needed.Technical challenges include the development of graph processing (RDF) techniques that considercontext (time, place and the nature of an association) and the meaning of a filtered graph relative to aconcept. The maturation of multi-dimensional clustering and word frame technologies may be relevant.The technical risk involves development of an appropriate data store/ taxonomy, graph simplificationthrough frame clustering, inferring concept maps through artificial intelligence automatically. Naturallanguage and video processing tools are able to structure the content of documents and video throughthe recognition and extraction of proper nouns (e.g. people, places) and selected other parts of speech orcontext. Structure and grammar frames have been used to classify the meaning of a sentence and/orimage. Combining the two techniques to enable a large data scalable machine understanding capability,that clearly shows meaningful relationships, can now be worked. A successful prototype wouldautomatically translate large RDF graphs into the stories they tell.

PHASE I: Develop processes and techniques to create an automated concept map; document theheuristic, machine learning and/or other methods used and show basis in scientific literature. A phase Ieffort should identify key technical risks associated with the development of a prototype and track riskreduction progress through the measurement of key technical parameters. A Phase I effort should endwith a proof of concept demonstration that bounds the size of a graph considered and the types ofconcept maps generated. The results should be put in a report and if time allows a conference or journalpublication. The final Phase I brief should show plans for Phase I Option and Phase II if selected.

PHASE II: Prototype a system that can take a question, input RDF from documents and video and outputa concept map. The prototype system will be able to automatically process, display a graph and providelinks to sources (pedigree). The system should work with little burden on operators but provide means torefine process decisions. The performer should profile a prototype system that is effective against abounded set of information questions and data sources (graphs of at least 10 million nodes). Theselection of questions and data should be consistent with those of interest to the target transitionprogram. It is possible that operational RDF of interest to the transition program will be classified secret.

PHASE III: Produce a system capable of deployment and operational evaluation that is relevant tomultiple user domains and can operate against RDF graphs of at least 100 million nodes. The system

should address topics or themes that are specific to use cases favored by the transition program andcommercial application. Machine based processing steps, metadata tags and heuristics should beaccessible by operator in human understandable form. Data input/outputs and software environmentshould be modified to operate in accordance with guidelines provided by the transition sponsor.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: The private-sector internetmarket is always interested in new ways to make sense of tagged data. Currently search engines areavailable that allow for the discovery of information based on user generated tags. This topic wouldexpand future search capabilities to discover based on machine generated tags and machine generatedconcept maps. Tagged data has caused the large data problem to become a large graph problem. Thistopic will support research to translate big graphs to relevant stories.

REFERENCES:

1. Joseph D. Novak and Alberto J. Caas, "The Theory Underlying Concept Maps and How To Constructand Use Them", Institute for Human and Machine Cognition, 2006.

2. Plotnick, Eric, "Concept Mapping: A Graphical System for Understanding the Relationship betweenConcepts", ERIC Digest, 1997.http://www.ericdigests.org/1998-1/concept.htm

3. Victor H. Ruiz, "A Knowledge Taxonomy for Army Intelligence Training: An Assessment of the MilitaryIntelligence Basic Officer Leaders Course Using Lundvalls Knowledge Taxonomy",2010https://digital.library.txstate.edu/bitstream/handle/10877/3440/fulltext.pdf?sequence=1
http://www.ericdigests.org/1998-1/concept.htmhttp://www.ericdigests.org/1998-1/concept.htmhttp://www.ericdigests.org/1998-1/concept.htmhttps://digital.library.txstate.edu/bitstream/handle/10877/3440/fulltext.pdf?sequence=1https://digital.library.txstate.edu/bitstream/handle/10877/3440/fulltext.pdf?sequence=1https://digital.library.txstate.edu/bitstream/handle/10877/3440/fulltext.pdf?sequence=1https://digital.library.txstate.edu/bitstream/handle/10877/3440/fulltext.pdf?sequence=1http://www.ericdigests.org/1998-1/concept.htm


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4. John Jones, "When Robots Write", digital Media and Learning (DML) Central, April 14,2011http://dmlcentral.net/blog/john-jones/when-robots-write

KEYWORDS: Concept Maps, RDF Stores, Knowledge Bases, Cognitive Science, MachineUnderstanding, Tagged Data

TPOC: Martin KrugerEmail:[email protected] TPOC: Scott McGirrEmail:[email protected] TPOC: Maya RubeizEmail:[email protected]
http://dmlcentral.net/blog/john-jones/when-robots-writehttp://dmlcentral.net/blog/john-jones/when-robots-writehttp://dmlcentral.net/blog/john-jones/when-robots-writemailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://dmlcentral.net/blog/john-jones/when-robots-write

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