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SEMANTIC TECHNOLOGY FOR
INTELLIGENCE, DEFENSE, AND SECURITY

Tuesday, November 12th

09:00 - 09:40		Registration
09:40 - 17:00		Tutorials

Wednesday, November 13th

08:00 - 09:00		Registration and Breakfast
09:00 - 09:10		Initial remarks
09:10 - 09:30		Welcome
09:30 - 10:30		Keynote Address
		Dr. Benjamin Grosof Highly Expressive yet Scalable Knowledge for Intelligence, Defense, and Security    presentation (Bio)
10:30 - 11:00		Break
11:00 - 12:00		Lightning Talks   -    Chair: Kathryn Laskey
		Big Data for Combating Cyber Attacks paper        presentation	Terry Janssen, Nancy Grady SAIC
	Abstract This position paper explores a means of improving cybersecurity using Big Data technologies augmented by ontology for preventing or reducing losses from cyber attacks.Because of the priority of this threat to national security, it is necessary to attain results far superior to those found in modern day security operations centers (SOCs). Focus is on the potential application of ontology engineering to this end. Issues and potential next steps are discussed.
		Hierarchical Decision Making paper        presentation	Matthew Lewis Michigan Aerospace Corporation
	Abstract Decision making must be made within an appropriate context; we contend that such context is best represented by a hierarchy of states. The lowest levels of this hierarchy represent the observed raw data, or specific low-level behaviors and decisions. As we ascend the hierarchy, the states become increasingly abstract, representing higher order tactics, strategies, and over-arching mission goals. By representing the hierarchy using probabilistic graphical models, we can readily learn the structure and parameters that define a user's behavior by observing his activities over time--what data they use, how it is visualized, and what decisions are made. Once learned, the resulting mathematical models may be combined with the techniques of reinforcement learning to predict behavior and anticipate the needs of the user, delivering appropriate data, visualizations, and recommending optimal actions.
		Towards Context-Aware, Real Time and Autonomous Decision Making Using Information Aggregation and Network Analytics paper        presentation	Prithiviraj Dasgupta, Sanjukta Bhowmicks University of Nebraska at Omaha
	Abstract We consider the problem of real-time, proactive decision making for dynamic and time-critical decision-events where the choices made for multiple, individual decisions over time determine the final decision outcome of an event. We posit that the quality of such individual decisions can be significantly improved if human decision makers are provided with decision aids in the form of dynamically updated information and dependencies between the different decision variables, and the humans affecting those decision variables. In this position paper, we propose the CONRAD (CONtext aware Real-time Adaptive Decision making)system that uses computational techniques from large scale network analysis and game theory-based distributed information aggregation to develop such decision aids. CONRAD's functionalities are implemented through three subsystems--a decision making subsystem that updates and mathematically combines information from different decision variables to predict the outcome of the decision event, a decision assessment subsystem that uses the currently predicted decision outcome to estimate the future decision trajectory and recommends information collection-related actions to the human decision maker, and, a network analysis subsystem that uses those recommended actions to dynamically update the dependencies and correlations between events and people influencing the decision variables. To the best of our knowledge, our work is one of the first attempts towards combining dynamic decision updates and using the predicted decision trajectory as a proactive feedback mechanism to dynamically update the correlations between decision variables so that human decision makers can make more strategically informed and well-aligned decisions towards the desired outcome of decision events.
		Need for Community of Interest for Context in Applied Decision Making paper        presentation	Peter S. Morosoff Electronic Mapping Systems, Inc.
	Abstract There is interest in building a community of interest for Context in Applied Decision Making. Warfighters have long exploited context in decision making. The mystery, therefore, is why the information technology (IT) community that supports warfighters provides so little IT that exploits context for decision making. One possible answer is the lack of a forum such as a community of interest that facilitates sharing (a) among those who do or might develop IT that exploits context for decision making and (b) with warfighters. This paper provides background information on warfighter's use of context and highlights an IT system that uses computer representations of context in order to facilitate establishing a community for Context in Applied Decision Making.
12:00 - 13:30		Lunch
13:30 - 15:00		Parallel Sessions 1
		Session 1A: Uncertainty Representation   -   Chair: Ranjeev Mittu
13:30 - 14:00		Context Correlation Using Probabilistic Semantics paper	Setareh Rafatirad, Kathryn Laskey, Paulo Costa George Mason University
	Abstract We present an approach for recognizing high-level geo-temporal phenomena, referred to as events/occurrences, from in-depth discovery of information, using geo-tagged photos,formal event models, and various context cues like weather, space, time, and people. Due to the relative availability of information, our approach automatically obtains a probabilistic measure of occurrence likelihood for the recognized geo-temporal phenomena. This measure, however, is not only used to find the best event among the merely possible candidates Š witnessing the data (including photos), but it can also provide informative cues to human operators in the environments where uncertainty is involved in the existing knowledge.
14:00 - 14:30		A Reference Architecture for Probabilistic Ontology Development paper        presentation	Richard Haberlin (a), Paulo Costa (b), Kathryn Laskey (b) (a) EMSolutions Inc., (b) George Mason University
	Abstract The use of ontologies is on the rise, as they facilitate interoperability and provide support for automation. Today, ontologies are popular for research in areas such as the Semantic Web, knowledge engineering, artificial intelligence and knowledge management. However, many real world problems in these disciplines are burdened by incomplete information and other sources of uncertainty which traditional ontologies cannot represent. Therefore, a means to incorporate uncertainty is a necessity. Probabilistic ontologies extend current ontology formalisms to provide support for representing and reasoning with uncertainty. Representation of uncertainty in real-world problems requires probabilistic ontologies, which integrate the inferential reasoning power of probabilistic representations with the first-order expressivity of ontologies. This paper introduces a systematic approach to probabilistic ontology development through a reference architecture which captures the evolution of a traditional ontology into a probabilistic ontology implementation for real-world problems. The Reference Architecture for Probabilistic Ontology Development catalogues and defines the processes and artifacts necessary for the development, implementation and evaluation of explicit, logical and defensible probabilistic ontologies developed for knowledge-sharing and reuse in a given domain.
14:30 - 15:00		Focused Belief Measures for Uncertainty Quantification in High Performance Semantic Analysis paper	Cliff Joslyn, Jesse Weaver Pacific Northwest National Laboratory
	Abstract In web-scale semantic data analytics there is a great need for methods which aggregate uncertainty claims, on the one hand respecting the information provided as accurately as possible, while on the other still being tractable. Traditional statistical methods are more robust, but only represent distributional,additive uncertainty. Generalized information theory methods, including fuzzy systems and Dempster-Shafer(DS) evidence theory, represent multiple forms of uncertainty, but are computationally and methodologically difficult. We require methods which provide an effective balance between the complete representation of the full complexity of uncertainty claims in their interaction, while satisfying the needs of both computational complexity and human cognition. Here we build on Jøsang's subjective logic to posit methods in focused belief measures(FBMs), where a full DS structure is focused to a single event. The resulting ternary logical structure is posited to be able to capture the minimally sufficient amount of generalized complexity needed at a maximum of computational efficiency. We demonstrate the efficacy of this approach in a web ingest experiment over the2012 Billion Triple dataset from the Semantic Web Challenge.
		Session 1B: Intelligence Analysis   -   Chair: Katherine Goodier
13:30 - 14:00		Recognizing and Countering Biases in Intelligence Analysis with TIACRITIS paper	Gheorghe Tecuci, David Schum, Dorin Marcu, Mihai Boicu George Mason University
	Abstract This paper discusses different biases which have been identified in Intelligence Analysis and how TIACRITIS, a knowledge-based cognitive assistant for evidence-based hypotheses analysis, can help recognize and partially counter them. After reviewing the architecture of TIACRITIS, the paper shows how it helps recognize and counter many of the analysts' biases in the evaluation of evidence, in the perception of cause and effect, in the estimation of probabilities, and in the retrospective evaluation of intelligence reports. Then the paper introduces three other types of bias that are rarely discussed, biases of the sources of testimonial evidence, biases in the chain of custody of evidence, and biases of the consumers of intelligence, which can also be recognized and countered with TIACRITIS.
14:00 - 14:30		IAO-Intel: An Ontology of Information Artifacts in the Intelligence Domain paper        presentation	Barry Smith (a), Tatiana Malyuta (b) Ron Rudnicki (c), William Mandrick (d), David Salmen (d), Peter Morosoff (e), Danielle K. Duff (f), James Schoening (f), Kesny Parent (f) (a) University at Buffalo, (b) CUNY & Data Tactics, (c) CUBRC, (d) Data Tactics, (e) Electronic Mapping Systems, Inc., (f) I2WD
	Abstract We describe on-going work on IAO-Intel, an information artifact ontology developed as part of a suite of ontologies designed to support the needs of the US Army intelligence community within the framework of the Distributed Common Ground System (DCGS-A). IAO-Intel provides a controlled, structured vocabulary for the consistent formulation of metadata about documents, images, emails and other carriers of information. It will provide a resource for uniform explication of the terms used in multiple existing military dictionaries, thesauri and metadata registries, thereby enhancing the degree to which the content formulated with their aid will be available to computational reasoning.
14:30 - 15:00		Managing Semantic Big Data for Intelligence paper        presentation	Anne-Claire Boury-Brisset Defence Research and Development, Canada
	Abstract All-source intelligence production involves the collection and analysis of intelligence data provided in various formats (raw data from sensors, imagery, text-based from human reports, etc.) and distributed across heterogeneous data stores. The advance in sensing technologies, the acquisition of new sensors, and use of mobile devices result in the production of an overwhelming amount of sensed data, that augment the challenges to transform these raw data into useful, actionable intelligence in a timely manner. Leveraging recent advances in data integration, Semantic Web and Big Data technologies, we are adapting key concepts of unified dataspaces and semantic enrichment for the design and implementation of a R&D intelligence data integration platform MIDIS (Multi-Intelligence Data Integration System). The development of this scalable data integration platform rests on the layered dataspace approach, makes use of recent Big Data technologies and leverages ontological models, and semantic-based analysis services developed for various purposes as part of the semantic layer.
15:00 - 15:30		Break
15:30 - 17:00		Parallel Sessions 2
		Session 2A: Context-Driven Decision Making  - Chair: Ciara Sibley
15:30 - 16:00		Context as a Cognitive Process: An Integrative Framework for Supporting Decision Making paper	Wayne Zachary (a), Andrew Rosoff (a), Stephen Read (b), Lynn Miller (b) (a) CHI Systems, Inc., (b) Univ of So California
	Abstract Multiple lines of research in cognitive science have brought insight on the role that internal (cognitive) representations of situational context play in framing decision making and in differentiating expert versus novice decision performance. However, no single framework has emerged to integrate these lines of research, particularly the views from narrative reasoning research and those from situation awareness and recognition-primed decision research. The integrative framework presented here focuses on the cognitive processes involved in developing and maintaining context understanding,rather than on the content of the context representation at any given moment. The Narratively-Integrated Multilevel (NIM)framework views context development as an on-going and self-organizing process in which a set of knowledge elements, rooted in individual experience and expertise, construct and maintain a declarative, hierarchical representation of the situational context.The context representation that arises from this process is then shown to be the central point of both situational interpretation and decision-making processes at multiple levels, from achieving specific local goals to pursuing broad motives in a domain or theater of action.
16:00 - 16:30		Towards a Context-Aware Proactive Decision Support Framework paper	Benjamin Newsom (a), Ranjeev Mittu (b) Next Century Corporation (a), U.S. Naval Research Lab (b)
	Abstract The problem of automatically recognizing context,the implications of its shifting properties, and reacting in a dynamic manner is at the core of mission intelligence and decision making. Operational environments, such as the OZONE Widget Framework, provide the foundation for capturing the objectives, actions and activities of the mission analyst and decision maker. By utilizing a "context container" that envelops an OZONE Application, we can capture both action and intent which allows us to characterize this context with respect to its operational modality (strategic, tactical, opportunistic, or random).
16:30 - 17:00		Dynamic Data Relevance Estimation by Exploring Models (D2REEM) paper        presentation	H. Van Dyke Parunak Soar Technology, Inc.
	Abstract Analysts in many areas of national security face a massive (high volume), dynamically changing (high velocity) flood of possibly relevant information. Identifying reasonable suspects confronts a tension between data that is too atomic to be diagnostic and knowledge that is too complex to guide search. D2REEM (Dynamic Data Relevance Estimation by Exploring Models) is a knowledge-based metaheuristic that uses stochastic search of a graph-based semantic model to guide successive queries of high-volume, high-velocity data. We motivate D2REEM by considering the nature of knowledge-based search in high-volume, high-velocity data and reviewing current tools. We then outline the D2REEM metaheuristic and describe the state of progress in applying it to a range of model types, including geospatial movement, behavioral models, discourse models, narrative generators, and social networks. Finally, we outline work that needs to be done to advance the D2REEM agenda.
		Session 2B: Social Media   -   Chair: Setareh Rafatirad
15:30 - 16:00		Data Analytics to Detect Money Laundering Evolution paper	Murad Mehmet, Duminda Wijesekera George Mason University
	Abstract Money laundering evolves using multiple layers of trade, multi trading methods and uses multiple components in order to evade detection and prevention techniques. Consequently, detecting money laundering requires an analytical framework that can handle large amounts of unstructured, semistructured and transactional data that stream at transactional speeds to detect business-complexities, and discover deliberately concealed relationships. Based on our prior work and a static risk model proposed in the Bank Security Act, we propose a dynamic risk model that assigns a risk score for every transaction being a potential component of a larger money-laundering scheme. We use social networks to connect missing links in such potential transaction sequences. Taken together we can provide a financial sector independent risk assessment to submitted transactions.The proposed risk model is validated using data from realistic scenarios and our already developed money laundering evolution detection framework (MLEDF) that we developed earlier using sequence matching, case-based analysis, social networks, and complex event processing to link fraudulent transaction trails. MLEDF has components to collect data, run them against business rules and evolution models, run detection algorithms and use social network analysis to connect potential participants.
16:00 - 16:30		Extraction of Semantic Activities from Twitter Data paper	Aleksey Panasyuk (a), Erik Blasch (a), Sue E. Kase (b), Liz Bowman (b) (a) Air Force Research Lab, (b) Army Research Lab
	Abstract With the growing popularity of Twitter, numerous issues surround the usefulness of the technology for intelligence, defense, and security. For security, Twitter provides a real-time opportunity to determine unrest and discontent. For defense, twitter can be a source of open-source intelligence (INT) information related to areas of contested environments. However, the semantic content, location of tweets, and richness of the information requires big data analysis for understanding the use of the information for intelligence. In this paper, we describe some results in using extraction features from twitter data to determine events, the semantic implications of the results from the data, as well as discuss pragmatic uses of twitter data for multi-INT data fusion. The results collected during the period of Egypt Arab spring conclude that (1) many tweets are clutter or noise in analysis, (2) location information does not always convey the geometrical accuracy of the information, and (3) the aggregate processing of the twitter data results in real-time trends of possible events that warrant more conventional information gathering.
16:30 - 17:00		Situational Awareness from Social Media paper	Brian Ulicny (a), Jakub Moskal (a), Mieczyslaw M. Kokar (b) (a) VIStology, Inc, (b) Northeastern University
	Abstract This paper describes VIStology's HADRian system for semantically integrating disparate information sources into a common operational picture (COP) for humanitarian assistance/disaster relief (HADR) operations. Here the system is applied to the task of determining where unexploded or additional bombs were being reported via Twitter in the hours immediately after the Boston Marathon bombing in April, 2013. We provide an evaluation of the results and discuss future directions.
17:00 - 18:30		Poster Session / Social Event

Thursday, November 14th

08:30 - 09:15		Breakfast
09:15 - 09:30		Announcements
09:30 - 10:30		Keynote Address
		Dr. Jeffrey Morrison Exploring the Role of Context in Applied Decision Making    (Bio)
10:30 - 11:00		Break
11:00 - 12:00		Parallel Session 3
		Session 3A: Cyber Operations   -   Chair: Brian Ulicny
11:00 - 11:30		Towards a Cognitive System for Decision Support in Cyber Operations paper	Alessandro Oltramari (a), Christian Lebiere (a), Lowell Vizenor (b), Wen Zhu (c), Randall Dipert (d) (a) Carnegie Mellon Univ, (b) Refinary 29, (c) Alion Science and Tech, (d) University of Buffalo
	Abstract This paper presents the general requirements to build a "cognitive system for decision support", capable of simulating defensive and offensive cyber operations. We aim to identify the key processes that mediate interactions between defenders, adversaries and the public, focusing on cognitive and ontological factors. We describe a controlled experimental phase where the system performance is assessed on a multi-purpose environment, which is a critical step towards enhancing situational awareness in cyber warfare.
11:30 - 12:00		Using a Semantic Approach to Cyber Impact Assessment paper	Alexandre de Barros Barreto (a), Paulo Cesar G Costa (b), Edgar Toshiro Yano (a) (a) Instituto Technológico      de Aeronáutica, Brazil, (b) George Mason University
	Abstract The use of cyberspace as a platform for military operations presents many new research challenges. This paper focuses on the specific problem of assessing the impact of an event in the cyber domain (e.g. a cyber attack) on the missions it supports. The approach involves the use of Cyber-ARGUS, a C2 simulation framework, along with semantic technologies to provide consistent mapping between domains. Relevant information is stored in a semantic knowledge base about the nodes in the cyber domain, and then used to build a Bayesian network to provide impact assessment. The technique is illustrated through the simulation of an air transportation scenario in which the C2 infrastructure is subjected to various cyber attacks, and their associated impact to the operations is assessed.
		Session 3B: Alternative Representations   -   Chair: Charles Twardy
11:00 - 11:30		Analyzing Military Intelligence Using Interactive Semantic Queries paper	Rod Moten Sotera Defense Solutions, Inc.
	Abstract We describe a strategy for performing semantic searches for analyzing military intelligence. Our strategy allows the analyst and the query engine to work together to reduce a complex query into simpler queries. The answers for the simpler queries are combined into answers for the original query. The queries can be refined using rules defined by the analyst or analytics created by a data scientist. Our strategy uses an alternative approach to semantic modeling than the state-of-the-art approaches based on OWL. OWL is an implementation of a branch of mathematical logics designed specifically for semantic modeling called description logics. Our strategy uses a branch of mathematical logics called type theory. We use type theory because of the long history of developing systems based on type theory for reasoning interactively. We demonstrate with an example how the strategy can be used to answer questions posed by analysts that couldn't be answered using conventional methods.
11:30 - 12:00		Sketches, Views and Pattern-Based Reasoning paper        presentation	Ralph L. Wojtowicz Baker Mountain Research Corp and Shepherd University
	Abstract The mathematical theory of sketches provides a graphical framework for describing and relating knowledge representations and their models. Maps between sketches can extract domain-specific context from a sketch, express knowledge dynamics and be used to manage representations created for distinct applications or by different analysts. There are precise connections between classes of sketches and fragments of first order, infinitary predicate logic. EA sketches are a particular class that is related to entity-attribute-relation diagrams and can be implemented using features available in many relational database systems. In this paper we illustrate sketch theory through development of a simple human terrain model. We apply the theory to an example of aligning sketch-based knowledge representations and compare the approach to one using OWL/RDF. We describe the computational infrastructure that is available for working with sketches and outline research challenges.
12:00 - 13:30		Lunch
13:30 - 14:30		Parallel Session 4
		Session 4A: Access-Control   -   Chair: Bill Mandrick
13:30 - 14:00		An Ontological Inference Driven IVR System paper	Mohammad Ababneh, Duminda Wijesekera George Mason University
	Abstract Someone seeking entry to an access controlled facility or through a border control point may face an in person interview. Questions that may be asked in such an interview may depend on the context and vary in detail. One of the issues that interviewers face is to ask relevant questions that would enable them to either accept or reject entrance. Repeating questions asked at entry point interviews may render them useless because most interviewees may come prepared to answer common questions. As a solution, we present an interactive voice response system that can generate a random set of questions that are contextually relevant, of the appropriate level of difficulty and not repeated in successive question answer sessions. Furthermore, our system will have the ability to limit the number of questions based on the available time, degree of difficulty of generated questions or the desired subject concentration. Our solution uses Item Response Theory to select questions from a large item bank generated by inferences over multiple distributed ontologies.
14:00 - 14:30		Fast Semantic Attribute-Role-Based Access Control (ARBAC) paper	Leo Obrst, Dru McCandless, David Ferrell MITRE Corporation
	Abstract We report on our research effort, called Fast Semantic Attribute-Role-Based Access Control (ARBAC), to develop a semantic platform-independent framework enabling information originators and security administrators to specify access rights to information consistently and completely, in a social network environment, and then to rigorously enforce that specification. We use a modified ARBAC security model and an OWL ontology with additional rules in a logic programming and Java framework to express access policy, going beyond the limitations of previous attempts in this vein. We also experimented with knowledge compilation optimizing techniques that allow access policy constraint checking to be implemented in real-time, via a bit-vector encoding that can be used for rapid run-time reasoning.
		Session 4B: Crisis Management   -   Chair: Alessandro Oltramari
13:30 - 14:00		Supporting Evacuation Missions with Ontology-based SPARQL Federation paper        presentation	Audun Stolpe, Jonas Halvorsen, Bjørn Jervell Hansen Norwegian Defence Research Establishment (FFI)
	Abstract We study ontology-based SPARQL federation in support of coordinated action by deployed units in military operations.It is presumed that bandwidth is limited and unstable.Thus, we need an approach that generates few HTTP requests.Existing techniques employ join-order heuristics that may cause requests to multiply as a factor of the number of joins in a query. This can easily lead to an amount of traffic that exceeds network capacity. We propose an approach that builds an in-memory excerpt of the remote sources, sending one request to each source. A query is answered against this excerpt, which is a provably sound and complete representation of the sources wrt. query answering. The paper ends with a case study involving three military sources used for planning evacuation missions.
14:00 - 14:30		Navigation Assistance Framework for Emergency paper	Paul Ngo, Duminda Wijesekera George Mason University
	Abstract Emergencies occur every day at unexpected times and impact our lives in unimaginable ways. In any emergency situation, there are two type of victims: direct victims and indirect victims. Both will have their current plans disrupted in order to deal with the emergency. Federal, State, and Local government shave established a 911 system to assist direct victims. However, there is still lack of assistance provided to the indirect victims. In this paper, we propose a Navigation Assistance Framework that allows emergency organizations to provide emergency information that can assist victims navigating out of the emergency area and reaching their intended destinations in a reasonable amount of time. We develop an emergency prototype ERSimMon to simulate this capability in a small scale to show the effectiveness of the proposed solution. In addition, we develop the Emergency Response Application (ERApp) for a smart phone platform, which intercepts the enhanced Commercial Mobile Alert System(CMAS) broadcast message, displays the userÕs location with respect to the emergency location on the map and provides navigational assistance and recommend actions to help the user navigate out of ongoing emergencies.
14:30 - 15:00		Break
15:00 - 16:30		Panel Discussion Semantic Technologies for Big Data   -   Moderator: Ian Emmons
		Anne-Claire Boury-Brisset
	Short Bio Anne-Claire Boury-Brisset holds a Ph.D. in computer science (artificial intelligence) from the University of Nancy (France). She works as a defense scientist at Defence Research and Development Canada for 15 years. She is part of the Command Control and Intelligence section, and group leader All-source Intelligence Management. Her research areas focus on information and knowledge management, ontologies and semantic technologies, applied to various command and control, and intelligence application problems. She currently leads an applied research project dealing with All-source intelligence information integration and management.
		Ian Emmons
	Short Bio Ian Emmons has over 22 years of software development experience concentrated in various forms of data management, with more than 8 years of experience in the Semantic Web community. His Semantic Web experience has emphasized semantic alignment of multi-source data, coreference resolution, semantic storage and query, and processing of temporal data. Prior to that, his interests included transactional data caching and replication, concurrent processing techniques, object-to-relational mapping, document management, and natural language-assisted information retrieval. His background includes an MA in Mathematics and a BS in Physics (University of Rochester), 3 patents, and 5 publications.
		Benjamin Grosof
	Short Bio Benjamin Grosof is an industry leader in knowledge representation, reasoning, and acquisition. He has pioneered semantic technology and industry standards for rules, the combination of rules with ontologies, the applications of rules in e-commerce and policies, and the acquisition of rules and ontologies from natural language (NL). He has had driving roles in RuleML, W3C RIF (Rule Interchange Format), and W3C OWL-RL (rule-based ontologies). He led the invention of several fundamental technical advances in knowledge representation, including courteous defeasibility, restraint bounded rationality, and the rule-based technique which rapidly became the currently dominant approach to commercial implementation of OWL. He has extensive experience in machine learning, probabilistic reasoning, and user interaction design. Dr. Grosof has experience applying core technology for knowledge, reasoning, and related HCI in a wide variety of application areas, including: trust/privacy/security, contracts, compliance, legal, and services engineering; financial/ insurance services, risk management,and regulations; defense and national intelligence; biomedical research; and data/ decision analytics. From fall 2007 to early 2013, he led a large research program in Artificial Intelligence (AI) and rule-based semantic technologies at Vulcan Inc. for Paul G. Allen; this centered around the SILK system for highly expressive, yet scalable, rules. Previously he was an IT professor at MIT Sloan (2000-2007) and a senior software scientist at IBM Research (1988-2000). He is president of the expert consulting firm Benjamin Grosof & Associates founded while he was at MIT, and co-founder of the recent start-up Coherent Knowledge Systems. His background includes 4 major industry software releases, 2 years in software startups, a Stanford PhD (Computer Science), a Harvard BA (Applied Mathematics), 2 patents, and over 50 refereed publications.
		Terry Janssen
	Short Bio Terry Janssen
		Larry Kerschberg
	Short Bio Larry Kerschberg received his PhD from Case Western Reserve University in Systems Engineering. His MS in Electrical Engineering is from the University of Wisconsin-Madison, and his BS degree in Engineering Science is from Case Institute of Technology. He has taught at the Catholic University of Rio de Janeiro, the University of Maryland-College Park, the University of South Carolina-Columbia, and, for the last 25 years at George Mason University. His research areas include the functional data model, expert database systems, intelligent integration of information, large-scale information architectures, and semantic search. He has published numerous papers in journals and conferences, and holds two patents in the area of semantic search technology. His work has been funded by NASA, DARPA and the National Geospatial-Intelligence Agency (NGA). He has served as Co-Editor-in-Chief of the Journal of Intelligent Information Systems, published by Springer, since 1992.
16:30 - 16:45		Future of STIDS   -   Paulo Costa
16:45 - 17:00		Wrap Up