Mark Greaves

Semantic Web 2.0

The Web 2.0 phenomena is poised to set the creative tone for software developers for years. It could even become a technology wave--one that the Semantic Web would do well to catch.

Amplify scientific discovery with artificial intelligence

Many human activities are a bottleneck in progress Technological innovations are penetrating all areas of science, making predominantly human activities a principal bottleneck in scientific progress while also making scientific advancement more subject to error and harder to reproduce. This is an area where a new generation of artificial intelligence (AI) systems can radically transform the practice of scientific discovery. Such systems are showing an increasing ability to automate scientific data analysis and discovery processes, can search systematically and correctly through hypothesis spaces to ensure best results, can autonomously discover complex patterns in data, and can reliably apply small-scale scientific processes consistently and transparently so that they can be easily reproduced. We discuss these advances and the steps that could help promote their development and deployment.

Inquire Biology: A Textbook that Answers Questions

Inquire Biology is a prototype of a new kind of intelligent textbook — one that answers students’ questions, engages their interest, and improves their understanding. Inquire Biology provides unique capabilities via a knowledge representation that captures conceptual knowledge from the textbook and uses inference procedures to answer students’ questions. Students ask questions by typing free-form natural language queries or by selecting passages of text. The system then attempts to answer the question and also generates suggested questions related to the query or selection. The questions supported by the system were chosen to be educationally useful, for example: what is the structure of X? compare X and Y? how does X relate to Y? In user studies, students found this question-answering capability to be extremely useful while reading and while doing problem solving. In an initial controlled experiment, community college students using the Inquire Biology prototype outperformed students using either a hardcopy or conventional E-book version of the same biology textbook. While additional research is needed to fully develop Inquire Biology, the initial prototype clearly demonstrates the promise of applying knowledge representation and question-answering technology to electronic textbooks.

Applying problem solving methods for process knowledge acquisition, representation, and reasoning

In this paper we present an approach towards knowledgeacquisition of process knowledge for the natural sciences.The work has been conducted within Project Halo, whichis creating advanced knowledge authoring and questionanswering systems for the natural sciences. An analysis of AP®-level questions for Biology, Chemistry and Physicsuncovered that process knowledge is the single most frequenttype of knowledge required. Thus, we developedmeans to acquire process knowledge, to formally representit, and to reason about it in order to answer novel questionsabout the domains.All these tasks are supported by an abstract process metamodel.It provides the terminology for user-tailored processdiagrams, which are automatically translated into executableFLogic code. The meta-model and the code generationare based on the notion of Problem Solving Methods(PSM) which represent an abstract formalization of thereasoning strategies needed for processes.

A Formalism and Method for Representing and Reasoning with Process Models Authored by Subject Matter Experts

Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82 percent were well formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25 and 30 percent with respect to the base case, respectively.

Agent Applications in Defense Logistics

During World War II, US Military logistics was the envy of the world. By Desert Storm / Desert Shield, overwhelming mass had become the supply strategy of the day. In the years following Desert Storm, the military set out to reinvent its logistics strategy through Focused Logistics and the Defense Advanced Research Projects Agency (DARPA) was charged with developing the next generation information technology to make it a reality. This chapter reviews the vision, concepts and technologies of DARPAs Advanced Logistics Project (ALP) and UltraLog Project as well as the development, experimentation, demonstration, transition and eventual commercialization of the Cognitive Agent Architecture (Cougaar).

Technosocial predictive analytics for security informatics

Challenges to the security, health, and sustainable growth of our society keep escalating asymmetrically due to the growing pace of globalization and global change. The increasing velocity of information sharing, social networking, economic forces, and environmental change has resulted in a rapid increase in the number and frequency of “game-changing moments” that a community can face. Social movements that once took a decade to build now take a year; shifts in public opinion that once took a year to take root now take a couple of months. More and more frequently, these critical moments occur too suddenly for the affected communities to succeed in countering the consequent adversities or seizing the emerging opportunities. Now more than ever, we need anticipatory reasoning technologies to forecast and manage change in order to secure and improve our way of life and the environment we inhabit. The ability to estimate the occurrence of future events using expertise, observation and intuition is critical to the human decision-making process. From a biophysical perspective, there is strong evidence that the neocortex provides a basic framework for memory and prediction in which human intelligence emerges as a process of pattern storage, recognition and projection rooted in our experience of the world and driven by perception and creativity [1]. There is increasing consensus among cognitive psychologists that human decision making can be seen as a situation-action matching process which is context-bound and driven by experiential knowledge and intuition [2-4]. Despite the natural disposition of humans towards prediction, our ability to forecast, analyze and respond to plausible futures remains one of the greatest intelligence challenges. There are well known limitations on human reasoning due to cognitive and cultural biases. Kahneman’s and Tversky’s groundbreaking work

Wikis, semantics, and collaboration: Symposium on collaboration analysis and reasoning systems, at the 2014 conference on collaboration technologies and systems

Designing software for collaborative sensemaking environments begins with a set of very challenging requirements. At a high level, the software needs to be flexible enough to support multiple lines of inquiry, contradictory hypotheses, and collaborative tasking by multiple analysts. It should also include support for managing evolving human/machine workflows and analytic products at various levels of strictness and formality, processing partial and ambiguous evidence arriving in streams, and developing explanatory scenarios based on both serendipitous and structured discovery. Eventually, it should support the analytic team as they evaluate multiple alternatives and converge on one or more consensus responses, while preserving the history and underlying reasoning. Finally, it should be delightful and simple to use, not require an inordinate degree of precision and exactness, and be quickly and inexpensively deployable in a variety of rapid-response analytic situations. It has not been possible thus far to create a single software architecture that adequately balances all these goals. However, we can shed useful light on this problem by looking at the experience of semantic wiki architectures: an emerging class of software that blends wikis, databases, social tagging systems, and Semantic Web representations.

The Growing Semantic Web

From its beginnings in 2004, the data available on the web in Semantic Web formats has typically been both eclectic and relatively small, and closely linked the interests of particular researchers. In the past year, however, the quantity and scope of data published on the public semantic web has exploded, and the size of the semantic web is now measured in the billions of assertions. It is a significant and growing resource for applications which depend on web-based resources for some or all of their knowledge. With this massive increase in quantity and scope come many opportunities, as well as the usual issues of scale on the web: inconsistency, mapping problems, incompleteness and data variability. This talk will cover the history and current state of the Semantic Web and the Linked Data Cloud, describe some of the uses to which web-based semantic data is currently put, and discuss prospects for the ECML/PKDD community to leverage this growing web of data.

Identification of program signatures from cloud computing system telemetry data

Malicious cloud computing activity can take many forms, including running unauthorized programs in a virtual environment. Detection of these malicious activities while preserving the privacy of the user is an important research challenge. Prior work has shown the potential viability of using cloud service billing metrics as a mechanism for proxy identification of malicious programs. Previously this novel detection method has been evaluated in a synthetic and isolated computational environment.