Keynote Speech (1)

Keynote Speech (2)

Keynote Speech (3)

From Search Engines to Question-Answering Systems - The Problems of World Knowledge, Relevance, Deduction and Precisiation

Lotfi A. Zadeh

Professor in the Graduate School, Computer Science Division
Department of Electrical Engineering and Computer Sciences
University of California, Berkeley, CA 94720 -1776, USA
Director, Berkeley Initiative in Soft Computing (BISC)

Abstract

Existing search engines, with Google at the top, have many truly remarkable capabilities. Furthermore, constant progress is being made in improving their performance. But what is not widely recognized is that there is a basic capability which existing search engines do not have: deduction capability—the capability to synthesize an answer to a query by drawing on bodies of information which reside in various parts of the knowledge base. By definition, a question-answering system, or a Q/A system for short, is a system which has deduction capability. Can a search engine be upgraded to a question-answering system through the use of existing tools—tools which are based on bivalent logic and probability theory? A view which is articulated in the following is that the answer is: No.

The first obstacle is world knowledge—the knowledge which humans acquire through experience, communication and education. Simple examples are: “Icy roads are slippery,” “Princeton usually means Princeton University,” “Paris is the capital of France,” and “There are no honest politicians.” World knowledge plays a central role in search, assessment of relevance and deduction. The problem with world knowledge is that it is, for the most part, perception-based. Perceptions—and especially perceptions of probabilities—are intrinsically imprecise, reflecting the fact that human sensory organs, and ultimately the brain, have a bounded ability to resolve detail and store information. Imprecision of perceptions stands in the way of using conventional techniques—techniques which are based on bivalent logic and probability theory—to deal with perception-based information. A further complication is that much of world knowledge is negative knowledge in the sense that it relates to what is impossible and/or non-existent. For example, “A person cannot have two fathers,” and “Netherlands has no mountains.”

The second obstacle centers on the concept of relevance. There is an extensive literature on relevance, and every search engine deals with relevance in its own way, some at a high level of sophistication. But what is quite obvious is that the problem of assessment of relevance is quite complex and far from solution.

There are two kinds of relevance: (a) question relevance and (b) topic relevance. Both are matters of degree. For example, on a very basic level, if the question is q: “Number of cars in California?” and the available information is p: “Population of California is 37,000,000,” then what is the degree of relevance of p to q? Another example: To what degree is a paper entitled “A New Approach to Natural Language Understanding” of relevance to the topic of machine translation.

Basically, there are two ways of approaching assessment of relevance: (a) semantic; and (b) statistical. To illustrate, in the number of cars example, relevance of p to q is a matter of semantics and world knowledge. In existing search engines, relevance is largely a matter of statistics, involving counts of links and words, with little if any consideration of semantics. Assessment of semantic relevance presents difficult problems whose solutions lie beyond the reach of bivalent logic and probability theory. What should be noted is that assessment of topic relevance is more amendable to the use of statistical techniques, which explains why existing search engines are much better at assessment of topic relevance then question relevance.

The third obstacle is deduction from perception-based information. As a basic example, assume that the question is q: What is the average height of Swedes?, and the available information is p: Most adult Swedes are tall. Another example is: Usually Robert returns from work at about 6pm. What is the probability that Robert is at home at 6:15 pm? Neither bivalent logic nor probability theory provide effective tools for dealing with problems of this type. The difficulty is centered on deduction from premises which are both uncertain and imprecise. 

Underlying the problems of world knowledge, relevance and deduction is a very basic problem—the problem of natural language understanding. Much of world knowledge and web knowledge is expressed in a natural language. A natural language is basically a system for describing perceptions. Since perceptions are intrinsically imprecise, so are natural languages.

A prerequisite to mechanization of question-answering is mechanization of natural language understanding, and a prerequisite to mechanization of natural language understanding is precisiation of meaning of concepts and proposition drawn from a natural language. To deal effectively with world knowledge, relevance, deduction and precisiation, new tools are needed. The principal new tools are: Precisiated Natural Language (PNL); Protoform Theory (PFT); and the Generalized Theory of Uncertainty (GTU). These tools are drawn from fuzzy logic—a logic in which everything is, or is allowed to be, a matter of degree.

The centerpiece of the new tools is the concept of a generalized constraint. The importance of the concept of a generalized constraint derives from the fact that in PNL and GTU it serves as a basis for generalizing the universally accepted view that information is statistical in nature. More specifically, the point of departure in PNL and GTU is the fundamental premise that, in general, information is representable as a system of generalized constraints, with statistical information constituting a special case. This, much more general, view of information is needed to deal effectively with world knowledge, relevance, deduction, precisiation and related problems.

In summary, the principal objectives of this paper are: (a) to make a case for the view that a quantum jump in search engine IQ cannot be achieved through the use of methods based on bivalent logic and probability theory; and (b) to introduce and outline a collection of non-standard concepts, ideas and tools which are needed to achieve a quantum jump in search engine IQ.

Bio

Lotfi A. Zadeh joined the Department of Electrical Engineering at the University of California, Berkeley, in 1959, and served as its chairman from 1963 to 1968. Earlier, he was a member of the electrical engineering faculty at Columbia University. In 1956, he was a visiting member of the Institute for Advanced Study in Princeton, New Jersey. In addition, he held a number of other visiting appointments, among them a visiting professorship in Electrical Engineering at MIT in 1962 and 1968; a visiting scientist appointment at IBM Research Laboratory, San Jose, CA, in 1968, 1973, and 1977; and visiting scholar appointments at the AI Center, SRI International, in 1981, and at the Center for the Study of Language and Information, Stanford University, in 1987-1988. Currently he is a Professor in the Graduate School, and is serving as the Director of BISC (Berkeley Initiative in Soft Computing).

Until 1965, Dr. Zadeh's work had been centered on system theory and decision analysis. Since then, his research interests have shifted to the theory of fuzzy sets and its applications to artificial intelligence, linguistics, logic, decision analysis, control theory, expert systems and neural networks. Currently, his research is focused on fuzzy logic, soft computing, computing with words, and the newly developed computational theory of perceptions and precisiated natural language.

An alumnus of the University of Tehran, MIT, and Columbia University, Dr. Zadeh is a fellow of the IEEE, AAAS, ACM, AAAI and IFSA, and a member of the National Academy of Engineering. He held NSF Senior Postdoctoral Fellowships in 1956-57 and 1962-63, and was a Guggenheim Foundation Fellow in 1968. Dr. Zadeh was the recipient of the IEEE Education Medal in 1973 and a recipient of the IEEE Centennial Medal in 1984. In 1989, Dr. Zadeh was awarded the Honda Prize by the Honda Foundation, and in 1991 received the Berkeley Citation, University of California.

In 1992, Dr. Zadeh was awarded the IEEE Richard W. Hamming Medal "For seminal contributions to information science and systems, including the conceptualization of fuzzy sets." He became a Foreign Member of the Russian Academy of Natural Sciences (Computer Sciences and Cybernetics Section) in 1992, and received the Certificate of Commendation for AI Special Contributions Award from the International Foundation for Artificial Intelligence. Also in 1992, he was awarded the Kampe de Feriet Prize and became an Honorary Member of the Austrian Society of Cybernetic Studies.

In 1993, Dr. Zadeh received the Rufus Oldenburger Medal from the American Society of Mechanical Engineers "For seminal contributions in system theory, decision analysis, and theory of fuzzy sets and its applications to AI, linguistics, logic, expert systems and neural networks." He was also awarded the Grigore Moisil Prize for Fundamental Researches, and the Premier Best Paper Award by the Second International Conference on Fuzzy Theory and Technology. In 1995, Dr. Zadeh was awarded the IEEE Medal of Honor "For pioneering development of fuzzy logic and its many diverse applications." In 1996, Dr. Zadeh was awarded the Okawa Prize "For outstanding contribution to information science through the development of fuzzy logic and its applications."

In 1997, Dr. Zadeh was awarded the B. Bolzano Medal by the Academy of Sciences of the Czech Republic "For outstanding achievements in fuzzy mathematics." He also received the J.P. Wohl Career Achievement Award of the IEEE Systems, Science and Cybernetics Society. He served as a Lee Kuan Yew Distinguished Visitor, lecturing at the National University of Singapore and the Nanyang Technological University in Singapore, and as the Gulbenkian Foundation Visiting Professor at the New University of Lisbon in Portugal. In 1998, Dr. Zadeh was awarded the Edward Feigenbaum Medal by the International Society for Intelligent Systems and the Richard E. Bellman Control Heritage Award by the American Council on Automatic Control. In addition, he received the Information Science Award from the Association for Intelligent Machinery and the SOFT Scientific Contribution Memorial Award from the Society for Fuzzy Theory in Japan. In 1999, he was elected to membership in Berkeley Fellows and received the Certificate of Merit from IFSA (International Fuzzy Systems Association). In 2000, he received the IEEE Millennium Medal; the IEEE Pioneer Award in Fuzzy Systems; the ASPIH 2000 Lifetime Distinguished Achievement Award; and the ACIDCA 2000 Award for the paper, "From Computing with Numbers to Computing with Words—From Manipulation of Measurements to Manipulation of Perceptions." In addition, he received the Chaos Award from the Center of Hyperincursion and Anticipation in Ordered Systems for his outstanding scientific work on foundations of fuzzy logic, soft computing, computing with words and the computational theory of perceptions. In 2001, Dr. Zadeh received the ACM 2000 Allen Newell Award for seminal contributions to AI through his development of fuzzy logic. In addition, he received a Special Award from the Committee for Automation and Robotics of the Polish Academy of Sciences for his significant contributions to systems and information science, development of fuzzy sets theory, fuzzy logic control, possibility theory, soft computing, computing with words and computational theory of perceptions. In 2003, Dr. Zadeh was elected as a foreign member of the Finnish Academy of Sciences, and received the Norbert Wiener Award of the IEEE Society of Systems, Man and Cybernetics “For pioneering contributions to the development of system theory, fuzzy logic and soft computing.” In 2004, Dr. Zadeh was awarded Civitate Honoris Causa by Budapest Tech (BT) Polytechnical Institution, Budapest, Hungary. Also in 2004, he was awarded the V. Kaufmann Prize, International Association for Fuzzy-Set Management and Economy (SIGEF).

Dr. Zadeh is a recipient of twenty-three honorary doctorates from: Paul-Sabatier University, Toulouse, France; State University of New York, Binghamton, NY; University of Dortmund, Dortmund, Germany; University of Oviedo, Oviedo, Spain; University of Granada, Granada, Spain; Lakehead University, Canada; University of Louisville, KY; Baku State University, Azerbaijan; the Silesian Technical University, Gliwice, Poland; the University of Toronto, Toronto, Canada; the University of Ostrava, the Czech Republic; the University of Central Florida, Orlando, FL; the University of Hamburg, Hamburg, Germany; the University of Paris(6), Paris, France; Jahannes Kepler University, Linz, Austria; University of Waterloo, Canada; and the University of Aurel Vlaicu, Arad, Romania; Lappeenranta University of Technology, Lappeenranta, Finland; Muroran Institute of Technology, Muroran, Japan; Hong Kong Baptist University, Hong Kong, China.

Dr. Zadeh has single-authored over two hundred papers and serves on the editorial boards of over fifty journals. He is a member of the Advisory Committee, Center for Education and Research in Fuzzy Systems and Artificial Intelligence, Iasi, Romania; Senior Advisory Board, International Institute for General Systems Studies; the Board of Governors, International Neural Networks Society; and is the Honorary President of the Biomedical Fuzzy Systems Association of Japan and the Spanish Association for Fuzzy Logic and Technologies. In addition, he is a member of the Advisory Board of the National Institute of Informatics, Tokyo; a member of the Governing Board, Knowledge Systems Institute, Skokie, IL; and an honorary member of the Academic Council of NAISO-IAAC.

Professor in the Graduate School and Director, Berkeley Initiative in Soft Computing (BISC), Computer Science Division, Department of EECS, University of California, Berkeley, CA 94720-l776; Telephone: 5l0-642-4959; Fax: 5l0-642-l7l2; E-mail: zadeh@eecs.berkeley.edu
http://www.cs.berkeley.edu/~zadeh/

Research supported in part by ONR N00014-02-1-0294, BT Grant CT1080028046, Omron Grant, Tekes Grant and the BISC Program of UC Berkeley.

Granular Computing in Knowledge Integration and Reuse

Witold Pedrycz

Dept. of Electrical & Computer Engineering
University of Alberta, Edmonton, Canada
&
Systems Research Institute
Polish Academy of Sciences, Warsaw, Poland

Abstract

Granular Computing has emerged as a coherent and comprehensive framework for representing and processing heterogeneous pieces of information. Information granules regarded as a direct manifestation of the fundamental mechanisms of abstraction and its practical realization lie in the heart of processes of knowledge acquisition and management.

In this talk, we briefly review the main features of Granular Computing, elaborate on the underlying formalisms of information granules (which include constructs such as fuzzy sets, rough sets, interval analysis, and shadowed sets, just to name the most visible representatives) and briefly report on their essential similarities and striking differences. Owing to the evident heterogeneity of information coming from various sources, we discuss how Granular Computing helps realize their management and reuse. In particular, we will be concerned with the issues of compatibility, interoperability, and aggregation that become essential when dealing with non-uniform levels of granularity (specificity) of information and its different formalizations. We also relate the concept of granularity and its quantification to reuse by discussing the evolution of granules and their processing schemes.

We will present how the mechanisms of collaboration and integration between sources of information can be established given their level of granular compatibility and the existing representation differences. The role of information granulation in the formation of ontologies will be demonstrated. The rule-based architectures being regarded as an illustrative framework will be discussed in detail.

Bio

Witold Pedrycz (M’88, SM’94, F’99) is a Professor and Canada Research Chair (CRC) in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland.

He is actively pursuing research in Computational Intelligence, human-centric computing, fuzzy modeling, knowledge discovery and data mining, fuzzy control including fuzzy controllers, pattern recognition, knowledge-based neural networks, relational computation, bioinformatics, and Software Engineering and system engineering. He has published numerous papers in this area. He is also an author of 9 research monographs covering various aspects of Computational Intelligence and Software Engineering. 

Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. He currently serves as an Associate Editor of IEEE Transactions on Systems Man and Cybernetics, IEEE Transactions on Fuzzy Systems and IEEE Transactions on Neural Networks. Dr. Pedrycz is an Editor-in-Chief of Information Sciences. He is a President-elect of International Fuzzy Systems Association (IFSA) and president of North American Fuzzy Information Processing Society (NAFIPS). He was a General Chair of NAFIPS 2004 held in Banff, Canada.

Relevant Funding Opportunities at ONR and a New NSF/Navy Program for Research Student Support

Dr. ERNEST L. MCDUFFIE

Deputy Director,
Naval Research - Science & Technology for America's Readiness (N-STAR)

Abstract

Initial focus will be on describing current and pending funding opportunities at ONR in areas of interest. Within ONR’s Department of Information, Electronics & Surveillance (Code 31) is the Mathematical, Computer, and Information Sciences Division (Code 311) which is responsible for all research activities funded by ONR in these technical areas. Contact information, program descriptions, and priority areas will be provided and optimal strategies for successful proposal submissions will be discussed. Also to be covered is a relatively new Navy initiative called Naval Research – Science & Technology for America’s Readiness (N-STAR). This initiative is focused on the revitalization of Navy’s science and technology (S&T) civilian workforce. A key component of N-STAR is the NSF Navy Civilian Service (NNCS) scholarship/fellowship program. NNCS provides up to two years of full support including a stipend for US students to complete a degree in a relevant area of science at the BS, MS, or Ph.D. level in return for two years of service at a Naval R&D center. This program is partly in response to the growing global pressure on developing and recruiting the next generation of S&T workers.

Bio

Currently the Deputy Director of the Office of Naval Research’s (ONR) Naval Research – Science & Technology for America’s Readiness (N-STAR) program which is jointly managed between ONR and the National Science Foundation (NSF), Dr. McDuffie was from August of 2002 to August of 2004 the Lead Program Director for the Federal Cyber Service: Scholarship for Service (SFS) program at NSF. Both programs focus on providing scholarships to undergraduate and graduate US students in the sciences in return for Federal service to meet critical skill needs in the Federal government. For seven years prior to that he served as an assistant professor in the Department of Computer Science at Florida State University. During his stay there, he taught both graduate and undergraduate courses in all areas of computer science. Research interests and activities were mainly in the area of Artificial Intelligence (AI), focusing on temporal reasoning, expert systems, neural networks, automatic scheduling, and other hybrid AI techniques applied to many different real world problem domains. A published author of numerous journal, conference, and technical research papers Dr. McDuffie has also presented his work at both national and international computer science conferences. Over the past two decades his professional career has seen him participate in major software engineering projects for such well known organizations as the United States Air Force, the National Center for Atmospheric Research, the Federal Aviation Administration, Lockheed Missiles and Space Company, Los Alamos National Laboratory, and the National Security Agency. During this period, he has performed as a programmer, an analyst, a project supervisor, an engineer, a researcher, and an educator. 

In addition to being a McKnight Doctoral Fellow, Dr. McDuffie was a founding member of the Florida Institute of Technology’s Zeta Chapter of Upsilon Pi Epsilon, the International Honor Society for the Computing Sciences. He is also a member of the Association for Computing Machinery, the Institute of Electrical and Electronics Engineers, and has served as the Chairperson for the McKnight Alumni Association Board and Chairperson for the Tallahassee Coalition Center of Excellence Advisory Board. Dr. McDuffie received both his Ph.D. (1995) and MS degrees (1992) in computer science from the Florida Institute of Technology in Melbourne, Florida, a BS (1990) in computer science from Stockton State College, in New Jersey, an AAS (1983) in communications technology from the Community College of the Air Force, and spent two years as a cadet at the United States Military Academy, at West Point, New York (1970-72).