Is KS=(D+I+S+K)*E + KM? Juan Llorens Computer Science Department Universidad Carlos III de Madrid Leganés, Madrid, Spain llorens@inf.uc3m.es Rubén Prieto-Díaz Commonwealth Information Security Center James Madison University, Harrisonburg, VA prietodiaz@cisat.jmu.edu Abstract Is it time for a science of knowledge? In this paper we argue that yes, it is time and define it as: KS = (D+I+S+K)*E + KM Where KS is Knowledge Science, DE is Domain Engineering, IE is Information Engineering, SE is Software Engineering and KE is Knowledge Engineering, and that Knowledge Management (KM) moderates these disciplines. We try to clarify the apparent state of confusion in the current landscape where KS, KM and KE have different meanings to different audiences. After clarifying the difference between KE and KM and arguing that KE and KM are complementary we explain why we selected the word knowledge instead of information and decided on the word science in KS. The core of the paper explains the term (D+I+S+K)*E in our proposed definition and why each of these disciplines contribute to KS. One of our conclusions is that KS deals with knowledge about knowledge, or about the knowledge needed to increase, expand or further acquire more knowledge, and that KS can be applied to all the existing sciences, covering their needs of methodologies, techniques and tools. Is it time for a Science of Knowledge? We argue that yes, it is time to create Knowledge Science (KS) and define it as the merging of several disciplines. KS = (D+I+S+K)*E + KM We propose defining KS as the merging of Domain Engineering (DE), Information Engineering (IE), Software Engineering (SE) and Knowledge Engineering (KE), all of them “moderated” by Knowledge Management (KM). Our first task in explaining the above equation is to clarify the difference between KE and KM. When facing problems where computers are required to deal with knowledge, engineers must choose whether to follow KM or KE: are these fields different? If so, which one should I use? We believe both are needed if we want to deal effectively with knowledge. The concept of KE can be traced back to the late 1950’s [1], when Artificial Intelligence (AI) brought it up to the computer science scene. AI experts wanted to emphasize that “general-computer- based-problem-solving” was not any longer an illusion but a pos- sible reality. State spaces from game theory, production rules, semantic networks, frames, and neural networks have all contrib- uted to materialize the kernel of knowledge engineering. Although it seems clear what scientists understand as KE, why is it different from KM? AI engineers, after all, model and manage knowledge in pursuing their AI engineering solutions. Some clari- fication is in order. We need to address the following two ques- tions: What is engineering? And what is knowledge? According to the Accreditation Board for Engineering and Tech- nology (ABET) 1 “Engineering is the profession in which a knowl- edge of the mathematical and natural sciences, gained by study, experience, and practice, is applied with judgment to develop ways to utilize, economically, the materials and forces of nature for the benefit of mankind”. The Merriam-Webster Dictionary 2 defines engineering as “the application of science and mathemat- ics by which the properties of matter and the sources of energy in nature are made useful to people”. Although other definitions can be found 3 , most converge to the same concepts. It seems from these definitions of engineering that what AI engi- neers were doing was managing rather than engineering knowl- edge. But before drawing any conclusions, knowledge needs to be defined. According to the Merriam-Webster Dictionary and the Cambridge Dictionary of English 4 , knowledge can be defined as “understand- ing of or information about a subject which has been obtained by experience or study, and which is either in a person's mind or pos- sessed by people generally”, “acquaintance with or understanding of a science, art, or technique” and “the fact or condition of hav- ing information or of being learned”. The Free On-Line dictionary of computing 5 provides a somewhat more specific definition of knowledge. “The objects, concepts and relationships that are as- sumed to exist in some area of interest”. To better understand these definitions it is necessary to define information. Information is defined by the Merriam-Webster Dictionary as “the equivalent of or the capacity of something to perform organiza- tional work, the difference between two forms of organization or between two states of uncertainty before and after a message has been received, but also the degree to which one variable of a sys- tem depends on or is constrained by [..] another.” Can it be concluded from these definitions that knowledge is learned information? Or that knowledge is a particular kind of information? If this is the understanding, then should AI’s KE be called Information Management or Knowledge Management? It seems that we have further blurred rather than clarified the differ- ence between KE and KM. It is necessary then to provide another viewpoint by examining the present understanding of KM. In the last decades people like Drucker [2], Strassman [3] and Senge [4] introduced the concept of KM. A definition of KM as understood today may require 1 See http://www.abet.org 2 See http://www.m-w.com 3 See http://civil.engr.siu.edu/intro/definitions.htm 4 See http://dictionary.cambridge.org 5 See http://wombat.doc.ic.ac.uk/foldoc/index.html ACM SIGSOFT Software Engineering Notes vol 28 no 2 March 2003 Page 1