Knowledge Discovery in Databases
“The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” (Fayyad, Piatetsky-Shapiro, and Smyth, 1996, p. 30)Also known as knowledge extraction, information harvesting, data archeology, and information extraction

Information Retrieval
“The methods and processes for searching relevant information out of information systems that contain extremely large numbers of documents” (Rocha, 2001, 1.1)“The ultimate goal of IR is to produce or recommend relevant information to users” (1.2)“Traditional IR does not identify users and classifies subjects only with unchanging keywords and categories” (1.2)Distributed Information Systems (DIS) are collections of electronic networked information resources (e.g. databases) in some kind of interaction with communities of users; examples of such systems are: the Internet, the World Wide Web, corporate intranets, databases, library information retrieval systems, etc. DIS serve large and diverse communities of users by providing access to a large set of heterogeneous electronic information resources. Information Retrieval (IR) refers to all the methods and processes for searching relevant information out of information systems (isolated or part of DIS) that contain extremely large numbers of documents. As the complexity and size of both user communities and information resources grows, the fundamental limitations of traditional information retrieval systems have become evident in modern DIS.Traditional IR systems are based solely on keywords that index (semantically characterize) documents and a query language to retrieve documents from centralized databases according to these keywords – users need to know how to “pull” relevant information from passive databases. This setup leads to a number of flaws (Rocha and Bollen, 2000), which prevent traditional IR processes in DIS to achieve any kind of interesting coupling with users. The human-machine interaction observed in these systems is particularly rigid: Most cannot pro-actively “push” relevant information to its users about related topics that they may be unaware of, there is typically no mechanism to exchange knowledge, or crossover of relevant information among users and information resources, and there is no mechanism to recombine knowledge in different information resources to infer new linguistic categories of keywords used by evolving communities of users. In other words, traditional IR keeps DIS as static, passive, and isolated repositories of data; no interesting human-machine co-evolution of knowledge or learning is achieved (1.1). Rocha, L.. M. (2001). TalkMine: A soft computing approach to adaptive knowledge recommendation [Electronic version]. In V. Loia & S. Sessa (Eds.), Studies in fuzziness and soft computing: Vol. 75. Soft computing agents: New trends for designing autonomous systems.