Knowledge-based systems and solving knowledge integrating problems have seen a great surge of research activity in recent years. Knowledge Integration Methods provides a wide snapshot of building knowledge-based systems, inconsistency measures, methods for handling consistency, and methods for integrating knowledge bases. The book also provides the mathematical background to solving problems of restoring consistency and integrating probabilistic knowledge bases in the integrating process. The research results presented in the book can be applied in decision support systems, semantic web systems, multimedia information retrieval systems, medical imaging systems, cooperative information systems, and more. This text will be useful for computer science graduates and PhD students, in addition to researchers and readers working on knowledge management and ontology interpretation.

Problems can arise whenever inferencing is attempted on a knowledge base that is incomplete. Our work shows that data mining techniques can be applied to fill in incomplete areas in Bayesian Knowledge Bases (BKBs), as well as in other knowledge-based systems utilizing probabilistic representations. The problem of inconsistency in BKBs has been addressed in previous work, where reinforcement learning techniques from neural networks were applied. However, the issue of automatically solving incompleteness in BKBs has yet to be addressed. Presently, incompleteness in BKBs is repaired through the application of traditional knowledge acquisition techniques. We show how association rules can be extracted from databases in order to replace excluded information and express missing relationships. A methodology for incorporating those results while maintaining a consistent knowledge base is also included.

Abstract: "As knowledge bases are enlarged to support more complex classes of problems, expert systems will demand efficient knowledge-management techniques -- techniques that are already available in database systems. In this paper, we present the design of a database schema suitable for [sic] knowledge base that employ [sic] a decision-network representation. Using this schema, we describe the process of translating existing knowledge bases into relational format. Although exploratory in nature, our work indicates that the application of database techniques offer numerous advantages over an ad-hoc scheme for managing probabilistic knowledge bases."

Knowledge bases (KB) facilitate real world decision making by providing access to structured relational information that enables pattern discovery and semantic queries. Although there is a large amount of data available for populating a KB; the data must first be gathered and assembled. Traditionally, this integration is performed automatically by storing the output of an information extraction pipeline directly into a database as if this prediction were the ``truth.'' However, the resulting KB is often not reliable because (a) errors accumulate in the integration pipeline, and (b) they persist in the KB even after new information arrives that could rectify these errors. We envision a paradigm-shift in KB construction for addressing these concerns that we term an ``epistemological'' database. In epistemological databases the existence and properties of entities are not directly input into the DB; they are instead determined by inference on raw evidence input into the DB. This shift in thinking is important because it allows inference to revisit previous conclusions and retroactively correct errors as new evidence arrives. Evidence is abundant and in steady supply from web spiders, semantic web ontologies, external databases, and even groups of enthusiastic human editors. As this evidence continues to accumulate and inference continues to run in the background, the quality of the knowledge base continues to improve. In this dissertation we develop the machine learning components necessary to achieve epistemological knowledge base construction at scale with key contributions in modeling, inference and learning.