Showing papers on "Knowledge extraction published in 1994"

Using dynamic time warping to find patterns in time series

Abstract: Knowledge discovery in databases presents many interesting challenges within the context of providing computer tools for exploring large data archives. Electronic data repositories are growing quickly and contain data from commercial, scientific, and other domains. Much of this data is inherently temporal, such as stock prices or NASA telemetry data. Detecting patterns in such data streams or time series is an important knowledge discovery task. This paper describes some preliminary experiments with a dynamic programming approach to the problem. The pattern detection algorithm is based on the dynamic time warping technique used in the speech recognition field.

"Rough Sets, Fuzzy Sets and Knowledge Discovery"

Abstract: An Overview of Knowledge Discovery in Databases: Recent Progress and Challenges.- Rough Sets and Knowledge Discovery: An Overview.- Search for Concepts and Dependencies in Databases.- Rough Sets and Concept Lattices.- Human-Computer Interfaces: DBLEARN and SystemX.- A Heuristic for Evaluating Databases for Knowledge Discovery with DBLEARN.- Knowledge Recognition, Rough Sets, and Formal Concept Lattices.- Quantifying Uncertainty of Knowledge Discovered from Databases.- Temporal Rules Discovery Using Datalogic/R+ with Stock Market Data.- A System Architecture for Database Mining Applications.- An Attribute-Oriented Rough Set Approach for Knowledge Discovery in Databases.- A Rough Set Model for Relational Databases.- Data Filtration: A Rough Set Approach.- Automated Discovery of Empirical Laws in a Science Laboratory.- Hard and Soft Sets.- Rough Set Analysis of Multi-Attribute Decision Problems.- Rough Set Semantics for Non-Classical Logics.- A Note on Categories of Information Systems.- On Rough Sets in Topological Boolean Algebras.- Approximation of Relations.- Variable Precision Rough Sets with Asymmetric Bounds.- Uncertain Reasoning with Interval-Set Algebra.- On a Logic of Information for Reasoning About Knowledge.- Rough Consequence and Rough Algebra.- Formal Description of Rough Sets.- Rough Sets: A Special Case of Interval Structures.- A Pure Logic-Algebraic Analysis of Rough Top and Rough Bottom Equalities.- A Novel Approach to the Minimal Cover Problem.- Algebraic Structures of Rough Sets.- Rough Concept Analysis.- Rough Approximate Operators: Axiomatic Rough Set Theory.- Finding Reducts in Composed Information Systems.- PRIMEROSE: Probabilistic Rule Induction Method Based on Rough Set Theory.- Comparison of Machine Learning and Knowledge Acquisition Methods of Rule Induction Based on Rough Sets.- AQ, Rough Sets, and Matroid Theory.- Rough Classifiers.- A General Two-Stage Approach to Inducing Rules from Examples.- An Incremental Learning Algorithm for Constructing Decision Rules.- Decision Trees for Decision Tables.- Fuzzy Reasoning and Rough Sets.- Fuzzy Representations in Rough Set Approximations.- Trusting an Information Agent.- Handling Various Types of Uncertainty in the Rough Set Approach.- Intelligent Image Filtering Using Rough Sets.- Multilayer Knowledge Base System for Speaker-Independent Recognition of Isolated Words.- Image Segmentation Based on the Indiscernibility Relation.- Accurate Edge Detection Using Rough Sets.- Rough Classification of Pneumonia Patients Using a Clinical Database.- Rough Sets Approach to Analysis of Data of Diagnostic Peritoneal Lavage Applied for Multiple Injuries Patients.- Neural Networks and Rough Sets - Comparison and Combination for Classification of Histological Pictures.- Towards a Parallel Rough Sets Computer.- Learning Conceptual Design Rules: A Rough Sets Approach.- Intelligent Control System Implementation to the Pipe Organ Instrument.- An Implementation of Decomposition Algorithm and its Application in Information Systems Analysis and Logic Synthesis.- ESEP: An Expert System for Environmental Protection.- Author Index.

Amalgamating knowledge bases

Abstract: The integration of knowledge for multiple sources is an important aspect of automated reasoning systems. When different knowledge bases are used to store knowledge provided by multiple sources, we are faced with the problem of integrating multiple knowledge bases: Under these circumstances, we are also confronted with the prospect of inconsistency. In this paper we present a uniform theoretical framework, based on annotated logics, for amalgamating multiple knowledge bases when these knowledge bases (possibly) contain inconsistencies, uncertainties, and nonmonotonic modes of negation. We show that annotated logics may be used, with some modifications, to mediate between different knowledge bases. The multiple knowledge bases are amalgamated by a transformation of the individual knowledge bases into new annotated logic programs, together with the addition of a new axiom scheme. We characterize the declarative semantics of such amalgamated knowledge bases and study how the semantics of the amalgam is related to the semantics of the individual knowledge bases being combined.—Author's Abstract

Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases

Abstract: Concept hierarchies organize data and concepts in hierarchical forms or in certain partial order, which helps expressing knowledge and data relationships in databases in concise, high level terms, and thus, plays an important role in knowledge discovery processes. Concept hierarchies could be provided by knowledge engineers, domain experts or users, or embedded in some data relations. However, it is sometimes desirable to automatically generate some concept hierarchies or adjust some given hierarchies for particular learning tasks. In this paper, the issues of dynamic generation and refinement of concept hierarchies are studied. The study leads to some algorithms for automatic generation of concept hierarchies for numerical attributes based on data distributions and for dynamic refinement of a given or generated concept hierarchy based on a learning request, the relevant set of data and database statistics. These algorithms have been implemented in the DBLearn knowledge discovery system and tested against large relational databases. The experimental results show that the algorithms are efficient and effective for knowledge discovery in large databases.

Using Dynamic Time Warping to FindPatterns in Time Series

Abstract: Knowledge discovery in databases presents many interesting challenges within the ¢onte~t of providing computer tools for exploring large data archives. Electronic data .repositories are growing qulckiy and contain data from commercial, scientific, and other domains. Much of this data is inherently temporal, such as stock prices or NASA telemetry data. Detect£ug patterns in such data streams or time series is an important knowledge discovery task. This paper describes some pr~|~m;~,ry experiments with a dynamic prograrnm~,~g approach to the problem. The pattern detection algorithm is based on the dynamic time warping technique used in the speech recognition field.

PREPARE: a tool for knowledge base verification

Abstract: The knowledge base is the most important component in a knowledge-based system. Because a knowledge base is often built in an incremental, piecemeal fashion, potential errors may be inadvertently brought into it. One of the critical issues in developing reliable knowledge-based systems is how to verify the correctness of a knowledge base. The paper describes an automated tool called PREPARE for detecting potential errors in a knowledge base. PREPARE is based on modeling a knowledge base by using a predicate/transition net representation. Inconsistent, redundant, subsumed, circular, and incomplete rules in a knowledge base are then defined as patterns of the predicate/transition net model, and are detected through a syntactic pattern recognition method. The research results to date have indicated that: the methodology ran be adopted in knowledge-based systems where logic is used as knowledge representation formalism; the tool can be invoked at any stage of the system's development, even without a fully functioning inference engine; the predicate/transition net model of knowledge bases is easy to implement and provides a clear and understandable display of the knowledge to be used by the system. >

Abstraction of high level concepts from numerical values in databases

Abstract: A conceptual clustering method is proposed for discovering high level concepts of numerical attribute values from databases. The method considers both frequency and value distributions of data, thus is able to discover relevant concepts from numerical attributes. The discovered knowledge can be used for representing data semantically and for providing approximate answers when exact ones are not available. Our knowledge discovery approach is to partition the data set of one or more attributes into clusters that minimize the relaxation error. An algorithm is developed which finds the best binary partition in O(n) time and generates a concept hierarchy in O(n2) time where n is the number of distinct values of the attribute. The effectiveness of our clustering method is demonstrated by applying it to a large transportation database for approximate query answering.

The process of knowledge discovery in databases: a first sketch

Abstract: The general idea of discovering knowledge in large amounts of data is both appealing and intuitive. Typically we focus our attention on learning algorithms, which provide the core capability of generalizing from large numbers of small, very specific facts to useful high-level rules; these learning technique's seem to hold the most excitement and perhaps the most substantive scientific content in the knowledge discovery in databases (KDD) enterprise. However, when we engage in real-world discovery tasks, we find that they can be extremely complex, and that induction of rules is only one small part of the overall process. While others have written overviews of the concept of KDD, and even provided block diagrams for "knowledge discovery systems," no one has begun to identify all of the building blocks in a realistic KDD process. This is what we attempt to do here. Besides bringing into the discussion several parts of the process that have received inadequate attention in the KDD community, a careful elucidation of the steps in a realistic knowledge discovery process can provide a framework for comparison of different technologies and tools that are almost impossible to compare without a clean model.

An object-oriented knowledge representation structure for exploration data integration

Abstract: Knowledge representation structure and reasoning processes are very important issues in the knowledge-based approach of integrating multiple spatial data sets for resource exploration. An object-oriented knowledge representation structure and corresponding reasoning processes are formulated and tested in this research on the knowledge-based approach of integrating spatial exploration data. The map-based prototype expert system developed in this study has self-contained knowledge representation structure and inference mechanisms. It is important to distinguish between lack of information and information providing negative evidence for a map-based system because the spatial distribution of data sets are uneven in most cases. Error and uncertainty estimation is also an important component of any production expert system. The uncertainty propagation mechanisms developed here work well for this type of integrated exploration problem. Evidential bellef function theory provides a natural theoretical basis for representing and integrating spatially uneven geophysical and geological information. The prototype system is tested using real mineral exploration data sets from the Snow Lake area, northern Manitoba, Canada. The test results outline the favorable exploration areas successfully and show the effectiveness of the knowledge representation structure and inference mechanisms for the knowledge-based approach.

A knowledge representation system for integration of general and case-specific knowledge

Abstract: Combining various knowledge types-and reasoning methods-in knowledge-based systems is a challenge to the knowledge representation task. The paper describes an object-oriented, frame-based knowledge representation system aimed at unifying case-specific and general domain knowledge within a single representation system. It is targeted at the representational needs that have emerged from research in knowledge-intensive case-based reasoning, addressing complex problem solving in open and weak theory domains. Emphasis is put on representational expressiveness, on flexible reasoning and control schemes, and on easy inspection of cases and other knowledge objects. >

Knowledge discovery in large image databases: dealing with uncertainties in ground truth

Abstract: This paper discusses the problem of knowledge discovery in image databases with particular focus on the issues which arise when absolute ground truth is not available. The problem of searching the Magellan image data set in order to automatically locate and catalog small volcanoes on the planet Venus is used as a case study. In the absence of calibrated ground truth, planetary scientists provide subjective estimates of ground truth based on visual inspection of Magellan images. The paper discusses issues which arise in terms of elicitation of subjective probabilistic opinion, learning from probabilistic labels, and effective evaluation of both scientist and algorithm performance in the absence of ground truth. Data from the Magellan volcano detection project is used to illustrate the various techniques which we have developed to handle these issues. The primary conclusion of the paper is that knowledge discovery methodologies can be modified to handle lack of absolute ground truth provided the sources of uncertainty in the data are carefully handled.

Towards efficient induction mechanisms in database systems

Abstract: With the wide availability of huge amounts of data in database systems, the extraction of knowledge in databases by efficient and powerful induction or knowledge discovery mechanisms has become an important issue in the construction of new generation database and knowledge-base systems. In this article, an attribute-oriented induction method for knowledge discovery in databases is investigated, which provides an efficient, set-oriented induction mechanism for extraction of different kinds of knowledge rules, such as characteristic rules, discriminant rules, data evolution regularities and high level dependency rules in large relational databases. Our study shows that the method is robust in the existence of noise and database updates, is extensible to knowledge discovery in advanced and/or special purpose databases, such as object-oriented databases, active databases, spatial databases, etc., and has wide applications.

Knowledge refinement in a reflective architecture

Abstract: A knowledge acquisition tool should provide a user with maximum guidance in extending and debugging a knowledge base, by preventing inconsistencies and knowledge gaps that may arise inadvertently. Most current acquisition tools are not very flexible in that they are built for a predetermined inference structure or problem-solving mechanism, and the guidance they provide is specific to that inference structure and hard-coded by their designer. This paper focuses on EXPECT, a reflective architecture that supports knowledge acquisition based on an explicit analysis of the structure of a knowledge-based system, rather than on a fixed set of acquisition guidelines. EXPECT's problem solver is tightly integrated with LOOM, a state-of-the-art knowledge representation system. Domain facts and goals are represented declaratively, and the problem solver keeps records of their functionality within the task domain. When the user corrects the system's knowledge, EXPECT tracks any possible implications of this change in the overall system and cooperates with the user to correct any potential problems that may arise. The key to the flexibility of this knowledge acquisition tool is that it adapts its guidance as the knowledge bases evolve in response to changes introduced by the user.

Knowledge Extraction from Artificial Neural Networks and Applications

Abstract: Knowledge acquisition is a frequent bottleneck in artificial intelligence applications. Neural learning may offer a new perspective in this field. Using Self-Organising Neural Networks, as the Kohonen model, the inherent structures in high-dimensional input spaces are projected on a low dimensional space. The exploration of structures resp. classes is then possible applying the U-Matrix method for the visualisation of data. Since Neural Networks are not able to explain the obtained results, a machine learning algorithm sig* was developed to extract symbolic knowledge in form of rules out of subsymbolic data. Combining both approaches in hybrid system results in a powerful method to solve classification and diagnosis problems. Several applications have been used to test this method. Applications on processes with dynamic characteristics, such as chemical processes and avalanche forecasting show that an extension of this method from static to dynamic data is feasible.

DBLearn: a system prototype for knowledge discovery in relational databases

Abstract: A prototyped data mining system, DBLearn, has been developed, which efficiently and effectively extracts different kinds of knowledge rules from relational databases. It has the following features: high level learning interfaces, tightly integrated with commercial relational database systems, automatic refinement of concept hierarchies, efficient discovery algorithms and good performance. Substantial extensions of its knowledge discovery power towards knowledge mining in object-oriented, deductive and spatial databases are under research and development.

Local Patching Produces Compact Knowledge Bases

Abstract: Knowledge acquisition (KA) encompasses working with the expert to model the domain and a suitable problem solving method as preconditions for building a knowledge based system (KBS) and secondly working with the expert to populate the knowledge base. Ripple Down Rules (RDR) focuses on the second of these activities and allows an expert to populate a knowledge base (KB) without any knowledge engineering assistance. It is based on the idea that since the knowledge an expert provides is a justification of his or her judgment given in a specific context, this knowledge should only be used in the same context. Although the approach has been used for large single classification systems, it has the potential problem that the local nature of the knowledge may result in much repeated knowledge in the KB and much repeated knowledge acquisition. The study here attempts to quantitate and compare KB size and performance for systems built by experts with various levels of expertise and also inductively. The study also proposes a novel way of conducting such studies in that the different levels of expertise were achieved by using simulated experts. The conclusion from this study is that experts are likely to produce reasonably compact and efficient knowledge bases using the Ripple-Down Rule approach.

KDD–93: progress and challenges in knowledge discovery in databases

Abstract: Over 60 researchers from 10 countries took part in the Third Knowledge Discovery in Databases (KDD) Workshop, held during the Eleventh National Conference on Artificial Intelligence in Washington, D.C. A major trend evident at the workshop was the transition to applications in the core KDD area of discovery of relatively simple patterns in relational databases; the most successful applications are appearing in the areas of greatest need, where the databases are so large that manual analysis is impossible. Progress has been facilitated by the availability of commercial KDD tools for both generic discovery and domain-specific applications such as marketing. At the same time, progress has been slowed by problems such as lack of statistical rigor, overabundance of patterns, and poor integration. Besides applications, the main themes of this workshop were (1) the discovery of dependencies and models and (2) integrated and interactive KDD systems.

Integrating Induction in a Case-Based Reasoner

Abstract: This paper focuses on two key issues in building case-based reasoners (CBRs). The first issue is the knowledge engineering phase needed for CBRs as well as knowledge-based systems (KBS); the second issue is the integration of different methods of learning into CBRs. We show that we can use a knowledge modelling framework for the description and implementation of CBR systems; in particular we show how we used it in developing a CBR in the domain of protein purification. In order to encompass CBR (and learning in general) our knowledge modelling framework extends the usual frameworks with the notion of memory. Including memory we provide the capability for storing and retrieving episodes of problem solving, the basis of case-based reasoning and learning. We show here that this framework, and the supporting language NOOS, allows furthermore to integrate other learning methods as needed. Specifically, we show how a method for the induction of class prototypes can be implemented and integrated with case-based methods in an uniform framework.

Knowledge discovery in databases: A rule-based attribute-oriented approach

Abstract: An attribute-oriented induction has been developed in the previous study of knowledge discovery in databases. A concept tree ascension technique is applied in concept generalization. In this paper, we extend the background knowledge representation from an unconditional non-rule-based concept hierarchy to a rule-based concept hierarchy, which enhances greatly its representation power. An efficient rule-based attribute-oriented induction algorithm is developed to facilitate learning with a rule-based concept graph. An information loss problem which is special to rule-based induction is described together with a solution suggested.

Using Knowledge Exploration Tools to Study Airspace Complexity in Air Traffic Control

Abstract: A study was conducted at the Federal Aviation Administration's Jacksonville en route air traffic control center to identify factors that contribute to airspace complexity. Direct (verbal reports) and indirect (multidimensional scaling) procedures were used to identify potential factors. A list of candidate factors was created by combining the data from the direct and indirect knowledge exploration methods. Important complexity factors were identified by determining their simple and multiple correlations with overall sector complexity as judged by a group of Traffic Management Unit personnel (flow controllers). A final list of 16 complexity factors was developed and is suggested as a reference for future research in the area. An evaluation of the knowledge extraction techniques indicated that, although little unique information was generated by the indirect procedure, it was useful for the identification of complexity factors when combined with data from direct sources. Further research to validate the ide...

An application of KEFIR to the analysis of healthcare information

Abstract: The Key Findings Reporter (KEFIR) is a system for discovering and explaining "key findings" in large, relational databases. This paper describes an application of KEFIR to the analysis of health-care information. The system performs an automatic analysis of data along multiple dimensions to determine the most interesting deviations of specific quantitative measures relative to norms and previous values. It explains key findings through their relationship to other findings in the data, and, where possible, generates simple recommendations for correcting detected problems. A final written report, complete with business graphics, is produced for viewing remotely over the internet with Mosaic, or for printing to hardcopy.

Ontology Capture Method (IDEF5).

Abstract: : In order to exploit relevant information about a specific domain, the domain vocabulary must be captured. In addition, rigorous definitions of the basic terms in the vocabulary and the logical connections between those terms must be identified. Ontologies are used to capture the concept and objects in a specific domain, along with associated relationships and meanings. In addition, ontology capture helps coordinate projects by standardizing terminology and creates opportunities for information reuse. The lDEF5 Ontology Capture Method has been developed to reliably construct ontologies in a way that closely reflects human understanding of the specific domain. lDEF5 relies on iterative knowledge extraction through various steps: organizingiscoping; data collection; data analysis; initial development; ontology refinement/validation. lDEF5 allows users to validate the vocabulary and axioms of a given domain and store that knowledge in a usable representational medium.

A sharable ontology for the formal representation of engineering-design knowledge

Abstract: Attribute models of design objects p. 33 Formal representation of meaning in architecture p. 45 Discussion: What is a formal representation and what is it good for? p. 59 A tutorial introduction to grammatical programming p. 73 Geometric design with boundary solid grammars p. 85 The use of post interpretation for grammar-based generative systems p. 107 Discussion: Design space description formalisms p. 121 A formal language for the design of manufacturable objects p. 135 A vocabulary for conceptual design p. 157 A grammatical approach to network flow synthesis p. 173 Discussion: Research issues in the application of design grammars p. 191 Formal specification for design automation p. 201 Abduction for design p. 221 Adaptive design using a genetic algorithm p. 245 A constraint-based approach to supporting human negotiation in concurrent engineering p. 263

Automated knowledge acquisition for PROSPECTOR-like expert systems

Abstract: The method for automatic knowledge acquisition from categorical data is explained. Empirical implications are generated from data according to their frequencies. Only those of them are inserted to created knowledge base whose validity in data statistically significantly differs from the weight composed by the Prospector like inference mechanism from the weights of the implications already present in the base. A comparison with classical machine learning algorithms is discussed. The method is implemented as a part of the Knowledge EXplorer system.

A lock method for KBMSs using abstraction relationships' semantics

Abstract: Knowledge Base Management Systems (KBMSs) are a growing research area finding applicability in different domains. As a consequence, the demand for ever-larger knowledge bases (KBs) is growing more and more. Inside this context, knowledge sharing turns out to be a crucial point to be supported by KBMSs. In this paper, we propose a way of controlling knowledge sharing. We show how we obtain serializability of transactions providing many different locking granules, which are based on the semantics of the abstraction relationships. The main benefit of our technique is the high degree of potential concurrency, to be obtained through a logical partitioning of the KB graph and the provision of lock types used for each referenced partition. By this way, we capture more of the semantics contained in a KB graph, through an interpretation of its edges grounded in the abstraction relationships, and make feasible a full exploitation of all inherent parallelism in a knowledge representation approach.

DBROUGH: A Rough Set Based Knowledge Discovery System

Abstract: Knowledge discovery in databases, or data mining, is an important objective in the development of data- and knowledge-base systems. An attribute-oriented rough set method is developed for knowledge discovery in databases. The method integrates learning from example techniques with rough set theory. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of the database learning processes. Then the rough set techniques are applied to the generalized relation to derive different knowledge rules. Moreover, the approach can find all the maximal generalized rules in the data. Based on these principles, a prototype database learning system, DBROUGH, has been constructed. Our study shows that attribute-oriented induction combined with rough set techniques provide an efficient and effective mechanism for knowledge discovery in database systems.

Creating a Knowledge Base of Biological Research Papers

Abstract: Intelligent text-oriented tools for representing and searching the biological research literature are being developed, which combine object-oriented databases with artificial intelligence techniques to create a richly structured knowledge base of Materials and Methods sections of biological research papers. A knowledge model of experimental processes, biological and chemical substances, and analytical techniques is described, based on the representation techniques of taxonomic semantic nets and knowledge frames. Two approaches to populating the knowledge base with the contents of biological research papers are described: natural language processing and an interactive knowledge definition tool.

Practical techniques for organizing and measuring knowledge

Abstract: representation schema, abstract schema: (*) An idealized KNOWLEDGE REPRESENTATION SCHEMA describing representation principles, but containing simplify- ing assumptions or constructs that may not be finitely realizable. An abstract schema

Query answering via cooperative data inference

Abstract: This paper proposes the use of accessible information (data/knowledge) to infer inaccessible data in a distributed database system. Inference rules are extracted from databases by means of knowledge discovery techniques. These rules can derive inaccessible data due to a site failure or network partition in a distributed system. Such query answering requires combining incomplete and partial information from multiple sources. The derived answer may be exact or approximate. Our inference process involves two phases to reason with reconstructed information. One phase involves using local rules to infer inaccessible data. A second phase involves merging information from different sites. We shall call such reasoning processes cooperative data inference. Since the derived answer may be incomplete, new algebraic tools are developed for supporting operations on incomplete information. A weak criterion called toleration is introduced for evaluating the inferred results. The conditions that assure the correctness of combining partial results, known as sound inference paths, are developed. A solution is presented for terminating an iterative reasoning process on derived data from multiple knowledge sources. The proposed approach has been implemented on a cooperative distributed database testbed, CoBase, at UCLA. The experimental results validate the feasibility of this proposed concept and can significantly improve the availability of distributed knowledge base/database systems.