We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving thii problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems. We also show how the best features of the two proposed algorithms can be combined into a hybrid algorithm, called AprioriHybrid. Scale-up experiments show that AprioriHybrid scales linearly with the number of transactions. AprioriHybrid also has excellent scale-up properties with respect to the transaction size and the number of items in the database.

/pdf/fast-algorithms-for-mining-association-rules-3yuau93ycd.pdf

Fast algorithms for mining association rules

物件導向軟體之架構(Object-Oriented Software Construction)探討

1 Why develop an ontology? In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of ArtificialIntelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. The Defense Advanced Research Projects Agency (DARPA), in conjunction with the W3C, is developing DARPA Agent Markup Language (DAML) by extending RDF with more expressive constructs aimed at facilitating agent interaction on the Web (Hendler and McGuinness 2000). Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Price and Spackman 2000) and the semantic network of the Unified Medical Language System (Humphreys and Lindberg 1993). Broad general-purpose ontologies are emerging as well. For example, the United Nations Development Program and Dun & Bradstreet combined their efforts to develop the UNSPSC ontology which provides terminology for products and services (www.unspsc.org). An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them. Why would someone want to develop an ontology? Some of the reasons are:

/pdf/ontology-development-101-a-guide-to-creating-your-first-2c6x0j449d.pdf

Ontology Development 101: A Guide to Creating Your First Ontology

: Successful Software reuse requires the systematic discovery and exploitation of commonality across related software systems. By examining related software systems and the underlying theory of the class of systems they represent, domain analysis can provide a generic description of the requirements of that class of systems and a set of approaches for their implementation. This report will establish methods for performing a domain analysis and describe the products of the domain analysis process. To illustrate the application of domain analysis to a representative class of software systems, this report will provide a domain analysis of window management system software.

/pdf/feature-oriented-domain-analysis-foda-feasibility-study-2zsj0a30fo.pdf

Feature-Oriented Domain Analysis (FODA) Feasibility Study

https://www.cs.ox.ac.uk/people/bernardo.cuencagrau/publications/PelletDemo.pdf

Pellet: A practical OWL-DL reasoner

We describe Telos, a language intended to support the development of information systems. The design principles for the language are based on the premise that information system development is knowledge intensive and that the primary responsibility of any language intended for the task is to be able to formally represent the relevent knowledge. Accordingly, the proposed language is founded on concepts from knowledge representations. Indeed, the language is appropriate for representing knowledge about a variety of worlds related to a particular information system, such as the subject world (application domain), the usage world (user models, environments), the system world (software requirements, design), and the development world (teams, metodologies).We introduce the features of the language through examples, focusing on those provided for desribing metaconcepts that can then be used to describe knowledge relevant to a particular information system. Telos' fetures include an object-centered framework which supports aggregation, generalization, and classification; a novel treatment of attributes; an explicit representation of time; and facilities for specifying integrity constraints and deductive rules. We review actual applications of the language through further examples, and we sketch a formalization of the language.

/pdf/telos-representing-knowledge-about-information-systems-3aalvd1a0t.pdf

Telos: representing knowledge about information systems

CLASSIC is a data model that encourages the description of objects not only in terms of their relations to other known objects, but in terms of a level of intensional structure as well. The CLASSIC language of structured descriptions permits i) partial descriptions of individuals, under an 'open world' assumption, ii) answers to queries either as extensional lists of values or as descriptions that necessarily hold of all possible answers, and iii) an easily extensible schema, which can be accessed uniformly with the data. One of the strengths of the approach is that the same language plays multiple roles in the processes of defining and populating the DB, as well as querying and answering.CLASSIC (for which we have a prototype main-memory implementation) can actively discover new information about objects from several sources: it can recognize new classes under which an object falls based on a description of the object, it can propagate some deductive consequences of DB updates, it has simple procedural recognizers, and it supports a limited form of forward-chaining rules to derive new conclusions about known objects.The kind of language of descriptions and queries presented here provides a new arena for the search for languages that are more expressive than conventional DBMS languages, but for which query processing is still tractable. This space of languages differs from the subsets of predicate calculus hitherto explored by deductive databases.

/pdf/classic-a-structural-data-model-for-objects-3ktxhtqk40.pdf

CLASSIC: a structural data model for objects

CLASSIC is a recently developed knowledge representation system that follows the paradigm originally set out in the KL-ONE system: It concentrates on the definition of structured concepts, their organization into taxonomies, the creation and manipulation of individual instances of such concepts, and the key inferences of subsumption and classification. Rather than simply presenting a description of CLASSIC, we complement a brief system overview with a discussion of how to live within the confines of a limited object-oriented deductive system. By analyzing the representational strengths and weaknesses of CLASSIC, we consider the circumstances under which it is most appropriate to use (or not use) it. We elaborate a knowledge engineering methodology for building KL-ONE-style knowledge bases, with emphasis on the modeling choices that arise in the process of describing a domain. We also address some of the key difficult issues encountered by new users, including primitive vs. defined concepts, and differences between roles and concepts, as well as representational “tricks-of-the-trade,” which we believe to be generally useful. Much of the discussion should be relevant to many of the current systems based on KL-ONE.

LIVING WITH CLASSIC: When and How to Use a KL-ONE-Like Language

We argue that accessing the transitive closure of relationships is an important component of both databases and knowledge representation systems in Artificial Intelligence. The demands for efficient access and management of large relationships motivate the need for explicitly storing the transitive closure in a compressed and local way, while allowing updates to the base relation to be propagated incrementally. We present a transitive closure compression technique, based on labeling spanning trees with numeric intervals, and provide both analytical and empirical evidence of its efficacy, including a proof of optimality.

Alexander Borgida

Papers

Telos: representing knowledge about information systems

CLASSIC: a structural data model for objects

LIVING WITH CLASSIC: When and How to Use a KL-ONE-Like Language

Efficient management of transitive relationships in large data and knowledge bases

On the relative expressiveness of description logics and predicate logics