Catálogo de publicaciones - libros

The growing use of XML in commercial as well as non-commercial domains to transport information poses new challenges to concepts to access this information. Common ways to access parts of a document use XPath-expressions. We provide a transformation of DTDs into a knowledge base in Description Logic. We use reasoning capabilities grounded in description logics to decide if a given XPath can be satisfied by a document, and to guide the search of XML-Processors into possibly successful branches of the document, thus avoiding parts of the document that will not yield results. The extension towards object oriented subclassing schemes opens this approach towards OODB-queries. In contrast to other approaches we do not use any kind of graph representing the document structure, and no steps towards incorporation of the XML/OODB-processor itself will be taken.

The maintenance of large knowledge systems usually is a rather complex task. In this paper we will show that extensions or modifications of a knowledge base can be supported by appropriate visualizations techniques, e.g. by illustrating dependencies within the considered knowledge.

In particular, we introduce a declarative approach for querying and visualizing rule–based knowledge represented as documents; a knowledge engineer can extract and visually inspect parts of the knowledge base by ad–hoc declarations in a flexible manner.

Scalable data mining in large databases is one of today’s real challenges to database research area. The integration of data mining with database systems is an essential component for any successful large-scale data mining application. A fundamental component in data mining tasks is finding frequent patterns in a given dataset. Most of the previous studies adopt an -like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. In this study we present an evaluation of SQL based frequent pattern mining with a novel frequent pattern growth (-) method, which is efficient and scalable for mining both long and short patterns without candidate generation. We examine some techniques to improve performance. In addition, we have made performance evaluation on DBMS with IBM DB2 UDB EEE V8.

In this paper we present a machine translation system, which translates Japanese into German. We have developed a transfer-based architecture in which the transfer rules are learnt incrementally from translation examples provided by a user. This means that there are no handcrafted rules, but, on the contrary, the user can customize the system according to his own preferences. The translation system has been implemented by using Amzi! Prolog. This programming environment had the big advantage of offering sufficient scalability even for large lexicons and rule bases, powerful unification operations for the application of transfer rules, and full Unicode support for Japanese characters. Finally, the application programming interface to Visual Basic made it possible to design an embedded translation environment so that the user can use Microsoft Word to work with the Japanese text and invoke the translation features directly from within the text editor. We have integrated the machine translation system into a language learning environment for German-speaking language students to create a Personal Embedded Translation and Reading Assistant (PETRA).

Semi-automatic data mining approaches often yield better results than plain automatic methods, due to the early integration of the user’s goals. For example in the medical domain, experts are likely to favor simpler models instead of more complex models. Then, the accuracy of discovered patterns is often not the only criterion to consider. Instead, the simplicity of the discovered knowledge is of prime importance, since this directly relates to the understandability and the interpretability of the learned knowledge.

In this paper, we present quality measures considering the understandability and the accuracy of (learned) rule bases. We describe a unifying quality measure, which can trade-off small losses concerning accuracy vs. an increased simplicity. Furthermore, we introduce a semi-automatic data mining method for learning understandable and accurate rule bases. The presented work is evaluated using cases from a real world application in the medical domain.

This paper provides results from a usability experiment comparing two different database query languages. The research focuses on a specific type of query task, namely classification queries. Classification is the process of assigning input data to discrete classes according to application specific criteria. While SQL can be used to perform classification tasks, we seek to discover whether a different type of query language offers any advantages over SQL. We present a rule-based language, which organizes the queries in a logical way. The rule based language is specifically designed to support classification tasks. The usability experiment measures the effectiveness, efficiency and satisfaction of novice and expert users performing a variety of classification tasks. The results show that while both approaches are usable for classification tasks, the rule-based approach was preferred by expert users.

We present a framework for a declarative approach to spatio-temporal reasoning on geographical data, based on the constraint logical language STACLP, which offers deductive and inductive capabilities. It can be exploited for a deductive rule-based approach to represent domain knowledge on data. Furthermore, it is well suited to model trajectories of moving objects, which can be analysed by using inductive techniques, like clustering, in order to find common movement patterns. A sketch of a case study on behavioural ecology is presented.

‘Video data mining’ is a technique to discover useful patterns from videos. It plays an important role in efficient video management. Particularly, we concentrate on extracting useful editing patterns from movies. These editing patterns are useful for an amateur editor to produce a new, more attractive video. But, it is essential to extract editing patterns associated with their semantic contents, called ‘semantic structures’. Otherwise the amateur editor can’t determine how to use the extracted editing patterns during the process of editing a new video.

In this paper, we propose two approaches to extract semantic structures from a movie, based on two different time series models of the movie. In one approach, the movie is represented as a multi-stream of metadata derived from visual and audio features in each shot. In another approach, the movie is represented as one-dimensional time series consisting of durations of target character’s appearance and disappearance. To both time series models, we apply data mining techniques. As a result, we extract the semantic structures about shot transitions and about how the target character appears on the screen and disappears from the screen.

In this paper we apply answer set programming to solve alternating Boolean equation systems. We develop a novel characterization of solutions for variables in disjunctive and conjunctive Boolean equation systems. Based on this we devise a mapping from Boolean equation systems with alternating fixed points to normal logic programs such that the solution of a given variable of an equation system can be determined by the existence of a stable model of the corresponding logic program. The technique can be used to model check alternating formulas of modal -calculus.

Constraint programming provides a declarative approach to solving combinatorial (optimization) problems. The user just states the problem as a constraint satisfaction problem (CSP) and a generic solver finds a solution without additional programming. However, in practice, the situation is more complicated because there usually exist several ways how to model the problem as a CSP, that is using variables, their domains, and constraints. In fact, different constraint models may lead to significantly different running times of the solver so constraint modeling is a crucial part of problem solving. This paper describes some known approaches to efficient modeling with constraints in a tutorial-like form. The primary audience is practitioners, especially in logic programming, that would like to use constraints in their projects but do not have yet deep knowledge of constraint satisfaction techniques.