A Delta Debugger for ILP Query Execution

Because query execution is the most crucial part of Inductive Logic Programming (ILP) algorithms, a lot of effort is invested in developing faster execution mechanisms. These execution mechanisms typically have a low-level implementation, making them hard to debug. Moreover, other factors such as the complexity of the problems handled by ILP algorithms and size of the code base of ILP data mining systems make debugging at this level a very difficult job. In this work, we present the trace-based debugging approach currently used in the development of new execution mechanisms in hipP, the engine underlying the ACE Data Mining system. This debugger uses the delta debugging algorithm to automatically reduce the total time needed to expose bugs in ILP execution, thus making manual debugging step much lighter.Comment: Paper presented at the 16th Workshop on Logic-based Methods in Programming Environments (WLPE2006

Principles of Query Visualization

Query Visualization (QV) is the problem of transforming a given query into a graphical representation that helps humans understand its meaning. This task is notably different from designing a Visual Query Language (VQL) that helps a user compose a query. This article discusses the principles of relational query visualization and its potential for simplifying user interactions with relational data.Comment: 20 pages, 12 figures, preprint for IEEE Data Engineering Bulleti

An MML-based tool for evaluating the complexity of (stochastic) logic programs

[ES] Esta tesis presenta el primer esquema de codificación MML para evaluar la simplicidad de modelos expresados en forma de programas lógicos estocásticos, así como su herramienta escrita en Prolog.[EN] The thesis presents the first general MML coding scheme for evaluating the simplicity of models expressed as stochastic logic programs, as a tool in Prolog.Castillo Andreu, H. (2012). An MML-based tool for evaluating the complexity of (stochastic) logic programs. http://hdl.handle.net/10251/17983Archivo delegad

Learning to deal with COTS (commercial off the shelf)

With the advent of model based development technologies, dependence of COTS in software development has increased considerably. Use of COTS is considered economical and practical when it comes to integration of various software components. However COTS are trapped with some pitfalls. COTS provided are not usually accompanied by models or extensive specifications. This approach makes usage & integration of COTS components with in house developed software components a very challenging task. Conformance of the implementation with the specification forms the basis for our approach. In this thesis, we analyze an approach where the model is extracted from the COTS software that greatly aids in integration.;We developed a system that extracts the state machine model from the COTS software using Dana Angluin\u27s L* Algorithm. We also developed a hierarchical approach of viewing the state machine model by static analysis of assembly code

Efficient Maximum A-Posteriori Inference in Markov Logic and Application in Description Logics

Maximum a-posteriori (MAP) query in statistical relational models computes the most probable world given evidence and further knowledge about the domain. It is arguably one of the most important types of computational problems, since it is also used as a subroutine in weight learning algorithms. In this thesis, we discuss an improved inference algorithm and an application for MAP queries. We focus on Markov logic (ML) as statistical relational formalism. Markov logic combines Markov networks with first-order logic by attaching weights to first-order formulas. For inference, we improve existing work which translates MAP queries to integer linear programs (ILP). The motivation is that existing ILP solvers are very stable and fast and are able to precisely estimate the quality of an intermediate solution. In our work, we focus on improving the translation process such that we result in ILPs having fewer variables and fewer constraints. Our main contribution is the Cutting Plane Aggregation (CPA) approach which leverages symmetries in ML networks and parallelizes MAP inference. Additionally, we integrate the cutting plane inference (Riedel 2008) algorithm which significantly reduces the number of groundings by solving multiple smaller ILPs instead of one large ILP. We present the new Markov logic engine RockIt which outperforms state-of-the-art engines in standard Markov logic benchmarks. Afterwards, we apply the MAP query to description logics. Description logics (DL) are knowledge representation formalisms whose expressivity is higher than propositional logic but lower than first-order logic. The most popular DLs have been standardized in the ontology language OWL and are an elementary component in the Semantic Web. We combine Markov logic, which essentially follows the semantic of a log-linear model, with description logics to log-linear description logics. In log-linear description logic weights can be attached to any description logic axiom. Furthermore, we introduce a new query type which computes the most-probable 'coherent' world. Possible applications of log-linear description logics are mainly located in the area of ontology learning and data integration. With our novel log-linear description logic reasoner ELog, we experimentally show that more expressivity increases quality and that the solutions of optimal solving strategies have higher quality than the solutions of approximate solving strategies