Inference of termination conditions for numerical loops in Prolog

We present a new approach to termination analysis of numerical computations in logic programs. Traditional approaches fail to analyse them due to non well-foundedness of the integers. We present a technique that allows overcoming these difficulties. Our approach is based on transforming a program in a way that allows integrating and extending techniques originally developed for analysis of numerical computations in the framework of query-mapping pairs with the well-known framework of acceptability. Such an integration not only contributes to the understanding of termination behaviour of numerical computations, but also allows us to perform a correct analysis of such computations automatically, by extending previous work on a constraint-based approach to termination. Finally, we discuss possible extensions of the technique, including incorporating general term orderings.Comment: To appear in Theory and Practice of Logic Programming. To appear in Theory and Practice of Logic Programmin

Inference of termination conditions for numerical loops

We present a new approach to termination analysis of numerical computations in logic programs. Traditional approaches fail to analyse them due to non well-foundedness of the integers. We present a technique that allows to overcome these difficulties. Our approach is based on transforming a program in way that allows integrating and extending techniques originally developed for analysis of numerical computations in the framework of query-mapping pairs with the well-known framework of acceptability. Such an integration not only contributes to the understanding of termination behaviour of numerical computations, but also allows to perform a correct analysis of such computations automatically, thus, extending previous work on a constraints-based approach to termination. In the last section of the paper we discuss possible extensions of the technique, including incorporating general term orderings.Comment: Presented at WST200

Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases

A critical challenge in constructing a natural language interface to database (NLIDB) is bridging the semantic gap between a natural language query (NLQ) and the underlying data. Two specific ways this challenge exhibits itself is through keyword mapping and join path inference. Keyword mapping is the task of mapping individual keywords in the original NLQ to database elements (such as relations, attributes or values). It is challenging due to the ambiguity in mapping the user's mental model and diction to the schema definition and contents of the underlying database. Join path inference is the process of selecting the relations and join conditions in the FROM clause of the final SQL query, and is difficult because NLIDB users lack the knowledge of the database schema or SQL and therefore cannot explicitly specify the intermediate tables and joins needed to construct a final SQL query. In this paper, we propose leveraging information from the SQL query log of a database to enhance the performance of existing NLIDBs with respect to these challenges. We present a system Templar that can be used to augment existing NLIDBs. Our extensive experimental evaluation demonstrates the effectiveness of our approach, leading up to 138% improvement in top-1 accuracy in existing NLIDBs by leveraging SQL query log information.Comment: Accepted to IEEE International Conference on Data Engineering (ICDE) 201

CML: the commonKADS conceptual modelling language

We present a structured language for the specification of knowledge models according to the CommonKADS methodology. This language is called CML (Conceptual Modelling Language) and provides both a structured textual notation and a diagrammatic notation for expertise models. The use of our CML is illustrated by a variety of examples taken from the VT elevator design system

Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models

The interpretation of complex high-dimensional data typically requires the use of dimensionality reduction techniques to extract explanatory low-dimensional representations. However, in many real-world problems these representations may not be sufficient to aid interpretation on their own, and it would be desirable to interpret the model in terms of the original features themselves. Our goal is to characterise how feature-level variation depends on latent low-dimensional representations, external covariates, and non-linear interactions between the two. In this paper, we propose to achieve this through a structured kernel decomposition in a hybrid Gaussian Process model which we call the Covariate Gaussian Process Latent Variable Model (c-GPLVM). We demonstrate the utility of our model on simulated examples and applications in disease progression modelling from high-dimensional gene expression data in the presence of additional phenotypes. In each setting we show how the c-GPLVM can extract low-dimensional structures from high-dimensional data sets whilst allowing a breakdown of feature-level variability that is not present in other commonly used dimensionality reduction approaches

It Takes (Only) Two: Adversarial Generator-Encoder Networks

We present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and unconditional samples boosted by adversarial learning. Unlike previous hybrids of autoencoders and adversarial networks, the adversarial game in our approach is set up directly between the encoder and the generator, and no external mappings are trained in the process of learning. The game objective compares the divergences of each of the real and the generated data distributions with the prior distribution in the latent space. We show that direct generator-vs-encoder game leads to a tight coupling of the two components, resulting in samples and reconstructions of a comparable quality to some recently-proposed more complex architectures

Polytool: polynomial interpretations as a basis for termination analysis of Logic programs

Our goal is to study the feasibility of porting termination analysis techniques developed for one programming paradigm to another paradigm. In this paper, we show how to adapt termination analysis techniques based on polynomial interpretations - very well known in the context of term rewrite systems (TRSs) - to obtain new (non-transformational) ter- mination analysis techniques for definite logic programs (LPs). This leads to an approach that can be seen as a direct generalization of the traditional techniques in termination analysis of LPs, where linear norms and level mappings are used. Our extension general- izes these to arbitrary polynomials. We extend a number of standard concepts and results on termination analysis to the context of polynomial interpretations. We also propose a constraint-based approach for automatically generating polynomial interpretations that satisfy the termination conditions. Based on this approach, we implemented a new tool, called Polytool, for automatic termination analysis of LPs