Schema Independent Relational Learning

Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions

Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation

Corpus-based techniques have proved to be very beneficial in the development of efficient and accurate approaches to word sense disambiguation (WSD) despite the fact that they generally represent relatively shallow knowledge. It has always been thought, however, that WSD could also benefit from deeper knowledge sources. We describe a novel approach to WSD using inductive logic programming to learn theories from first-order logic representations that allows corpus-based evidence to be combined with any kind of background knowledge. This approach has been shown to be effective over several disambiguation tasks using a combination of deep and shallow knowledge sources. Is it important to understand the contribution of the various knowledge sources used in such a system. This paper investigates the contribution of nine knowledge sources to the performance of the disambiguation models produced for the SemEval-2007 English lexical sample task. The outcome of this analysis will assist future work on WSD in concentrating on the most useful knowledge sources

Theory completion using inverse entailment

The main real-world applications of Inductive Logic Programming (ILP) to date involve the "Observation Predicate Learning" (OPL) assumption, in which both the examples and hypotheses define the same predicate. However, in both scientific discovery and language learning potential applications exist in which OPL does not hold. OPL is ingrained within the theory and performance testing of Machine Learning. A general ILP technique called "Theory Completion using Inverse Entailment" (TCIE) is introduced which is applicable to non-OPL applications. TCIE is based on inverse entailment and is closely allied to abductive inference. The implementation of TCIE within Progol5.0 is described. The implementation uses contra-positives in a similar way to Stickel's Prolog Technology Theorem Prover. Progol5.0 is tested on two different data-sets. The first dataset involves a grammar which translates numbers to their representation in English. The second dataset involves hypothesising the function of unknown genes within a network of metabolic pathways. On both datasets near complete recovery of performance is achieved after relearning when randomly chosen portions of background knowledge are removed. Progol5.0's running times for experiments in this paper were typically under 6 seconds on a standard laptop PC

Transcranial direct current stimulation of the frontal eye fields during pro- and antisaccade tasks

Transcranial direct current stimulation (tDCS) has been successfully applied to cortical areas such as the motor cortex and visual cortex. In the present study, we examined whether tDCS can reach and selectively modulate the excitability of the frontal eye field (FEF). In order to assess potential effects of tDCS, we measured saccade latency, landing point, and its variability in a simple prosaccade task and in an antisaccade task. In the prosaccade task, we found that anodal tDCS shortened the latency of saccades to a contralateral visual cue. However, cathodal tDCS did not show a significant modulation of saccade latency. In the antisaccade task, on the other hand, we found that the latency for ipisilateral antisaccades was prolonged during the stimulation, whereas anodal stimulation did not modulate the latency of antisaccades. In addition, anodal tDCS reduced the erroneous saccades toward the contralateral visual cue. These results in the antisaccade task suggest that tDCS modulates the function of FEF to suppress reflexive saccades to the contralateral visual cue. Both in the prosaccade and antisaccade tasks, we did not find any effect of tDCS on saccade landing point or its variability. Our present study is the first to show effects of tDCS over FEF and opens the possibility of applying tDCS for studying the functions of FEF in oculomotor and attentional performance

Human frontal eye fields and spatial priming of pop-out

"Priming of pop-out" is a form of implicit memory that facilitates detection of a recently inspected search target. Repeated presentation of a target's features or its spatial position improves detection speed (feature/spatial priming). This study investigated a role for the human frontal eye fields (FEFs) in the priming of color pop-out. To test the hypothesis that the FEFs play a role in short-term memory storage, transcranial magnetic stimulation (TMS) was applied during the intertrial interval. There was no effect of TMS on either spatial or feature priming. To test whether the FEFs are important when a saccade is being programmed to a repeated target color or location, TMS was applied during the search array. TMS over the left but not the right FEFs abolished spatial priming, but had no effect on feature priming. These findings demonstrate functional specialization of the left FEFs for spatial priming, and distinguish this role from target discrimination and saccade-related processes. The results suggest that the left FEFs integrate a spatial memory signal with an evolving saccade program, which facilitates saccades to a recently inspected location

Homelessness and access to the informational mainstream

Purpose – This paper aims to explore how homelessness affects access to information serving higher‐level needs such as identity formation and social interaction. Design/methodology/approach – A multi‐disciplinary literature review informed the design of 18 semi‐structured interviews as well as their subsequent analysis. The interview data were intended to be qualitative and exploratory since they addressed a perceived gap in the information and library science literature. Findings – Findings present the ways in which interviewees managed to access information and the way such information helps socialisation and well‐being. Research limitations/implications – The study focused on individuals who were potentially more confident and resourceful. The study is also limited to Glasgow which has relatively good provision for the homeless. Further research in a different locale and among less confident individuals would be necessary to corroborate findings in this regard. Practical implications – The findings confirmed a fundamental research assumption that homeless individuals would pursue higher‐level needs alongside more basic physiological needs. This has practical implications for public libraries' service provision to homeless populations, and also suggests there is greater room for collaboration between libraries and homeless service agencies. Originality/value – The paper addresses a gap in the literature concerning homelessness and higher‐level needs. This has implications for the provision of information and services within both public libraries and organisations serving the homeless. Findings also challenge widespread assumptions regarding the “otherness” or distinctiveness of people who are homeless

Analysing the behaviour of robot teams through relational sequential pattern mining

This report outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a relational representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of sequential behaviours that are characteristic of the team in question, represented by a set of sequences expressed in first-order logic atoms. We propose to use a relational learning algorithm to mine meaningful frequent patterns among the relational sequences to characterise team behaviours. We compared the performance of two teams in the RoboCup four-legged league environment, that have a very different approach to the game. One uses a Case Based Reasoning approach, the other uses a pure reactive behaviour.Comment: 25 page

Learning Chomsky-like grammars for biological sequence families

This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The positive-only learning framework of the Inductive Logic Programming (ILP) system CProgol is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). As far as these authors are aware, this is both the first biological grammar learnt using ILP and the first real-world scientific application of the positive-only learning framework of CProgol. Performance is measured using both predictive accuracy and a new cost function, em Relative Advantage (RA). The RA results show that searching for NPPs by using our best NPP predictor as a filter is more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. The highest RA was achieved by a model which includes grammar-derived features. This RA is significantly higher than the best RA achieved without the use of the grammar-derived features