Learning from Ordinal Data with Inductive Logic Programming in Description Logic

Here we describe a Description Logic (DL) based Inductive Logic Programming (ILP) algorithm for learning relations of order. We test our algorithm on the task of learning user preferences from pairwise comparisons. The results have implications for the development of customised recommender systems for e-commerce, and more broadly, wherever DL-based representations of knowledge, such as OWL ontologies, are used. The use of DL makes for easy integration with such data, and produces hypotheses that are easy to interpret by novice users. The proposed algorithm outperforms SVM, Decision Trees and Aleph on data from two domains

Using ILP to Detect Anomalies in Pipelines

Simulated data generated from an accurate modelling tool can demonstrate real-life events. This approach mimics pipeline opera- tion without the need for maintaining the original anomaly record that is already scarce in the pipeline industry. This synthetic data carries precise signatures and the shape of the curve depicts the type of alarm. Learning rules can be inferred from this parametric data and the examples are con- strued from the threshold levels. The issue is addressed by considering the method that lessens data handling and the associated complexity of the problem. Probabilistic ILPs can be the most appropriate candidate for classifying anomalies of this nature. A logic program addresses this issue by learning the parameters of a program given the structure (the rules), using the ability to incorporate probability in logic programming, interpreting the examples for target predicate, and refining background knowledge for the dissemination of discoveries. This synthetic data also develops a direct link with the ILP parameter learning for competing hypotheses. The modelling tool will receive feedback, check and tune the hypothesis until exclusivity is evolved. This will fall in the domain of closed-loop active learnin

Pairwise Comparisons in a Logic-Based Recommender System

In this paper, we propose a recommender system using pair- wise comparisons as the main source of information in the user pref- erence elicitation process. We use a logic-based approach implemented in APARELL, an inductive learner modelling the user's preferences in description logic. A within-subject preliminary user study with a large dataset from a real-world domain (car retail) was conducted to compare pairwise comparison interface to one using standard product list search. The results show the users' preference for the interface based on pairwise comparisons, which has proven signifcantly better in a number of ways

On the benefit of logic-based approach to learn pairwise comparisons

In recent years, many daily processes such as internet web searching, e-mail filtering, social media services, e-commerce have benefited from Machine Learning (ML) techniques. The implementation of ML techniques has been largely focused on black box methods where the general conclusions are not easily interpretable. Hence, the elaboration with other declarative software models to identify the correctness and completeness of the models is not easy to perform. On the other hand, the emerge of some logic-based machine learning approaches that can overcome such limitations with their white box methods has been proven to be well-suited for many software engineering tasks. In this paper, we propose the use of a logic-based approach to learn user preference in the form of pairwise comparisons. APARELL as a novel approach of inductive learning is able to model the user’s preferences in Description Logic(DL) and then build a model by generalising the concept for all examples given. This offers a rich, relational representation beyond the usual propositional domain, which is then can be used to produce a set of recommendations. A user study has been performed in our experiment to evaluate the implementation of pairwise preference recommender system when compared to a standard list interface. The result of the experiment shows that the pairwise interface was significantly better than the other interface in many ways

Detecting Causal Links between Financial News and Stocks

This article describes a novel framework for the detection of causal links between financial news and the subsequent movements of the stock market. The approach builds on and substantially improves a previously published in-house design for the detection and measurement of correlation between news and time series in the financial domain, which has been used here to detect a predictive causality relationship from news to prices and volumes of trade. While the original framework makes use of matrices of pairwise distances between companies, one based on news, the other - on financial performance, in order to produce a single measure of correlation between these two types of information for all traded companies, this article shows how the company contributing the most to the news-to-price/volume causal link can be singled out. The potential benefits of such information are made clear through its use in a straight-forward trading strategy, the results of which compare favourably to two strong, real-life alternatives that only make use of the time series