Predicate Anti-unification in (Constraint) Logic Programming

The concept of anti-unification refers to the process of determining the most specific generalization (msg) of two or more input program objects. In the domain of logic programming, anti-unification has primarily been investigated for computing msgs of tree-like program structures such as terms, atoms, and goals (the latter typically seen as ordered sequences). In this work, we study the anti-unification of whole predicate definitions. We provide a definition of a predicate generalization that allows to characterize the problem of finding the most specific generalization of two predicates as a (computationally hard) search problem. The complexity stems from the fact that a correspondence needs to be constructed between (1) some of the arguments of each of the predicates, (2) some of the clauses in each of the predicate's definitions, and (3) some of the body atoms in each pair of associated clauses. We propose a working algorithm that simultaneously computes these correspondences in a greedy manner. While our algorithm does not necessarily compute the most specific generalization, we conjecture that it allows to compute, in general, a sufficiently good generalization in an acceptable time

Anti-Unification of Unordered Goals

Anti-unification in logic programming refers to the process of capturing common syntactic structure among given goals, computing a single new goal that is more general called a generalization of the given goals. Finding an arbitrary common generalization for two goals is trivial, but looking for those common generalizations that are either as large as possible (called largest common generalizations) or as specific as possible (called most specific generalizations) is a non-trivial optimization problem, in particular when goals are considered to be unordered sets of atoms. In this work we provide an in-depth study of the problem by defining two different generalization relations. We formulate a characterization of what constitutes a most specific generalization in both settings. While these generalizations can be computed in polynomial time, we show that when the number of variables in the generalization needs to be minimized, the problem becomes NP-hard. We subsequently revisit an abstraction of the largest common generalization when anti-unification is based on injective variable renamings, and prove that it can be computed in polynomially bounded time

ScrImmo: A Real-time Web Scraper Monitoring the Belgian Real Estate Market

Web scraping (or Web crawling), a technique for automated data extraction from websites, has emerged as a valuable tool for scientific research and data analysis. This paper presents a comprehensive exploration of Web scraping, its methodologies and challenges.The discussion revolves around a concrete application, namely the automatic extraction of data concerning the Belgian real estate market. We introduce a real-time Web scraper called \scrimmo~and tailored to collect data from websites containing real estate classified ads. The tool is developed in a continuous iterative process and based on an innovative cloud architecture. The paper also briefly addresses the ethical aspects of Web scraping. By integrating insights from previous research and ethical guidelines, this study provides researchers with a comprehensive understanding of Web scraping and its potential benefits, while promoting responsible and ethical practices in data collection and analysis

Anti-unification in Constraint Logic Programming

Anti-unification refers to the process of generalizing two (or more) goals into a single, more general, goal that captures some of the structure that is common to all initial goals. In general one is typically interested in computing what is often called a most specific generalization, that is a generalization that captures a maximal amount of shared structure. In this work we address the problem of anti-unification in CLP, where goals can be seen as unordered sets of atoms and/or constraints. We show that while the concept of a most specific generalization can easily be defined in this context, computing it becomes an NP-complete problem. We subsequently introduce a generalization algorithm that computes a well-defined abstraction whose computation can be bound to a polynomial execution time. Initial experiments show that even a naive implementation of our algorithm produces acceptable generalizations in an efficient way. Under consideration for acceptance in TPLP.Comment: Paper presented at the 35th International Conference on Logic Programming (ICLP 2019), Las Cruces, New Mexico, USA, 20-25 September 2019, 16 page