Angelos Charalambidis

Formulating description logic learning as an Inductive Logic Programming task

We describe an Inductive Logic Programming (ILP) approach to learning descriptions in Description Logics (DL) under uncertainty. The approach is based on implementing many-valued DL proofs as propositionalizations of the elementary DL constructs and then providing this implementation as background predicates for ILP. The proposed methodology is tested on a many-valued variation of eastbound-trains and Iris, two well known and studied Machine Learning datasets.

SemaGrow: optimizing federated SPARQL queries

Processing SPARQL queries involves the construction of an efficient query plan to guide query execution. Alternative plans can vary in the resources and the amount of time that they need by orders of magnitude, making planning crucial for efficiency. On the other hand, the construction of optimal plans can become computationally intensive and it also operates upon detailed, difficult to obtain, metadata. In this paper we present Semagrow, a federated SPARQL querying system that uses metadata about the federated data sources in order to optimize query execution. We balance between a query optimizer that introduces little overhead, has appropriate fall backs in the absence of metadata, but at the same time produces optimal plans in as many situations as possible. Semagrow also exploits non-blocking and asynchronous stream processing technologies to achieve query execution efficiency and robustness. We also present and analyse empirical results using the FedBench benchmark to compare Semagrow against FedX and SPLENDID. Semagrow clearly outperforms SPLENDID and it is either on a par or much faster than FedX.

Minimum model semantics for extensional higher-order logic programming with negation

Extensional higher-order logic programming has been introduced as a generalization of classical logic programming. An important characteristic of this paradigm is that it preserves all the well-known properties of traditional logic programming. In this paper we consider the semantics of negation in the context of the new paradigm. Using some recent results from non-monotonic fixed-point theory, we demonstrate that every higher-order logic program with negation has a unique minimum infinite-valued model. In this way we obtain the first purely model-theoretic semantics for negation in extensional higher-order logic programming. Using our approach, we resolve an old paradox that was introduced by W. W. Wadge in order to demonstrate the semantic difficulties of higher-order logic programming.

Extensional Higher-Order Logic Programming

We propose a purely extensional semantics for higher-order logic programming. In this semantics program predicates denote sets of ordered tuples, and two predicates are equal iff they are equal as sets. Moreover, every program has a unique minimum Herbrand model which is the greatest lower bound of all Herbrand models of the program and the least fixed-point of an immediate consequence operator. We also propose an SLD-resolution proof system which is proven sound and complete with respect to the minimum Herbrand model semantics. In other words, we provide a purely extensional theoretical framework for higher-order logic programming which generalizes the familiar theory of classical (first-order) logic programming.

The BigDataEurope Platform – Supporting the Variety Dimension of Big Data

The management and analysis of large-scale datasets – described with the term Big Data – involves the three classic dimensions volume, velocity and variety. While the former two are well supported by a plethora of software components, the variety dimension is still rather neglected. We present the BDE platform – an easy-to-deploy, easy-to-use and adaptable (cluster-based and standalone) platform for the execution of big data components and tools like Hadoop, Spark, Flink, Flume and Cassandra. The BDE platform was designed based upon the requirements gathered from seven of the societal challenges put forward by the European Commission in the Horizon 2020 programme and targeted by the BigDataEurope pilots. As a result, the BDE platform allows to perform a variety of Big Data flow tasks like message passing, storage, analysis or publishing. To facilitate the processing of heterogeneous data, a particular innovation of the platform is the Semantic Layer, which allows to directly process RDF data and to map and transform arbitrary data into RDF. The advantages of the BDE platform are demonstrated through seven pilots, each focusing on a major societal challenge.

Efficient Intensional Implementation for Lazy Functional Languages

Abstract.The intensional transformation is a technique that can be used in order to eliminate higher-order functions from a functional program by introducing appropriate context-manipulation operators. The transformation can be applied to a significant class of higher-order programs and results in equivalent zero-order intensional programs that can be executed in a simple demand-driven way. Despite its simplicity, the transformation has never been seriously evaluated with respect to its efficiency and potential. Certain simple implementations of the technique have been performed, but questions regarding the merits of the method have remained inconclusive. In this paper we demonstrate that the transformation can be efficiently implemented by using what we call lazy activation records, namely activation records in which some entries are filled on-demand. An evaluation of our implementation demonstrates that the technique outperforms some of the most well-known functional programming systems, for the class of programs that can be transformed.

Equivalence of two fixed-point semantics for definitional higher-order logic programs ☆ ☆☆

Abstract Two distinct research approaches have been proposed for assigning extensional semantics to higher-order logic programming. The former approach [11] uses classical domain-theoretic tools while the latter [1] builds on a fixed-point construction defined on a syntactic instantiation of the source program. The relationships between these two approaches had not been investigated until now. In this paper we demonstrate that for a very broad class of programs, namely the class of definitional programs introduced by W.W. Wadge, the two approaches coincide with respect to ground atoms that involve symbols of the program. On the other hand, we argue that if existential higher-order variables are allowed to appear in the bodies of program rules, the two approaches are in general different. The results of the paper contribute to a better understanding of the semantics of higher-order logic programming.

Dataset Descriptions for Optimizing Federated Querying

Dataset description vocabularies focus on provenance, versioning, licensing, and similar metadata. VoID is a notable exception, providing some expressivity for describing subsets and their contents and can, to some extent, be used for discovering relevant resources and for optimizing querying. In this poster we describe an extension of VoID that provides the expressivity needed in order to support the query planning methods typically used in federated querying.

Extensional higher-order logic programming

We propose a purely extensional semantics for higher-order logic programming. Under this semantics, every program has a unique minimum Herbrand model which is the greatest lower bound of all Herbrand models of the program and the least fixed-point of the immediate consequence operator of the program. We also propose an SLD-resolution proof procedure which is sound and complete with respect to the minimum model semantics. In other words, we provide a purely extensional theoretical framework for higher-order logic programming which generalizes the familiar theory of classical (first-order) logic programming.

Higher-order logic programming: an expressive language for representing qualitative preferences

We consider the problem of concisely representing and handling preferences in logic programming and relational databases. Our starting point is a well-known proposal [8] which advocates the embedding of first-order preference formulas into relational algebra through a single winnow operator that is parameterized by a database relation and a preference formula. We argue that despite its elegance, the framework of [8] has a number of shortcomings: only intrinsic preference formulas are supported, the preference relations and preference queries are expressed in two different languages, and there is no direct way to define alternative operators beyond winnow. We propose the use of higher-order logic programming as a logical framework that remedies all the above deficiencies. In particular, the proposed framework supports both intrinsic and extrinsic preference formulas, it can represent both preference relations as-well-as queries, and it can be used to define a variety of interesting alternative operators beyond winnow. We demonstrate the feasibility of our approach by presenting an implementation of all the proposed concepts in the higher-order logic programming language Hilog.