Short Biography

Ilias Tachmazidis is currently a Research Fellow at the University of Huddersfield, UK and a member of the PARK (Planning, Autonomy and Representation of Knowledge) research group. As a PhD Candidate at the University of Huddersfield, UK he has investigated whether, how and to what extend large-scale reasoning can be achieved through mass parallelization, while being supervised by Prof. Grigoris Antoniou. He graduated from the University of Crete, Greece receiving both M.Sc. and B.Sc. in 2012 and 2010 respectively. His research interests fall in: Knowledge Representation and Reasoning, Semantic Web, High performance Computing and Distributed Computing.

Video: Presenting at ESWC 2017

Presenting at the 14th Extended Semantic Web Conference (ESWC2017),  Portotoz, Slovenia, May 28 - June 1, 2017.

Title: "A Hypercat-enabled Semantic Internet of Things Data Hub"
Authors: Ilias Tachmazidis, Sotiris Batsakis, John Davies, Alistair Duke, Mauro Vallati, Grigoris Antoniou and Sandra Stincic Clarke
Presentation video: Videolectures ESWC 2017

Abstract: An increasing amount of information is generated from the rapidly increasing number of sensor networks and smart devices. A wide variety of sources generate and publish information in different formats, thus highlighting interoperability as one of the key prerequisites for the success of Internet of Things (IoT). The BT Hypercat Data Hub provides a focal point for the sharing and consumption of available datasets from a wide range of sources. In this work, we propose a semantic enrichment of the BT Hypercat Data Hub, using well-accepted Semantic Web standards and tools. We propose an ontology that captures the semantics of the imported data and present the BT SPARQL Endpoint by means of a mapping between SPARQL and SQL queries. Furthermore, federated SPARQL queries allow queries over multiple hub-based and external data sources. Finally, we provide two use cases in order to illustrate the advantages afforded by our semantic approach.

Presenting at ESWC 2016 Tutorial

Presenting at a half day tutorial at the 13th European Semantic Web Conference (ESWC 2016), Anissaras, Crete, Greece, May 29, 2016.

Title: "Improving Quality and Scalability in Semantic Data Management"

  • Vassilis Christophides, University of Crete, Greece
  • Oscar Corcho, Universidad Politécnica de Madrid, Spain
  • Vasilis Efthymiou, University of Crete and ICS-FORTH, Greece
  • Vadim Ermolayev, Zaporizhzhya National University, Ukraine
  • Andreas Harth, Karlsruhe Institute of Technology, Germany
  • Luis-Daniel Ibáñez, University of Southampton, UK
  • Natalya Keberle, Zaporizhzhya National University, Ukraine
  • Kostas Stefanidis, ICS-FORTH, Greece
  • Ilias Tachmazidis, University of Huddersfield, UK

This tutorial presents and discusses several complementary techniques which are important to help solve the challenges of improving quality and scalability of Semantic Data Management, giving an account to distributed nature, dynamics and evolution of the data. In its choice of the techniques it focuses on finding solutions to several important problems: (i) Refined temporal representation - the developments regarding the required temporal features which help cope with dataset dynamics and evolution are presented; (ii) Ontology learning and knowledge extraction - the methodology is presented allowing to extract a consensual set of community requirements from the relevant professional document corpus, refine the ontology, and evaluate the quality of this refinement; (iii) Distribution, autonomy, consistency and trust - the approach to implement a Read/Write Linked Open Data coping with participants autonomy and trust at scale is presented; (iv) Entity resolution - the approaches to revisit traditional entity resolution workflows for to cope with the new challenges stemming from the Web openness and heterogeneity, data variety, complexity, and scale are discussed; (v) Large scale reasoning - the existing platforms and techniques that allow parallel and scalable data processing, enabling large-scale reasoning based on rule and data partitioning over various logics are overviewed.

Presenting at ICTAI 2014

Presenting at the 26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2014), Limassol, Cyprus, November 10-12, 2014.

Title: "Massively Parallel Reasoning under the Well-Founded Semantics using X10"
Authors: Ilias Tachmazidis, Long Cheng, Spyros Kotoulas, Grigoris Antoniou and Tomas E. Ward

Academia and industry are investigating novel approaches for processing vast amounts of data coming from enterprises, the Web, social media and sensor readings in an area that has come to be known as Big Data. Logic programming has traditionally focused on complex knowledge structures/programs. The question arises whether and how it can be applied in the context of Big Data. In this paper, we study how the well-founded semantics can be computed over huge amounts of data using mass parallelization. Specifically, we propose and evaluate a parallel approach based on the X10 programming language. Our experiments demonstrate that our approach has the ability to process up to 1 billion facts within minutes.

Presenting at ISWC 2014 Tutorial

Presenting at a full day tutorial at the 13th International Semantic Web Conference (ISWC 2014) Riva del Garda, Trentino Italy, 19-23 Oct, 2014.

Title: "Large Scale Reasoning Over Semantic Data"
Presenters: Jeff Z. Pan, Guilin Qi, Raghava Mutharaju and Ilias Tachmazidis

The tutorial aims to provide an overview of the approaches used for large scale reasoning over semantic data, the systems developed as well as the lessons learned while developing them. We will discuss some applications which require scalable reasoning solutions. Questions such as what makes distributed/parallel reasoning hard would also be covered during the tutorial. Directions for future research work would be discussed.

Presenting at ICLP 2014

Presenting at the 30th International Conference on Logic Programming (ICLP 2014), Vienna, Austria, July 19-22, 2014.

Title: "Efficient Computation of the Well-Founded Semantics over Big Data"
Authors: Ilias Tachmazidis, Grigoris Antoniou and Wolfgang Faber

Data originating from the Web, sensor readings and social media result in increasingly huge datasets. The so called Big Data comes with new scientific and technological challenges while creating new opportunities, hence the increasing interest in academia and industry. Traditionally, logic programming has focused on complex knowledge structures/programs, so the question arises whether and how it can work in the face of Big Data. In this paper, we examine how the well-founded semantics can process huge amounts of data through mass parallelization. More specifically, we propose and evaluate a parallel approach using the MapReduce framework. Our experimental results indicate that our approach is scalable and that well-founded semantics can be applied to billions of facts. To the best of our knowledge, this is the first work that addresses large scale nonmonotonic reasoning without the restriction of stratification for predicates of arbitrary arity.

Joining PARK Research Group

Ilias Tachmazidis is now a research student at PARK: Planning, Autonomy and Representation of Knowledge research group of University of Huddersfield, UK. PARK is a research group within Artificial Intelligence that covers Automated Planning, Knowledge Engineering, Knowledge Representation and Reasoning, and Ontological Engineering.