Kleanthi Georgala

Paderborn University | UPB · Department of Computer Science

The Linked Data paradigm builds upon the backbone of distributed knowledge bases connected by typed links. The mere volume of current knowledge bases as well as their sheer number pose two major challenges when aiming to support the computation of links across and within them. The first is that tools for link discovery have to be time-efficient whe...

With the growth in number and variety of RDF datasets comes an increasing need for both scalable and accurate solutions to support link discovery at instance level within and across these datasets. In contrast to ontology matching, most linking frameworks rely solely on string similarities to this end. The limited use of semantic similarities when...

Modern data-driven frameworks often have to process large amounts of data periodically. Hence, they often operate under time or space constraints. This also holds for Linked Data-driven frameworks when processing RDF data, in particular, when they perform link discovery tasks. In this work, we present a novel approach for link discovery under const...

The aim of the Mighty Storage Challenge (MOCHA) at ESWC 2018 was to test the performance of solutions for SPARQL processing in aspects that are relevant for modern applications. These include ingesting data, answering queries on large datasets and serving as backend for applications driven by Linked Data. The challenge tested the systems against da...

With the growth of the number and the size of RDF datasets comes an increasing need for scalable solutions to support the linking of resources. Most Link Discovery frameworks rely on complex link specifications for this purpose. We address the scalability of the execution of link specifications by presenting the first dynamic planning approach for...

The aim of the Mighty Storage Challenge (MOCHA) at ESWC 2017 was to test the performance of solutions for SPARQL processing in aspects that are relevant for modern applications. These include ingesting data, answering queries on large datasets and serving as backend for applications driven by Linked Data. The challenge tested the systems against da...

Time-efficient link discovery is of central importance to implement the vision of the Semantic Web. Some of the most rapid Link Discovery approaches rely internally on planning to execute link specifications. In newer works, linear models have been used to estimate the runtime of the fastest planners. However, no other category of models has been s...

Time-efficient link discovery is of central importance to implement the vision of the Semantic Web. Some of the most rapid Link Discovery approaches rely internally on planning to execute link specifications. In newer works, linear models have been used to estimate the runtime the fastest planners. However, no other category of models has been stud...

Event data is increasingly being represented according to the Linked Data principles. The need for large-scale machine learning on data represented in this format has thus led to the need for efficient approaches to compute RDF links between resources based on their temporal properties. Time-efficient approaches for computing links between RDF reso...

This book addresses the problems that are encountered, and solutions that have been proposed, when we aim to identify people and to reconstruct populations under conditions where information is scarce, ambiguous, fuzzy and sometimes erroneous. The process from handwritten registers to a reconstructed digitized population consists of three major ph...

This chapter covers the topic of record linkage in historic texts, specifically documents from the Middle Ages and Early Modern period. The challenge of record linkage, in general, is to analyze large collections of data recording people, with the aim of recognizing links between these people, and deciding whether multiple mentions of people actual...

This paper introduces a method that deals with unwanted mail messages by combining active learning with incremental clustering. The proposed approach is motivated by the fact that the user cannot provide the correct category for all received messages. The email messages are divided into chronological batches (e.g. one per day). The user is asked to...