Search

Your search keyword '"Lantzaki, Christina"' showing total 6 results
Start Over Author "Lantzaki, Christina"
6 results on '"Lantzaki, Christina"'

1. Tasks that Require, or can Benefit from, Matching Blank Nodes

Author

Lantzaki, Christina and Tzitzikas, Yannis

Subjects
Computer Science - Artificial Intelligence, Computer Science - Databases
Abstract

In various domains and cases, we observe the creation and usage of information elements which are unnamed. Such elements do not have a name, or may have a name that is not externally referable (usually meaningless and not persistent over time). This paper discusses why we will never `escape' from the problem of having to construct mappings between such unnamed elements in information systems. Since unnamed elements nowadays occur very often in the framework of the Semantic Web and Linked Data as blank nodes, the paper describes scenarios that can benefit from methods that compute mappings between the unnamed elements. For each scenario, the corresponding bnode matching problem is formally defined. Based on this analysis, we try to reach to more a general formulation of the problem, which can be useful for guiding the required technological advances. To this end, the paper finally discusses methods to realize blank node matching, the implementations that exist, and identifies open issues and challenges., Comment: 19 pages, 2 figures

Published
2014

2. Generating Synthetic RDF Data with Connected Blank Nodes for Benchmarking

Author

Lantzaki, Christina, Yannakis, Thanos, Tzitzikas, Yannis, Analyti, Anastasia, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Presutti, Valentina, editor, d’Amato, Claudia, editor, Gandon, Fabien, editor, d’Aquin, Mathieu, editor, Staab, Steffen, editor, and Tordai, Anna, editor

Published
2014
Full Text
View/download PDF

3. Blank Node Matching and RDF/S Comparison Functions

Author

Tzitzikas, Yannis, Lantzaki, Christina, Zeginis, Dimitris, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cudré-Mauroux, Philippe, editor, Heflin, Jeff, editor, Sirin, Evren, editor, Tudorache, Tania, editor, Euzenat, Jérôme, editor, Hauswirth, Manfred, editor, Parreira, Josiane Xavier, editor, Hendler, Jim, editor, Schreiber, Guus, editor, Bernstein, Abraham, editor, and Blomqvist, Eva, editor

Published
2012
Full Text
View/download PDF

6. Radius-aware approximate blank node matching using signatures.

Author

Lantzaki, Christina, Papadakos, Panagiotis, Analyti, Anastasia, and Tzitzikas, Yannis

Subjects
ISOMORPHISM (Mathematics), CLOUD computing, BIG data, APPROXIMATION algorithms, STATISTICAL matching
Abstract

In the linked open data cloud, the biggest open data graph that currently exists, a remarkable percentage of data are unnamed resources, also called blank nodes. Several fundamental tasks, such as graph isomorphism checking and RDF data versioning, require computing a map between the sets of blank nodes of two graphs. This map aims at minimizing the delta size, i.e. the number of change operations that are required to make the graphs isomorphic. Computing the optimal map is NP-Hard in the general case, and various approximation algorithms have been proposed. In this work, we propose a novel radius-aware signature-based algorithm that is not restricted to the direct neighborhood of the compared blank nodes. Contrary to the older algorithms, the proposed algorithm manages to decrease the deviation from the optimal solution even for graphs that contain connected blank nodes in large and dense structures. The conducted experiments over real and synthetically generated datasets (including datasets from the Billion Triple Challenge 2012 and 2014) show the significantly smaller deltas. For isomorphism checking (simple RDF equivalence), with a wise configuration of radius, the proposed algorithm achieves optimality for $$100\,\%$$ of the datasets, while in non-isomorphic datasets the deltas are on average 50-75 % smaller than those of the previous algorithms. Finally, the trade-off between radius, deviation from the optimum and time efficiency is analyzed. [ABSTRACT FROM AUTHOR]

Published
2017
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results