TY - CHAP

T1 - Holistic and scalable ranking of RDF data

AU - Ngomo, Ngonga

AU - Ngomo, Ngonga

AU - Hoffmann, Michael

AU - Usbeck, Ricardo

AU - Jha, Kunal

N1 - Conference code: 5

PY - 2017/7/1

Y1 - 2017/7/1

N2 - The volume and number of data sources published using Semantic Web standards such as RDF grows continuously. The largest of these data sources now contain billions of facts and are updated periodically. A large number of applications driven by such data sources requires the ranking of entities and facts contained in such knowledge graphs. Hence, there is a need for time-efficient approaches that can compute ranks for entities and facts simultaneously. In this paper, we present the first holistic ranking approach for RDF data. Our approach, dubbed HARE, allows the simultaneous computation of ranks for RDF triples, resources, properties and literals. To this end, HARE relies on the representation of RDF graphs as bi-partite graphs. It then employs a time-efficient extension of the random walk paradigm to bi-partite graphs. We show that by virtue of this extension, the worst-case complexity of HARE is O(n5) while that of PageRank is O(n6). In addition, we evaluate the practical efficiency of our approach by comparing it with PageRank on 6 real and 6 synthetic datasets with sizes up to 108 triples. Our results show that HARE is up to 2 orders of magnitude faster than PageRank. We also present a brief evaluation of HARE's ranking accuracy by comparing it with that of PageRank applied directly to RDF graphs. Our evaluation on 19 classes of DBpedia demonstrates that there is no statistical difference between HARE and PageRank. We hence conclude that our approach goes beyond the state of the art by allowing the ranking of all RDF entities and of RDF triples without being worse w.r.t. the ranking quality it achieves on resources. HARE is open-source and is available at http://github.com/dice-group/hare.

AB - The volume and number of data sources published using Semantic Web standards such as RDF grows continuously. The largest of these data sources now contain billions of facts and are updated periodically. A large number of applications driven by such data sources requires the ranking of entities and facts contained in such knowledge graphs. Hence, there is a need for time-efficient approaches that can compute ranks for entities and facts simultaneously. In this paper, we present the first holistic ranking approach for RDF data. Our approach, dubbed HARE, allows the simultaneous computation of ranks for RDF triples, resources, properties and literals. To this end, HARE relies on the representation of RDF graphs as bi-partite graphs. It then employs a time-efficient extension of the random walk paradigm to bi-partite graphs. We show that by virtue of this extension, the worst-case complexity of HARE is O(n5) while that of PageRank is O(n6). In addition, we evaluate the practical efficiency of our approach by comparing it with PageRank on 6 real and 6 synthetic datasets with sizes up to 108 triples. Our results show that HARE is up to 2 orders of magnitude faster than PageRank. We also present a brief evaluation of HARE's ranking accuracy by comparing it with that of PageRank applied directly to RDF graphs. Our evaluation on 19 classes of DBpedia demonstrates that there is no statistical difference between HARE and PageRank. We hence conclude that our approach goes beyond the state of the art by allowing the ranking of all RDF entities and of RDF triples without being worse w.r.t. the ranking quality it achieves on resources. HARE is open-source and is available at http://github.com/dice-group/hare.

KW - Data Volume

KW - PageRank

KW - Ranking

KW - Scalability

KW - Semantic Web

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85047828136&partnerID=8YFLogxK

U2 - 10.1109/BigData.2017.8257990

DO - 10.1109/BigData.2017.8257990

M3 - Article in conference proceedings

AN - SCOPUS:85047828136

SN - 978-1-5386-2714-3

SN - 978-1-5386-2716-7

T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017

SP - 746

EP - 755

BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017

A2 - Nie, Jian-Yun

A2 - Obradovic, Zoran

A2 - Suzumura, Toyotaro

A2 - Ghosh, Rumi

A2 - Nambiar, Raghunath

A2 - Wang, Chonggang

A2 - Zang, Hui

A2 - Baeza-Yates, Ricardo

A2 - Baeza-Yates, Ricardo

A2 - Hu, Xiaohua

A2 - Kepner, Jeremy

A2 - Cuzzocrea, Alfredo

A2 - Tang, Jian

A2 - Toyoda, Masashi

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on Big Data, Big Data 2017

Y2 - 11 December 2017 through 14 December 2017

ER -