LODsyndesis
Global Scale Knowledge Services
Bibliographic Data
| ID | 13124457 |
|---|---|
| Authors | Michalis Mountantonakis (0000-0002-1951-0241, University of Crete, corresponding author), Yannis Tzitzikas (0000-0001-8847-2130, University of Crete, corresponding author) |
| Year | 2018 |
| Volume | 1 |
| Issue | 2 |
| Pages | 335-348 |
| Publication date | 2018-11-17 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Heritage (JOURNAL) |
| Journal identifiers | ISSN: 2571-9408 • E-ISSN: 2571-9408 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/heritage1020023 |
| OpenAlex | W2901328386 |
| Language | EN |
| Citations received | 1 |
| References cited | 13 |
In this paper, we present LODsyndesis, a suite of services over the datasets of the entire Linked Open Data Cloud, which offers fast, content-based dataset discovery and object co-reference. Emphasis is given on supporting scalable cross-dataset reasoning for finding all information about any entity and its provenance. Other tasks that can be benefited from these services are those related to the quality and veracity of data since the collection of all information about an entity, and the cross-dataset inference that is feasible, allows spotting the contradictions that exist, and also provides information for data cleaning or for estimating and suggesting which data are probably correct or more accurate. In addition, we will show how these services can assist the enrichment of existing datasets with more features for obtaining better predictions in machine learning tasks. Finally, we report measurements that reveal the sparsity of the current datasets, as regards their connectivity, which in turn justifies the need for advancing the current methods for data integration. Measurements focusing on the cultural domain are also included, specifically measurements over datasets using CIDOC CRM (Conceptual Reference Model), and connectivity measurements of British Museum data. The services of LODsyndesis are based on special indexes and algorithms and allow the indexing of 2 billion triples in around 80 min using a cluster of 96 computers
Cloud computing · Data mining · Data science · Database · Domain (mathematical analysis · Inference · Information retrieval · Scalability · Scale (ratio · Search engine indexing · Suite · Biomedical Text Mining and Ontologies · Computer Science · Research Data Management Practices · Semantic Web and Ontologies · Artificial Intelligence
| Citations per year | 0.25 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |