Details
| Original language | English |
|---|---|
| Title of host publication | Database and Expert Systems Applications |
| Subtitle of host publication | 30th International Conference, DEXA 2019, Linz, Austria, August 26–29, 2019, Proceedings |
| Editors | Sven Hartmann, Josef Küng, Gabriele Anderst-Kotsis, Ismail Khalil, Sharma Chakravarthy, A Min Tjoa |
| Pages | 175-185 |
| Number of pages | 11 |
| Volume | I |
| ISBN (electronic) | 978-3-030-27615-7 |
| Publication status | Published - 3 Aug 2019 |
| Event | 30th International Conference on Database and Expert Systems Applications, DEXA 2019 - Linz, Austria Duration: 26 Aug 2019 → 29 Aug 2019 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11706 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Context-specific description of entities –expressed in RDF– poses challenges during data-driven tasks, e.g., data integration, and context-aware entity matching represents a building-block for these tasks. However, existing approaches only consider inter-schema mapping of data sources, and are not able to manage several contexts during entity matching. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and context-based similarity metrics to match contextually equivalent entities. COMET executes a novel 1-1 perfect matching algorithm for matching contextually equivalent entities based on the combined scores of semantic similarity and context similarity. COMET employs the Formal Concept Analysis algorithm in order to compute the context similarity of RDF entities. We empirically evaluate the performance of COMET on a testbed from DBpedia. The experimental results suggest that COMET is able to accurately match equivalent RDF graphs in a context-dependent manner.
Keywords
- Data integration, RDF entities, RDF knowledge graphs
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Database and Expert Systems Applications: 30th International Conference, DEXA 2019, Linz, Austria, August 26–29, 2019, Proceedings. ed. / Sven Hartmann; Josef Küng; Gabriele Anderst-Kotsis; Ismail Khalil; Sharma Chakravarthy; A Min Tjoa. Vol. I 1. ed. 2019. p. 175-185 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11706 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - COMET
T2 - 30th International Conference on Database and Expert Systems Applications, DEXA 2019
AU - Tasnim, Mayesha
AU - Collarana, Diego
AU - Graux, Damien
AU - Galkin, Mikhail
AU - Vidal, Maria Esther
N1 - Funding information: This research was supported by the European project QualiChain (number 822404).
PY - 2019/8/3
Y1 - 2019/8/3
N2 - Context-specific description of entities –expressed in RDF– poses challenges during data-driven tasks, e.g., data integration, and context-aware entity matching represents a building-block for these tasks. However, existing approaches only consider inter-schema mapping of data sources, and are not able to manage several contexts during entity matching. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and context-based similarity metrics to match contextually equivalent entities. COMET executes a novel 1-1 perfect matching algorithm for matching contextually equivalent entities based on the combined scores of semantic similarity and context similarity. COMET employs the Formal Concept Analysis algorithm in order to compute the context similarity of RDF entities. We empirically evaluate the performance of COMET on a testbed from DBpedia. The experimental results suggest that COMET is able to accurately match equivalent RDF graphs in a context-dependent manner.
AB - Context-specific description of entities –expressed in RDF– poses challenges during data-driven tasks, e.g., data integration, and context-aware entity matching represents a building-block for these tasks. However, existing approaches only consider inter-schema mapping of data sources, and are not able to manage several contexts during entity matching. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and context-based similarity metrics to match contextually equivalent entities. COMET executes a novel 1-1 perfect matching algorithm for matching contextually equivalent entities based on the combined scores of semantic similarity and context similarity. COMET employs the Formal Concept Analysis algorithm in order to compute the context similarity of RDF entities. We empirically evaluate the performance of COMET on a testbed from DBpedia. The experimental results suggest that COMET is able to accurately match equivalent RDF graphs in a context-dependent manner.
KW - Data integration
KW - RDF entities
KW - RDF knowledge graphs
UR - http://www.scopus.com/inward/record.url?scp=85077123658&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-27615-7_13
DO - 10.1007/978-3-030-27615-7_13
M3 - Conference contribution
AN - SCOPUS:85077123658
SN - 978-3-030-27614-0
VL - I
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 175
EP - 185
BT - Database and Expert Systems Applications
A2 - Hartmann, Sven
A2 - Küng, Josef
A2 - Anderst-Kotsis, Gabriele
A2 - Khalil, Ismail
A2 - Chakravarthy, Sharma
A2 - Tjoa, A Min
Y2 - 26 August 2019 through 29 August 2019
ER -