TY - GEN
T1 - Exploring Heterogeneous Data Graphs Through Their Entity Paths
AU - Barret, Nelly
AU - Gauquier, Antoine
AU - Law, Jia Jean
AU - Manolescu, Ioana
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Graphs, and notably RDF graphs, are a prominent way of sharing data. As data usage democratizes, users need help figuring out the useful content of a graph dataset. In particular, journalists with whom we collaborate [4] are interested in identifying, in a graph, the connections between entities, e.g., people, organizations, emails, etc. We present a novel, interactive method for exploring data graphs through their data paths connecting Named Entities (NEs, in short); each data path leads to a tabular-looking set of results. NEs are extracted from the data through dedicated Information Extraction modules. Our method builds upon the pre-existing ConnectionLens platform [4,5] and followup work on dataset abstraction [8,9]. The contribution of the present work is in the interactive and efficient approach to enumerate and compute NE paths, based on an algorithm which automatically recommends subpaths to materialize, and rewrites the path queries using these subpaths. Our experiments demonstrate the interest of NE paths and the efficiency of our method for computing them.
AB - Graphs, and notably RDF graphs, are a prominent way of sharing data. As data usage democratizes, users need help figuring out the useful content of a graph dataset. In particular, journalists with whom we collaborate [4] are interested in identifying, in a graph, the connections between entities, e.g., people, organizations, emails, etc. We present a novel, interactive method for exploring data graphs through their data paths connecting Named Entities (NEs, in short); each data path leads to a tabular-looking set of results. NEs are extracted from the data through dedicated Information Extraction modules. Our method builds upon the pre-existing ConnectionLens platform [4,5] and followup work on dataset abstraction [8,9]. The contribution of the present work is in the interactive and efficient approach to enumerate and compute NE paths, based on an algorithm which automatically recommends subpaths to materialize, and rewrites the path queries using these subpaths. Our experiments demonstrate the interest of NE paths and the efficiency of our method for computing them.
KW - Data graphs
KW - Graph exploration
KW - Information Extraction
U2 - 10.1007/978-3-031-42914-9_12
DO - 10.1007/978-3-031-42914-9_12
M3 - Conference contribution
AN - SCOPUS:85183801600
SN - 9783031429132
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 163
EP - 179
BT - Advances in Databases and Information Systems - 27th European Conference, ADBIS 2023, Proceedings
A2 - Abelló, Alberto
A2 - Romero, Oscar
A2 - Vassiliadis, Panos
A2 - Wrembel, Robert
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th European Conference on Advances in Databases and Information Systems , ADBIS 2023
Y2 - 4 September 2023 through 7 September 2023
ER -