HiDER - Query-Driven Entity Resolution for Historical Data.

Bijan Ranjbar Sahraei*, Julia Efremova, Hossein Rahmani, Toon Calders, Karl Tuyls, Gerhard Weiss

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


Entity Resolution (ER) is the task of finding references that refer to the same entity across different data sources. Cleaning a data warehouse and applying ER on it is a computationally demanding task, particularly for large data sets that change dynamically. Therefore, a query-driven approach which analyses a small subset of the entire data set and integrates the results in real-time is significantly beneficial. Here, we present an interactive tool, called HiDER, which allows for query-driven ER in large collections of uncertain dynamic historical data. The input data includes civil registers such as birth, marriage and death certificates in the form of structured data, and notarial acts such as estate tax and property transfers in the form of free text. The outputs are family networks and event timelines visualized in an integrated way. The HiDER is being used and tested at BHIC center(Brabant Historical Information Center, https://www.bhic.nl); despite the uncertainties of the BHIC input data, the extracted entities have high certainty and are enriched by extra information.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases. ECML PKDD 2015
Place of PublicationCham
PublisherSpringer Nature Switzerland AG
ISBN (Electronic)978-3-319-23461-8
ISBN (Print)978-3-319-23460-1
Publication statusPublished - 2015
EventEuropean Conference on Machine Learning and Practice of Knowledge Discovery in Databases - Porto, Portugal
Duration: 7 Sept 201511 Sept 2015

Publication series

SeriesLecture Notes in Computer Science


ConferenceEuropean Conference on Machine Learning and Practice of Knowledge Discovery in Databases
Abbreviated titleECML-PKDD 2015

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