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Title: NotaryPedia : a knowledge graph of historical notarial manuscripts
Other Titles: On the Move to Meaningful Internet Systems : OTM 2019 Conferences. Lecture Notes in Computer Science
Authors: Ellul, Charlene
Azzopardi, Joel
Abela, Charlie
Keywords: Knowledge representation (Information theory)
Information visualization -- Malta
Ontologies (Information retrieval) -- Malta
Notarial Archives (Valletta, Malta)
Manuscripts -- Data processing
Issue Date: 2019
Publisher: Springer, Cham
Citation: Ellul C., Azzopardi, J., & Abela, C. (2019). NotaryPedia : a knowledge graph of historical notarial manuscripts. In H. Panetto, C. Debruyne, M. Hepp, D. Lewis, C. Ardagna., &, R. Meersman (Eds.), On the Move to Meaningful Internet Systems: OTM 2019 Conferences. Lecture Notes in Computer Science, Vol. 11877 (pp. 626-645). Springer, Cham.
Abstract: The Notarial Archives in Valletta, the capital city of Malta, houses a rich and valuable collection of around twenty thousand notarial manuscripts dating back to the 15th century. The Archive wants to make the contents of this collection easily accessible and searchable to researchers and the general public. Knowledge Graphs have been successfully used to represent similar historical content. Nevertheless, building a Knowledge Graph for the archives is challenging as these documents are written in medieval Latin and currently there is a lack of information extraction tools that recognise this language. This is, furthermore, compounded with a lack of medieval Latin corpora to train and evaluate machine learning algorithms, as well as a lack of an ontological representation for the contents of notarial manuscripts. In this paper, we present NotaryPedia, a Knowledge Graph for the Notarial Archives. We extend our previous work on entity and keyphrase extraction with relation extraction to populate the Knowledge Graph using an ontological vocabulary for notarial deeds. Furthermore, we perform Knowledge Graph completeness using link-prediction and inference. Our work was evaluated using different translational distance and semantic matching models to predict relations amongst literals by promoting them to entities and to infer new knowledge from existing entities. A 49% relation prediction accuracy using TransE was achieved.
ISBN: 9783030332457
Appears in Collections:Scholarly Works - FacICTAI

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