Extraction of historical information using semantic techniques and knowledge graphs Jorge Álvarez Fidalgo Introduction In the framework of the Semantic Web, knowledge graphs such as Wikidata have emerged as powerful tools to represent and or- ganise large amounts of information in a structured way (linked data). Our aim is to create a methodology that enhances the extraction of information from digitised historical documents by relying on external knowledge graphs, feeding them back in the process. Research questions •RQ1. How can we extract linked data from historical documents? •RQ2. Is it possible to use data from external knowledge graphs to improve the extraction process? •RQ3. Is it possible to detect errors and shortcomings in external graphs from the extracted information? •RQ4. How can we use NLP techniques to improve the accuracy of the linked data obtained? Methodology •To solve RQ1, we extract triples (?head, ?rel, ?tail). •In order to get all ?head -and some ?tail- entities we perform NER using existing M-BERT [1] derived models. •To extract ?rels we propose to perform link prediction on Wikidata in two phases -thus addressing RQ2-. Link Prediction (I). Semi-inductive prediction [2] using tail prediction models trained on Wikidata datasets, such as KGT5 [3]. To restrict the number of properties to be predicted, we use Shape Expressions [4]. Link Prediction (II). Fully inductive prediction [2] in which, based on the information extracted in (I), we define property paths in Wikidata -by means of SPARQL queries- that, if they exist between two entities, indicate a possible relationship between them. Preliminary results To evaluate this approach we are applying these techniques on a corpus of Spanish medieval notarial records consisting of 128 documents [5]. We focus on three types of relationships of varying difficulty for their detection: father-child, spouse and sibling, obtaining high recall [0.60-0.87] yet very low accuracy ([0.06-0.08]). Preliminary conclusions •Most of the relevant triples are extracted successfully. •Those undetected are mostly due to a) incomplete information in the knowledge graph (RQ3) and b) relationships that do not adhere to any pattern. •Accuracy is very low since many non-relevant pairs of entities match the patterns. •RQ4 may be of help in this regard, thus facilitating the work of the human-in-the-loop. Bibliography [1] Telmo Pires, Eva Schlinger, and Dan Garrette. How multilingual is multilingual BERT? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4996–5001, Florence, Italy, July 2019. Association for Computational Linguistics. doi: 10.18653/v1/P19-1493. [2] Mehdi Ali, Max Berrendorf, Mikhail Galkin, Veronika Thost, Tengfei Ma, Volker Tresp, and Jens Lehmann. Improving inductive link prediction using hyper-relational facts. In The Semantic Web – ISWC 2021, pages 74–92, Cham, 2021. Springer International Publishing. ISBN 978-3-030-88361-4. [3] Apoorv Saxena, Adrian Kochsiek, and Rainer Gemulla. Sequence-to-sequence knowledge graph completion and question an- swering. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pages 2814–2828, 01 2022. doi: 10.18653/v1/2022.acl-long.201. [4] Eric Prud’hommeaux, Jose Emilio Labra Gayo, and Harold Solbrig. Shape expressions: an rdf validation and transformation language. In Proceedings of the 10th International Conference on Semantic Systems, SEM ’14, page 32–40, New York, NY, USA, 2014. Association for Computing Machinery. ISBN 9781450329279. doi: 10.1145/2660517.2660523. [5] Jorge Felpeto Cueva and Calleja Miguel Puerta. El archivo de un artesano del siglo XIV: el orfebre Alfonso Fernández de Oviedo. Phd thesis, Universidad de Oviedo, 2023.