9 research outputs found
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Experiential Observations: an Ontology Pattern-based Study on Capturing the Potential Content within Evidences of Experiences
Modelling the knowledge behind human experiences is a complex process: it should take into account, among others, the activities performed, human observations, and the documentation of the evidence. To represent this knowledge in a declarative way means to support data interoperability in the context of cultural heritage artefacts, as linked datasets on experience documentation have started to appear. With this objective in mind, we describe a study based on an Ontology Design Pattern for modelling experiences through observations, which are considered indirect evidence of a mental process (i.e., the experience). This pattern highlights the structural differences between types of experiential documentation, such as diaries and social media, providing a guideline for the comparability between different domains and for supporting the construction of heterogeneous datasets based on an epistemic compatibility. We have performed not only a formal evaluation over the pattern, but also an assessment through a series of case studies. This approach includes a) the analysis of interoperability among two case studies (reading through social media and historical sources); b) the development of an ontology for collecting evidences of reading, which reuses the proposed pattern; and c) the inspection of experience in humanities datasets
Proceedings of the 15th ISWC workshop on Ontology Matching (OM 2020)
15th International Workshop on Ontology Matching co-located with the 19th International Semantic Web Conference (ISWC 2020)International audienc
Machine Learning Algorithm for the Scansion of Old Saxon Poetry
Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools
deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We
implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon
and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and
we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm
reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested
the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that
the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input
verses
Actas del XXIV Workshop de Investigadores en Ciencias de la ComputaciĂłn: WICC 2022
Compilación de las ponencias presentadas en el XXIV Workshop de Investigadores en Ciencias de la Computación (WICC), llevado a cabo en Mendoza en abril de 2022.Red de Universidades con Carreras en Informátic