At this year’s Metadata and Semantics Research Conference (MTSR2020), I just presented our work on Linked Data Scopes: an ontology to describe data manipulation steps. The paper was co-authored with Ivette Bonestroo, one of our Digital Humanities minor students as well as Rik Hoekstra and Marijn Koolen from KNAW-HUC. The paper builds on earlier work by the latter two co-authors and was conducted in the context of the CLARIAH-plus project.
With the rise of data driven methods in the humanities, it becomes necessary to develop reusable and consistent methodological patterns for dealing with the various data manipulation steps. This increases transparency, replicability of the research. Data scopes present a qualitative framework for such methodological steps. In this work we present a Linked Data model to represent and share Data Scopes. The model consists of a central Data scope element, with linked elements for data Selection, Linking, Modeling, Normalisation and Classification. We validate the model by representing the data scope for 24 articles from two domains: Humanities and Social Science.
[This blog post is based on the Master thesis Information Sciences of Bram Schmidt, conducted at the KNAW Humanities cluster and IISG. It reuses text from his thesis]
Place names (toponyms) are very ambiguous and may change over time. This makes it hard to link mentions of places to their corresponding modern entity and coordinates, especially in a historical context. We focus on historical Toponym Disambiguation approach of entity linking based on identified context toponyms.
The thesis specifically looks at the American Gazetteer. These texts contain fundamental information about major places in its vicinity. By identifying and exploiting these tags, we aim to estimate the most likely position for the historical entry and accordingly link it to its corresponding contemporary counterpart.
Therefore, in this case study, Bram Schmidt examined the toponym recognition performance of state-of-the-art Named Entity Recognition (NER) tools spaCy and Stanza concerning historical texts and we tested two new heuristics to facilitate efficient entity linking to the geographical database of GeoNames.
We tested our method against a subset of manually annotated records of the gazetteer. Results show that both NER tools do function insufficiently in their task to automatically identify relevant toponyms out of the free text of a historical lemma. However, exploiting correctly identified context toponyms by calculating the minimal distance among them proves to be successful and combining the approaches into one algorithm shows improved recall score.