[This post is based on the Master Information Sciences project of Fabian Witeczek and reuses text from his thesis. The research is part of VU’s effort in the Intavia project and was co-supervised by Go Sugimoto]
To represent properly temporal data on the Semantic Web, there is a need for an ontology to represent vague or imprecise dates. In the context of his research, Fabian Witeczek developed an ontology that can be used to represent various forms of such vague dates. The engineering process of the ontology started with a requirements analysis that contained the collection of data records from existing Digital Humanities Linked Data sets containing temporally vague dates: Biographynet and Europeana. The occurrences of vagueness were evaluated, and categories of vagueness were defined.
The categories were evaluated through a survey conducted with domain experts in the digital humanities domain. The experts were also questioned about their problems when working with temporally vague dates. The survey results confirmed the meaningfulness of the ontology requirements and the categories of vagueness which were: 1) Unknown deviation, 2) within a time span, 3) before or after a specific date, 4) date options, and 5) complete vagueness.
Based on the findings, the ontology was designed and implemented, scoping to year-granularity only. Lastly, the ontology was tested and evaluated by linking its instances to instances of a historical dataset. This research concludes that the presented vague date ontology offers a clear way to specify how vague dates are and in which regard they are vague. However, the ontology requires much effort to make it work in practice for researchers in digital humanities. This is due to precision and deviation values that need to be set for every record within the datasets.
More information can be found in the Master Thesis, linked below.
This year’s edition of the VU Digital Humanities in Practice course was of course a virtual one. In this course, students of the Minor Digital Humanities and Social Analytics put everything that they have learned in that minor in practice, tackling a real-world DH or Social Analytics challenge. As in previous years, this year we had wonderful projects provided and supervised by colleagues from various institutes. We had projects related to the Odissei and Clariah research infrastructures, projects supervised by KNAW-HUC, Stadsarchief Amsterdam, projects from Utrecht University, UvA, Leiden University and our own Vrije Universiteit. We had a project related to Kieskompas and even a project supervised by researchers from Bologna University. A wide variety of challenges, datasets and domains! We would like to thank all the supervisors and the students on making this course a success.
The compilation video below shows all the projects’ results. It combines 2-minute videos produced by each of the 10 student groups.
After a very nice virtual poster session, everybody got to vote on the Best Poster Award. The winners are group 3, whose video you can also see in the video above. Below we list all the projects and the external supervisors.
Extracting named entities from Social Science data.
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.
Authorship attribution is the process of correctly attributing a publication to its corresponding author, which is often done manually in real-life settings. This task becomes inefficient when there are many options to choose from due to authors having the same name. Authors can be defined by characteristics found in their associated publications, which could mean that machine learning can potentially automate this process. However, authorship attribution tasks introduce a typical class imbalance problem due to a vast number of possible labels in a supervised machine learning setting. To complicate this issue even more, we also use problematic data as input data as this mimics the type of available data for many institutions; data that is heterogeneous and sparse of nature.
The thesis searches for answers regarding how to automate authorship attribution with its known problems and this type of input data, and whether automation is possible in the first place. The thesis considers children’s literature and publications that can have between 5 and 20 potential authors (due to having the same exact name). We implement different types of machine learning methodologies for this method. In addition, we consider all available types of data (as provided by the National Library of the Netherlands), as well as the integration of contextual information.
Furthermore, we consider different types of computational representations for textual input (such as the title of the publication), in order to find the most effective representation for sparse text that can function as input for a machine learning model. These different types of experiments are preceded by a pipeline that consists out of pre-processing data, feature engineering and selection, converting data to other vector space representations and integrating linked data. This pipeline shows to actively improve performance when used with the heterogeneous data inputs.
Ultimately the thesis shows that automation can be achieved in up to 90% of the cases, and in a general sense can significantly reduce costs and time consumption for authorship attribution in a real-world setting and thus facilitate more efficient work procedures. While doing so, the thesis also finds the following key notions:
Between comparison of machine learning methodologies, two methodologies are considered: author classification and similarity learning. Author classification grants the best raw performance (F1. 0.92), but similarity learning provides the most robust predictions and increased explainability (F1. 0.88). For a real life setting with end users the latter is recommended as it presents a more suitable option for integration of machine learning with cataloguers, with only a small hit to performance.
The addition of contextual information actively increases performance, but performance depends on the type of information inclusion. Publication metadata and biographical author information are considered for this purpose. Publication metadata shows to have the best performance (predominantly the publisher and year of publication), while biographical author information in contrast negatively affects performance.
We consider BERT, word embeddings (Word2Vec and fastText) and TFIDF for representations of textual input. BERT ultimately grants the best performance; up to 200% performance increase when compared to word embeddings. BERT is a sophisticated language model with an applied transformer, which leads to more intricate semantic meaning representation of text that can be used to identify associated authors.
Based on surveys and interviews, we also find that end users mostly attribute importance to author related information when engaging in manual authorship attribution. Looking more in depth into the machine learning models, we can see that these primarily use publication metadata features to base predictions upon. We find that such differences in perception of information should ultimately not lead to negative experiences, as multiple options exist for harmonizing both parties’ usage of information.
The course took the form of a 4-week internship at an organization working with humanities or social science data and challenges and student groups were asked to use these skills and knowledge to address a research challenge. Projects ranged from cleaning, indexing, visualizing and analyzing humanities data sets to searching for bias in news coverage of political topics. The students showed their competences not only in their research work but also in communicating this research through great posters.
The complete list of student projects and collaborating institutions is below:
“An eventful 80 years’ war” at Rijksmuseum identifying and mapping historical events from various sources.
An investigation into the use of structured vocabularies also at the Rijksmuseum
“Collecting and Modelling Event WW2 from Wikipedia and Wikidata” in collaboration with Netwerk Oorlogsbronnen (see poster image below)
A project where an search index for Development documents governed by the NICC foundation was built.
“EviDENce: Ego Documents Events modelliNg – how individuals recall mass violence” – in collaboration with KNAW Humanities Cluster (HUC)
“Historical Ecology” – where students searched for mentions of animals in historical newspapers – also with KNAW-HUC
Project MIGRANT: Mobilities and connection project in collaboration with KNAW-HUC and Huygens ING
Capturing Bias with media data analysis – an internal project at VU looking at indentifying media bias
At the DHBenelux 2018 conference, students from the VU minor “Digital Humanities and Social Analytics” presented their final DH in Practice work. In this video, the students talk about their experience in the minor and the internship projects. We also meet other participants of the conference talking about the need for interdisciplinary research.
At the Digital Humanities Benelux 2017 conference, the e-humanities Events working group organized a panel with the titel “A Pragmatic Approach to Understanding and Utilizing Events in Cultural Heritage”. In this panel, researchers from Vrije Universiteit Amsterdam, CWI, NIOD, Huygens ING, and Nationaal Archief presented different views on Events as objects of study and Events as building blocks for historical narratives.
The session was packed and the introductory talks were followed by a lively discussion. From this discussion it became clear that consensus on the nature of Events or what typology of Events would be useful is not to be expected soon. At the same time, a simple and generic data model for representing Events allows for multiple viewpoints and levels of aggregations to be modeled. The combined slides of the panel can be found below. For those interested in more discussion about Events: A workshop at SEMANTICS2017 will also be organized and you can join!
Last week, the Volkswagen Stiftung-funded “Mixed Methods’ in the Humanities?” programme had its kickoff meeting for all funded projects in in Hannover, Germany. Our ArchiMediaL project on enriching and linking historical architectural and urban image collections was one of the projects funded through this programme and even though our project will only start in September, we already presented our approach, the challenges we will be facing and who will face them (our great team of post-docs Tino Mager, Seyran Khademi and Ronald Siebes). Other interesting projects included analysing of multi-religious spaces on the Medieval World (“Dhimmis and Muslims”); the “From Bach to Beatles” project on representing music and schemata to support musicological scholarship as well as the nice Digital Plato project which uses NLP technologies to map paraphrasing of Plato in the ancient world. An overarching theme was a discussion on the role of digital / quantitative / distant reading methods in humanities research. The projects will run for three years so we have some time to say some sensible things about this in 2020.
I received a good news letter from Volkswagen Stiftung who decided to award us a research grant for a 3-year Digital Humanities project named “ArchiMediaL” around architectural history. This project will be a collaboration between architecture historians from TU Delft, computer scientists from TU Delft and VU-Web and Media. A number of German scholars will also be involved as domain experts. The project will combine image analysis software with crowdsourcing and semantic linking to create networks of visual resources which will foster understanding of understudied areas in architectural history.
From the proposal:In the mind of the expert or everyday user, the project detaches the digital images from its existence as a single artifact and includes it into a global network of visual sources, without disconnecting it from its provenance. The project that expands the framework of hermeneutic analysis through a quantitative reference system, in which discipline-specific canons and limitations are questions. For the dialogue between the history of architecture and urban form this means a careful balancing of qualitative and quantitative information and of negotiating new methodological approaches for future investigation.