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We describe FastCat Catalogues, a Web application that supports researchers studying archival material, such as historians, in exploring and quantitatively analysing the data (transcripts) of archival documents. The application was designed based on real information needs provided by a large group of researchers, makes use of JSON technology, and is configurable for use over any type of archival documents whose contents have been transcribed and exported in JSON format. The supported functionalities include a) source- or record-specific entity browsing, b) source-independent entity browsing, c) data filtering, d) inspection of provenance information, e) data aggregation and visualisation in charts, f) table and chart data export for further (external) analysis. The application is provided as open source and is currently used by historians in maritime history research., Comment: This is a preprint of a paper accepted for publication at the 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

A vast area of research in historical science concerns the documentation and study of artefacts and related evidence. Current practice mostly uses spreadsheets or simple relational databases to organise the information as rows with multiple columns of related attributes. This form offers itself for data analysis and scholarly interpretation, however it also poses problems including i) the difficulty for collaborative but controlled documentation by a large number of users, ii) the lack of representation of the details from which the documented relations are inferred, iii) the difficulty to extend the underlying data structures as well as to combine and integrate data from multiple and diverse information sources, and iv) the limitation to reuse the data beyond the context of a particular research activity. To support historians to cope with these problems, in this paper we describe the Synthesis documentation system and its use by a large number of historians in the context of an ongoing research project in the field of History of Art. The system is Web-based and collaborative, and makes use of existing standards for information documentation and publication (CIDOC-CRM, RDF), focusing on semantic interoperability and the production of data of high value and long-term validity., Comment: This is a preprint of an article accepted for publication at the 20th International Semantic Web Conference (ISWC 2021)

Descriptive and empirical sciences, such as History, are the sciences that collect, observe and describe phenomena in order to explain them and draw interpretative conclusions about influences, driving forces and impacts under given circumstances. Spreadsheet software and relational database management systems are still the dominant tools for quantitative analysis and overall data management in these these sciences, allowing researchers to directly analyse the gathered data and perform scholarly interpretation. However, this current practice has a set of limitations, including the high dependency of the collected data on the initial research hypothesis, usually useless for other research, the lack of representation of the details from which the registered relations are inferred, and the difficulty to revisit the original data sources for verification, corrections or improvements. To cope with these problems, in this paper we present FAST CAT, a collaborative system for assistive data entry and curation in Digital Humanities and similar forms of empirical research. We describe the related challenges, the overall methodology we follow for supporting semantic interoperability, and discuss the use of FAST CAT in the context of a European (ERC) project of Maritime History, called SeaLiT, which examines economic, social and demographic impacts of the introduction of steamboats in the Mediterranean area between the 1850s and the 1920s., Comment: This is a preprint of an article accepted for publication at the ACM Journal on Computing and Cultural Heritage (JOCCH)

Web archiving is the process of collecting portions of the Web to ensure that the information is preserved for future exploitation. However, despite the increasing number of web archives worldwide, the absence of efficient and meaningful exploration methods still remains a major hurdle in the way of turning them into a usable and useful information source. In this paper, we focus on this problem and propose an RDF/S model and a distributed framework for building semantic profiles ("layers") that describe semantic information about the contents of web archives. A semantic layer allows describing metadata information about the archived documents, annotating them with useful semantic information (like entities, concepts and events), and publishing all this data on the Web as Linked Data. Such structured repositories offer advanced query and integration capabilities, and make web archives directly exploitable by other systems and tools. To demonstrate their query capabilities, we build and query semantic layers for three different types of web archives. An experimental evaluation showed that a semantic layer can answer information needs that existing keyword-based systems are not able to sufficiently satisfy., Comment: This is a preprint of an article accepted for publication in the International Journal on Digital Libraries (2018)

How did the popularity of the Greek Prime Minister evolve in 2015? How did the predominant sentiment about him vary during that period? Were there any controversial sub-periods? What other entities were related to him during these periods? To answer these questions, one needs to analyze archived documents and data about the query entities, such as old news articles or social media archives. In particular, user-generated content posted in social networks, like Twitter and Facebook, can be seen as a comprehensive documentation of our society, and thus meaningful analysis methods over such archived data are of immense value for sociologists, historians and other interested parties who want to study the history and evolution of entities and events. To this end, in this paper we propose an entity-centric approach to analyze social media archives and we define measures that allow studying how entities were reflected in social media in different time periods and under different aspects, like popularity, attitude, controversiality, and connectedness with other entities. A case study using a large Twitter archive of four years illustrates the insights that can be gained by such an entity-centric and multi-aspect analysis., Comment: This is a preprint of an article accepted for publication in the International Journal on Digital Libraries (2018)

Archived collections of documents (like newspaper archives) serve as important information sources for historians, journalists, sociologists and other interested parties. Semantic Layers over such digital archives allow describing and publishing metadata and semantic information about the archived documents in a standard format (RDF), which in turn can be queried through a structured query language (e.g., SPARQL). This enables to run advanced queries by combining metadata of the documents (like publication date) and content-based semantic information (like entities mentioned in the documents). However, the results returned by structured queries can be numerous and also they all equally match the query. Thus, there is the need to rank these results in order to promote the most important ones. In this paper, we focus on this problem and propose a ranking model that considers and combines: i) the relativeness of documents to entities, ii) the timeliness of documents, and iii) the relations among the entities.

Archived collections of documents (like newspaper and web archives) serve as important information sources in a variety of disciplines, including Digital Humanities, Historical Science, and Journalism. However, the absence of efficient and meaningful exploration methods still remains a major hurdle in the way of turning them into usable sources of information. A semantic layer is an RDF graph that describes metadata and semantic information about a collection of archived documents, which in turn can be queried through a semantic query language (SPARQL). This allows running advanced queries by combining metadata of the documents (like publication date) and content-based semantic information (like entities mentioned in the documents). However, the results returned by such structured queries can be numerous and moreover they all equally match the query. In this paper, we deal with this problem and formalize the task of "ranking archived documents for structured queries on semantic layers". Then, we propose two ranking models for the problem at hand which jointly consider: i) the relativeness of documents to entities, ii) the timeliness of documents, and iii) the temporal relations among the entities. The experimental results on a new evaluation dataset show the effectiveness of the proposed models and allow us to understand their limitations