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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., This is a preprint of a paper accepted for publication at the 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

We present the SYNTHESIS documentation system and its use in the context of a large European research project of Art History, called RICONTRANS. SYNTHESIS is Web-based, multilingual, and configurable for use in other digital humanities fields. It focuses on semantic interoperability and achieves this by making use of standards for data modelling (CIDOC-CRM). The aim is the production of data with high value, longevity and long-term validity., published and presented at Digital Humanities 2022 (DH2022), July 25-29, 2022. https://dh2022.adho.org/

For ordinary users, the task of accessing knowledge graphs through structured query languages like SPARQL is rather demanding. As a result, various approaches exploit the simpler and widely used keyword-based search paradigm, either by translating keyword queries to structured queries, or by adopting classical information retrieval (IR) techniques. In this paper, we study and adapt Elasticsearch, an out-of-the-box document-centric IR system, for supporting keyword search over RDF datasets. Contrary to other works that mainly retrieve entities, we opt for retrieving triples, due to their expressiveness and informativeness. We specify the set of functional requirements and study the emerging questions related to the selection and weighting of the triple data to index, and the structuring and ranking of the retrieved results. Finally, we perform an extensive evaluation of the different factors that affect the IR performance for four different query types. The reported results are promising and offer useful insights on how different Elasticsearch configurations affect the retrieval effectiveness and efficiency.

An increasing number of organisations in almost all fields have started adopting semantic web technologies for publishing their data as open, linked and interoperable (RDF) datasets, queryable through the SPARQL language and protocol. Link traversal has emerged as a SPARQL query processing method that exploits the Linked Data principles and the dynamic nature of the Web to dynamically discover data relevant for answering a query by resolving online resources (URIs) during query evaluation. However, the execution time of link traversal queries can become prohibitively high for certain query types due to the high number of resources that need to be accessed during query execution. In this paper we propose and evaluate baseline methods for estimating the evaluation cost of link traversal queries. Such methods can be very useful for deciding on-the-fly the query execution strategy to follow for a given query, thereby reducing the load of a SPARQL endpoint and increasing the overall reliability of the query service. To evaluate the performance of the proposed methods, we have created (and make publicly available) a ground truth dataset consisting of 2,425 queries.

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)

AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multi-disciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful Machine Learning (ML) algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features like race, sex, etc., 19 pages, 1 figure

Publicly available social media archives facilitate research in a variety of fields, such as data science, sociology or the digital humanities, where Twitter has emerged as one of the most prominent sources. However, obtaining, archiving and annotating large amounts of tweets is costly. In this paper, we describe TweetsKB, a publicly available corpus of currently more than 1.5 billion tweets, spanning almost 5 years (Jan'13-Nov'17). Metadata information about the tweets as well as extracted entities, hashtags, user mentions and sentiment information are exposed using established RDF/S vocabularies. Next to a description of the extraction and annotation process, we present use cases to illustrate scenarios for entity-centric information exploration, data integration and knowledge discovery facilitated by TweetsKB.

The federated query extension of SPARQL 1.1 allows executing queries distributed over different SPARQL endpoints. SPARQL-LD is a recent extension of SPARQL 1.1 which enables to directly query any HTTP web source containing RDF data, like web pages embedded with RDFa, JSON-LD or Microformats, without requiring the declaration of named graphs. This makes possible to query a large number of data sources (including SPARQL endpoints, online resources, or even Web APIs returning RDF data) through a single one concise query. However, not optimal formulation of SPARQL 1.1 and SPARQL-LD queries can lead to a large number of calls to remote resources which in turn can lead to extremely high query execution times. In this paper, we address this problem and propose a set of query reordering methods which make use of heuristics to reorder a set of SERVICE graph patterns based on their restrictiveness, without requiring the gathering and use of statistics from the remote sources. Such a query optimization approach is widely applicable since it can be exploited on top of existing SPARQL 1.1 and SPARQL-LD implementations. Evaluation results show that query reordering can highly decrease the query-execution time, while a method that considers the number and type of unbound variables and joins achieves the optimal query plan in 88% of the cases.

In this paper we introduce a method for exploiting entities from the emerging Web of Data for enhancing various Information Retrieval (IR) services. The approach is based on named-entity recognition applied in a set of search results, and on a graph of documents and identified entities that is constructed dynamically and analyzed stochastically using a Random Walk method. The result of this analysis is exploited in two different contexts: for automatic query expansion and for re-ranking a set of retrieved results. Evaluation results in the 2015 TREC Clinical Decision Support Track illustrate that query expansion can increase recall by retrieving more relevant hits, while re-ranking can notably improve the ranked list of results by moving relevant but low-ranked hits in higher positions.

SPARQL is a standard query language for retrieving and manipulating RDF data.However, the majority of SPARQL implementations require the data to be available in advance, i.e., to exist in main memory or in a RDF repository (accessible through a SPARQL endpoint). Nonetheless, Linked Data exists in the Web in various forms; even an HTML Web page can contain RDF data through RDFa, or RDF data may be dynamically created by a Web Service. In this paper, we propose and demonstrate an extension of SPARQL 1.1, called SPARQL-LD, that allows to directly and flexibly exploit this wealth of data. SPARQL-LD allows to fetch, query and integrate in the same SPARQL query: i) data coming from online RDF or JSON-LD files, ii) data coming from dereferenceable URIs, iii) data embedded in Web pages as RDFa, iv) data that is dynamically created by Web Services, and v) data coming by querying other endpoints. A distinctive characteristic of this extension is that it enables to fetch and query even data in datasets returned by a portion of the query, i.e. discovered at query-execution time.&nbsp

This book chapter describes the process and the tools used for building and exploring a rich semantic network of Maritime History data that integrates and consolidates information from a variety of historical archival sources, such as crew lists, civil registers and census data. The process starts with the collaborative transcription of a set of archival sources by historians, supported through the FAST CAT system. The transcribed data are curated and enriched through the collaborative system FAST CAT TEAM, before they are transformed into a semantic network using the X3ML data transformation framework and a data model compatible with the CIDOC-CRM ontology. The derived semantic network is made available for exploration by end-users through ResearchSpace, a Web application that allows browsing the network, running complex queries and visualizing the results in various ways, like in tables, charts or in a map., Preprint of: Fafalios, P., Samaritakis, G., Petrakis, K., Doerr, K., Kritsotaki, A., Axaridou, A., & Doerr, M. (2022). "Chapter 17 Building and Exploring a Semantic Network of Maritime History Data". In Mediterranean Seafarers in Transition. Leiden, The Netherlands: Brill. DOI: https://doi.org/10.1163/9789004514195