A visual exploration workflow as enabler for the exploitation of Linked Open Data

Abstract. Semantically annotating and interlinking Open Data results in Linked Open Data which concisely and unambiguously describes a knowledge domain. However, the uptake of the Linked Data depends on its usefulness to non-Semantic Web experts. Failing to support data consumers to understand the added-value of Linked Data and possible exploitation opportunities could inhibit its diffusion. In this paper, we propose an interactive visual workflow for discovering and ex-ploring Linked Open Data. We implemented the workflow considering academic library metadata and carried out a qualitative evaluation. We assessed the work-flow’s potential impact on data consumers which bridges the offer: published Linked Open Data; and the demand as requests for: (i) higher quality data; and (ii) more applications that re-use data. More than 70 % of the 34 test users agreed that the workflow fulfills its goal: it facilitates non-Semantic Web experts to un-derstand the potential of Linked Open Data.

Ontology-Based Recommendation of Editorial Products

Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have addressed this issue by creating Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University (OU) in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues. SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products. This is based on the Computer Science Ontology, a very large-scale, automatically generated taxonomy of research areas. SBR also allows users to investigate why a certain publication was suggested by the system. It does so by means of an interactive graph view that displays the topic taxonomy of the recommended editorial product and compares it with the topic-centric characterization of the input conference. An evaluation carried out with seven Springer Nature editors and seven OU researchers has confirmed the effectiveness of the solution

Indexing mathematical scholarly papers as linked open data

We present our work on developing an open source software platform for mining Linked Open Data (LOD) representation for a given collection of mathematical scholarly papers. Currently, the LOD cloud lacks up-to-date data on professional level mathematics. The main reason behind this is due to practical difficulties arising while dealing with such severe documents for indexing as mathematical papers that abound with formulas and specific structural elements ignored by the most state-of-the-art academic search engines. Our proof of concept demonstrates a feasible approach to parse these documents properly, dissect the semantics of their significant parts with the help of the ad hoc math-aware vocabulary, and publish their contents and metadata as RDF data. The authors argue that the platform at the final stage of its development cycle may be helpful for modern online scientific collections. For our experimental setup, we choose Math-Net.Ru – a digital collection well-known in the Russian mathematical community

Using the AcademySampo Portal and Data Service for Biographical and Prosopographical Research in Digital Humanities

Peer reviewe

LODE: Linking Digital Humanities Content to the Web of Data

Numerous digital humanities projects maintain their data collections in the form of text, images, and metadata. While data may be stored in many formats, from plain text to XML to relational databases, the use of the resource description framework (RDF) as a standardized representation has gained considerable traction during the last five years. Almost every digital humanities meeting has at least one session concerned with the topic of digital humanities, RDF, and linked data. While most existing work in linked data has focused on improving algorithms for entity matching, the aim of the LinkedHumanities project is to build digital humanities tools that work "out of the box," enabling their use by humanities scholars, computer scientists, librarians, and information scientists alike. With this paper, we report on the Linked Open Data Enhancer (LODE) framework developed as part of the LinkedHumanities project. With LODE we support non-technical users to enrich a local RDF repository with high-quality data from the Linked Open Data cloud. LODE links and enhances the local RDF repository without compromising the quality of the data. In particular, LODE supports the user in the enhancement and linking process by providing intuitive user-interfaces and by suggesting high-quality linking candidates using tailored matching algorithms. We hope that the LODE framework will be useful to digital humanities scholars complementing other digital humanities tools

Extending the Finnish Linked Data Infrastructure with Natural Language Processing Services in FIN-CLARIAH

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WarVictimSampo 1914–1922: a National War Memorial on the Semantic Web for Digital Humanities Research and Applications

AcceptedThis article presents the semantic portal and Linked Open Data service WARVICTIMSAMPO 1914-1922 about the war victims, battles, and prisoner camps in the Finnish Civil and other wars in 1914-1922. The system is based on a database of the National Archives of Finland and additional related data created, compiled, and linked during the project. The system contains detailed information about some 40,000 deaths extracted from several data sources and data about over 1,000 battles of the Civil War. A key novelty of WARVICTIMSAMPO 1914-1922 is the integration of ready-to-use Digital Humanities visualizations and data analysis tooling with semantic faceted search and data exploration, which allows, e.g., studying data about wider prosopographical groups in addition to individual war victims. The article focuses on demonstrating how the tools of the portal, as well as the underlying SPARQL endpoint openly available on the Web, can be used to explore and analyze war history in flexible and visual ways. WARVICTIMSAMPO 1914-1922 is a new member in the series of "Sampo" model-based semantic portals. The portal is in use and has had 23,000 users, including both war historians and the general public seeking information about their deceased relatives.Peer reviewe

Online Index Extraction from Linked Open Data Sources

The production of machine-readable data in the form of RDF datasets belonging to the Linked Open Data (LOD) Cloud is growing very fast. However, selecting relevant knowledge sources from the Cloud, assessing the quality and extracting synthetical information from a LOD source are all tasks that require a strong human effort. This paper proposes an approach for the automatic extraction of the more representative information from a LOD source and the creation of a set of indexes that enhance the description of the dataset. These indexes collect statistical information regarding the size and the complexity of the dataset (e.g. the number of instances), but also depict all the instantiated classes and the properties among them, supplying user with a synthetical view of the LOD source. The technique is fully implemented in LODeX, a tool able to deal with the performance issues of systems that expose SPARQL endpoints and to cope with the heterogeneity on the knowledge representation of RDF data. An evaluation on LODeX on a large number of endpoints (244) belonging to the LOD Cloud has been performed and the effectiveness of the index extraction process has been presented

Building Lightweight Ontologies for Faceted Search with Named Entity Recognition: Case WarMemoirSampo

Peer reviewe