Linked open data in authoring virtual exhibitions

PurposeIn the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects.Design/methodology/approachThis study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness.FindingsThis study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity.Originality/valueThe main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

Last years witnessed a shift from the potential utility in digitisation to a crucial need to enjoy activities virtually. In fact, before 2019, data curators recognised the utility of performing data digitisation, while during the lockdown caused by the COVID-19, investing in virtual and remote activities to make culture survive became crucial as no one could enjoy Cultural Heritage in person. The Cultural Heritage community heavily invested in digitisation campaigns, mainly modelling data as Knowledge Graphs by becoming one of the most successful Semantic Web technologies application domains. Despite the vast investment in Cultural Heritage Knowledge Graphs, the syntactic complexity of RDF query languages, e.g., SPARQL, negatively affects and threatens data exploitation, risking leaving this enormous potential untapped. Thus, we aim to support the Cultural Heritage community (and everyone interested in Cultural Heritage) in querying Knowledge Graphs without requiring technical competencies in Semantic Web technologies. We propose an engaging exploitation tool accessible to all without losing sight of developers’ technological challenges. Engagement is achieved by letting the Cultural Heritage community leave the passive position of the visitor and actively create their Virtual Assistant extensions to exploit proprietary or public Knowledge Graphs in question-answering. By accessible to all, we mean that the proposed software framework is freely available on GitHub and Zenodo with an open-source license. We do not lose sight of developers’ technical challenges, which are carefully considered in the design and evaluation phases. This article first analyses the effort invested in publishing Cultural Heritage Knowledge Graphs to quantify data developers can rely on in designing and implementing data exploitation tools in this domain. Moreover, we point out challenges developers may face in exploiting them in automatic approaches. Second, it presents a domain-agnostic Knowledge Graph exploitation approach based on virtual assistants as they naturally enable question-answering features where users formulate questions in natural language directly by their smartphones. Then, we discuss the design and implementation of this approach within an automatic community-shared software framework (a.k.a. generator) of virtual assistant extensions and its evaluation in terms of performance and perceived utility according to end-users. Finally, according to a taxonomy of the Cultural Heritage field, we present a use case for each category to show the applicability of the proposed approach in the Cultural Heritage domain. In overviewing our analysis and the proposed approach, we point out challenges that a developer may face in designing virtual assistant extensions to query Knowledge Graphs, and we show the effect of these challenges in practice.

Open data is becoming increasingly popular in a wide range of service domains; however, most open datasets in Taiwan remain separate. The lack of linked open data (LOD) makes it difficult to locate and combine open datasets for the creation of innovative applications. In this study, we sought to facilitate the spread of open data in Taiwan using a novel approach referred to as define–produce–invoke (DPI). The proposed scheme employs a newly defined data query language, called LODQL (LOD query language), to allow the definition of rules for the generation of LOD by data experts. We also developed an LOD engine, which is able to produce linked open datasets and allow application developers to access LOD by invoking RESTful services. This scheme also allows data visualizations indicating the relevance of open datasets and the associations among open data items. Experiments demonstrate the feasibility and effectiveness of the proposed DPI approach.

In cultural heritage, many projects execute Named Entity Linking (NEL) through global Linked Open Data (LOD) references in order to identify and disambiguate entities in their local datasets. It allows users to obtain extra information and contextualise the data with it. Thus, the aggregation and integration of heterogeneous LOD are expected. However, such development is still limited partly due to data quality issues. In addition, analysis on the LOD quality has not sufficiently been conducted for cultural heritage. Moreover, most research on data quality concentrates on ontology and corpus level observations. This paper examines the quality of the eleven major LOD sources used for NEL in cultural heritage with an emphasis on instance-level connectivity and graph traversals. Standardised linking properties are inspected for 100 instances/entities in order to create traversal route maps. Other properties are also assessed for quantity and quality. The outcomes suggest that the LOD is not fully interconnected and centrally condensed; the quantity and quality are unbalanced. Therefore, they cast doubt on the possibility of automatically identifying, accessing, and integrating known and unknown datasets. This implies the need for LOD improvement, as well as the NEL strategies to maximise the data integration.

Bridging the gap between linked open data-based recommender systems and distributed representations

Historical letters in archives are typically stored in fonds, based on letter recipients. To analyze the correspondences of a correspondent x, one has to aggregate the received letters in x’s own fond with the letters in the fonds of the correspondents yi who have received letters from x. To address this challenge, epistolary data services have been created by aggregating data from distributed heterogeneous archival data silos and fonds. This paper presents an overview of a new in-use data service and portal for this task, LetterSampo Finland – Finnish Nineteenth-Century Letters on the Semantic Web. In contrast to various legacy services online, this system is based on a Linked Open Data (LOD) Knowledge Graph (KG) in a SPARQL endpoint aggregated and harmonized from distributed heterogeneous data sources, in our case from 16 Finnish cultural heritage organizations and over 1,600 fonds. The new massive KG contains metadata about nearly 1.3 million letters sent or received in the Grand Duchy of Finland during 1809–1917, including also letter contents from four critical editions of correspondences of prominent Finns. The paper shows how this new KG and a portal on top of it can be used for searching and browsing letter data and for data analysis in digital humanities research. We show how the aggregated datasets are related to and enrich each other, pinpointing semantic challenges of data aggregation and linking processes needed. This kind of analysis is needed to make enriched LOD more transparent to the end user and to enhance data literacy for reliable computational analyzes.

Linked Open Data (LOD) is a huge effort in the direction of making the Web of Data a reality. The LOD cloud consists of about 200 datasets contributed by several independent data providers from various domains such as Publications, Geography, Government, Media, Biology and Drugs etc. The data is represented in RDF and SPARQL is the query language. Due to the open nature of the data and its heterogeneous origin, it is often the case that a real-world entity appears in different datasets and with different names. When such entities are to be reported in the query results, a mechanism of rank ordering them becomes essential. In this paper, we propose a new framework for calculating the importance scores of datasets and also resources inside the datasets. As consensus is a key element that adds value to the data in the Web of Data, we base our ranking framework on constructs that are used to express sameness or equivalence among the entities in RDF data. We also make use of the underlying graph structure of LOD in the framework. The framework is experimentally verified on the Billion Triple Challenge (BTC-2010) dataset. The results indicate that the framework is successful in giving entities from the most relevant dataset a higher score compared to other datasets.

Faceted Search Over RDF-Based Knowledge Graphs

Semantic-Based Data Access to Digital Cultural Heritage: A Qualitative Analysis of Linked Open Data Implementation

Companies and institutions now realize the potential of Linked Open Data (LOD) and they start publishing their own data as LOD. However, publishing LOD is still a challenging task. One of the main reasons is a lack of user friendly tooling which would properly support the whole LOD publishing process. The process typically consists of source data extraction, transformation to RDF, alignment with commonly used vocabularies, linking to other datasets, computing metadata, publishing on the web as a dump, loading into a triplestore and recording the dataset in a data catalog such as CKAN. In this paper we present LinkedPipes ETL, a tool for ETL-like LOD publishing, which mainly focuses on supporting such LOD publishing workflows in a user friendly way. In addition, the tool also eases consumption of already existing LOD data sources as it addresses some of the practical issues associated with it. Finally, the tool itself uses Linked Data technologies for representation of the ETL processes. We describe LinkedPipes ETL and its main distinguishing features in context of the use cases in which the tool has already been deployed. They include an institution of public administration, a municipality, a university, a software company and an open data initiative.

Knowledge graphs have become a vital tool for structuring and accessing cultural heritage data, offering new ways to connect and interpret historical information. As the volume and complexity of heritage data grow, integrating advanced computational methods becomes essential to enhance their accuracy, usability, and accessibility. This paper explores emerging trends in cultural heritage knowledge graphs, focusing on the role of artificial intelligence and machine learning in improving entity recognition, contextualization, and knowledge enrichment. It examines how natural language processing and deep learning techniques can refine data interpretation and automate updates, leading to dynamic, self-sustaining knowledge graphs. Additionally, the study highlights the integration of knowledge graphs with immersive technologies such as augmented reality and virtual reality, which offer interactive ways to engage with heritage content. Finally, it discusses the impact of linked open data initiatives in fostering crossinstitutional collaboration and global accessibility. These advancements collectively redefine how cultural heritage is studied and experienced, making it more interconnected, interactive, and adaptable to new discoveries.

This paper proposes information visualization system for exploratory LOD (Linked Open Data) Analysis. The LOD is a framework to make data open to the public. Recently, it has been widely used to publish various kinds of data such as statistical data, geographical data, and academic data. The RDF (Resource Description Framework), which describes data as a set of triples consisting of subject, predicate and object, is commonly used to publish data as LOD. When we want to use LOD, it is necessary to understand its structure, such as graph structure of RDF data, used vocabularies and resources. Therefore, we often have to conduct exploratory analysis of LOD. In order to support the analysis, the proposed system analyzes the structure of LOD written with RDF, and visualizes the result of analysis. As most of currently available LOD have table structure, the proposed system identifies whether target dataset contains table structure or not using resource sampling with SPARQL queries. Graph structure of resources obtained by the resource sampling is visualized by assigning different colors to different tables. A user cannot only examine the visualized structure, but also conduct exploratory search by selecting a resource in the visualized result. Effectiveness of the proposed system is evaluated by applying it to several LOD resources. Experiments with test participants are also conducted, of which results show even users who are not familiar with RDF can perform exploratory analysis effectively.

The web of Linked Open Data (LOD) has a prominent and rapid evolution recently. Over the last few years, LOD had developed to involve a wide range of various domains. Due to these facts, and the great interconnections among linked open datasets, linked data integration task had gained a huge attention and became a focal point of research. LOD applications aim to incorporate data from different LOD sources. Unfortunately, these sources of data are heterogeneous in schema and/or in vocabularies. Due to this heterogeneity, numerous challenges are emerging that have to be overcome. In this paper, a LOD integration framework is proposed, which aims to tackle these challenges. It works on integrating academic LOD datasets that reside in different LOD repositories with intrinsic schema and vocabularies heterogeneity. An automatic mapping technique in the integration processes is proposed in this paper. Consequently, an obvious decrease in execution time for the entire integration process, as well as, a great progress in the integrated data quality assessment metrics has been achieved.

Publication and Usage of Official Czech Pension Statistics Linked Open Data

Cultural heritage is a valuable wealth of human civilization, which needs to be effectively protected and inherited. Knowledge graph is a technology capable of representing and reasoning about complex knowledge, which can provide powerful support for the digitization, intelligence and revitalization of cultural heritage. This paper discusses the methods and effects of the development and analysis of the cultural heritage protection and inheritance platform based on knowledge graph, which mainly includes the following aspects: (1) Introducing the concepts, characteristics, and application fields of knowledge graph, and analyzing the roles and values of knowledge graph in the protection and inheritance of cultural heritage; (2) designing the architecture and functions of a cultural heritage protection and inheritance platform based on knowledge graph, which include four modules of knowledge graph construction, query, application, and update, introduced the technical principle and realization method of each module, respectively, and demonstrated the interface and operation flow of the platform; (3) evaluated the effect and performance of the platform, and carried out experimental design and result analysis from four aspects of the construction, query, application, and update of the knowledge graph, which proved the feasibility and validity of the platform; and (4) discussed the platform’s innovation points and advantages of the platform, analyzed the theoretical and practical significance of the platform, pointed out the shortcomings and room for improvement of the platform, and looked forward to the development prospects and application scenarios of the platform. The method and effect of the development and analysis of the platform for the protection and inheritance of cultural heritage based on knowledge graph proposed in this paper provide a new idea and way for the digital protection and inheritance of cultural heritage, as well as a new paradigm and case for the research and application of knowledge graph, which has certain academic value and social significance.