Semantic Web - Volume 14, issue 2

Authors: Alam, Mehwish | de Boer, Victor | Daga, Enrico | van Erp, Marieke | Hyvönen, Eero | Meroño-Peñuela, Albert

Article Type: Editorial

DOI: 10.3233/SW-223187

Citation: Semantic Web, vol. 14, no. 2, pp. 155-158, 2023

Authors: Candela, Gustavo | Escobar, Pilar | Sáez, María Dolores | Marco-Such, Manuel

Article Type: Research Article

Abstract: Cultural heritage institutions are exploring Semantic Web technologies to publish and enrich their catalogues. Several initiatives, such as Labs, are based on the creative and innovative reuse of the materials published by cultural heritage institutions. In this way, quality has become a crucial aspect to identify and reuse a dataset for research. In this article, we propose a methodology to create Shape Expressions definitions in order to validate LOD datasets published by libraries. The methodology was then applied to four use cases based on datasets published by relevant institutions. It intends to encourage institutions to use ShEx to validate LOD …datasets as well as to promote the reuse of LOD, made openly available by libraries. Show more

Keywords: Linked open data, data quality, libraries, cultural heritage

DOI: 10.3233/SW-210441

Citation: Semantic Web, vol. 14, no. 2, pp. 159-179, 2023

Authors: Ruiz-Calleja, Adolfo | García-Zarza, Pablo | Vega-Gorgojo, Guillermo | Bote-Lorenzo, Miguel L. | Gómez-Sánchez, Eduardo | Asensio-Pérez, Juan I. | Serrano-Iglesias, Sergio | Martínez-Monés, Alejandra

Article Type: Research Article

Abstract: This paper presents Casual Learn, an application that proposes ubiquitous learning tasks about Cultural Heritage. Casual Learn exploits a dataset of 10,000 contextualized learning tasks that were semiautomatically generated out of open data from the Web. Casual Learn offers these tasks to learners according to their physical location. For example, it may suggest describing the characteristics of the Gothic style when passing by a Gothic Cathedral. Additionally, Casual Learn has an interactive mode where learners can geo-search the tasks available. Casual Learn has been successfully used to support three pilot studies in two secondary-school institutions. It has also been awarded …by the regional government and an international research conference. This made Casual Learn to appear in several regional newspapers, radios, and TV channels. Show more

Keywords: Semantic Web, contextualized learning tasks, informal learning

DOI: 10.3233/SW-212907

Citation: Semantic Web, vol. 14, no. 2, pp. 181-195, 2023

Authors: Klic, Lukas

Article Type: Research Article

Abstract: This paper presents ArtVision, a Semantic Web application that integrates computer vision APIs with the ResearchSpace platform, allowing for the matching of similar artworks and photographs across cultural heritage image collections. The field of Digital Art History stands to benefit a great deal from computer vision, as numerous projects have already made good progress in tackling issues of visual similarity, artwork classification, style detection, gesture analysis, among others. Pharos, the International Consortium of Photo Archives, is building its platform using the ResearchSpace knowledge system, an open-source semantic web platform that allows heritage institutions to publish and enrich collections as Linked …Open Data through the CIDOC-CRM, and other ontologies. Using the images and artwork data of Pharos collections, this paper outlines the methodologies used to integrate visual similarity data from a number of computer vision APIs, allowing users to discover similar artworks and generate canonical URIs for each artwork. Show more

Keywords: Linked Open Data, Computer Vision, cultural heritage, Semantic Web, visual similarity

DOI: 10.3233/SW-212893

Citation: Semantic Web, vol. 14, no. 2, pp. 197-208, 2023

Authors: Suissa, Omri | Zhitomirsky-Geffet, Maayan | Elmalech, Avshalom

Article Type: Research Article

Abstract: With the rising popularity of user-generated genealogical family trees, new genealogical information systems have been developed. State-of-the-art natural question answering algorithms use deep neural network (DNN) architecture based on self-attention networks. However, some of these models use sequence-based inputs and are not suitable to work with graph-based structure, while graph-based DNN models rely on high levels of comprehensiveness of knowledge graphs that is nonexistent in the genealogical domain. Moreover, these supervised DNN models require training datasets that are absent in the genealogical domain. This study proposes an end-to-end approach for question answering using genealogical family trees by: (1) representing genealogical …data as knowledge graphs, (2) converting them to texts, (3) combining them with unstructured texts, and (4) training a transformer-based question answering model. To evaluate the need for a dedicated approach, a comparison between the fine-tuned model (Uncle-BERT) trained on the auto-generated genealogical dataset and state-of-the-art question-answering models was performed. The findings indicate that there are significant differences between answering genealogical questions and open-domain questions. Moreover, the proposed methodology reduces complexity while increasing accuracy and may have practical implications for genealogical research and real-world projects, making genealogical data accessible to experts as well as the general public. Show more

Keywords: Question answering, genealogy, neural networks, knowledge graph, natural language processing, Transformers, cultural heritage

DOI: 10.3233/SW-222925

Citation: Semantic Web, vol. 14, no. 2, pp. 209-237, 2023

Authors: Jain, Nitisha | Sierra-Múnera, Alejandro | Ehmueller, Jan | Krestel, Ralf

Article Type: Research Article

Abstract: As machine learning techniques are being increasingly employed for text processing tasks, the need for training data has become a major bottleneck for their application. Manual generation of large scale training datasets tailored to each task is a time consuming and expensive process, which necessitates their automated generation. In this work, we turn our attention towards creation of training datasets for named entity recognition (NER) in the context of the cultural heritage domain. NER plays an important role in many natural language processing systems. Most NER systems are typically limited to a few common named entity types, such as person, …location, and organization. However, for cultural heritage resources, such as digitized art archives, the recognition of fine-grained entity types such as titles of artworks is of high importance. Current state of the art tools are unable to adequately identify artwork titles due to unavailability of relevant training datasets. We analyse the particular difficulties presented by this domain and motivate the need for quality annotations to train machine learning models for identification of artwork titles. We present a framework with heuristic based approach to create high-quality training data by leveraging existing cultural heritage resources from knowledge bases such as Wikidata. Experimental evaluation shows significant improvement over the baseline for NER performance for artwork titles when models are trained on the dataset generated using our framework. Show more

Keywords: Training data generation, named entity recognition, cultural heritage data, weakly-supervised learning

DOI: 10.3233/SW-223177

Citation: Semantic Web, vol. 14, no. 2, pp. 239-260, 2023

Authors: Moraitou, Efthymia | Christodoulou, Yannis | Caridakis, George

Article Type: Research Article

Abstract: Over the last decade, the Cultural Heritage (CH) domain has gradually adopted Semantic Web (SW) technologies for organizing information and for tackling interoperability issues. Several semantic models have been proposed which accommodate essential aspects of information management: retrieval, integration, reuse and sharing. In this context, the CH subdomain of Conservation and Restoration (CnR) exhibits an increasing interest in SW technologies, in an attempt to effectively handle the highly heterogeneous and often secluded CnR information. This paper investigates semantic models relevant to the CnR knowledge domain. The scope, development methodology and coverage of CnR aspects are described and discussed. Furthermore, the …evaluation, deployment and current exploitation of each model are examined, with focus on the types and variety of services provided to support the CnR professional. Through this study, the following research questions are investigated: To what extent the various aspects of CnR are covered by existing CnR models? To what extent existing CnR models incorporate models of the broader CH domain and of relevant disciplines (e.g., Chemistry)? In what ways and to what extent services built upon the reviewed models facilitate CnR professionals in their various tasks? Finally, based on the findings, fields of interest that merit further investigation are suggested. Show more

Keywords: Ontologies, knowledge representation, Semantic Web, metadata schemata, decision-making

DOI: 10.3233/SW-223105

Citation: Semantic Web, vol. 14, no. 2, pp. 261-291, 2023

Authors: El Ghosh, Mirna | Delestre, Nicolas | Kotowicz, Jean-Philippe | Zanni-Merk, Cecilia | Abdulrab, Habib

Article Type: Research Article

Abstract: Graph-based semantic measures have been used to solve problems in several domains. They tend to compare semantic entities in order to estimate their similarity or relatedness. While semantic similarity is applicable to hierarchies or taxonomies, semantic relatedness is adapted to ontologies. In this work, we propose a novel semantic relatedness measure, named Rel Topic , within topic ontologies for topic labeling purposes. In contrast to traditional measures, which are dependent on textual resources, Rel Topic considers semantic properties of entities in ontologies. Thus, correlations of nodes and weights of …nodes and edges are assessed. The pertinence of Rel Topic is evaluated for topic labeling of old press articles. For this purpose, a topic ontology representing the articles, named Topic-OPA, is derived from open knowledge graphs by applying a SPARQL-based automatic approach. A use-case is presented in the context of the old French newspaper Le Matin . The generated topics are evaluated using a dual evaluation approach with the help of human annotators. Our approach shows an agreement quite close to that shown by humans. The entire approach’s reuse is demonstrated for labeling a different context of articles, recent (modern) newspapers. Show more

Keywords: Semantic relatedness, graph-based semantic measures, weighted graphs, topic ontologies, topic labeling, knowledge graphs

DOI: 10.3233/SW-222919

Citation: Semantic Web, vol. 14, no. 2, pp. 293-321, 2023

Authors: Pellegrino, Maria Angela | Scarano, Vittorio | Spagnuolo, Carmine

Article Type: Research Article

Abstract: 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. Show more

Keywords: Community-shared software framework, question-answering, virtual assistant, knowledge graph, SPARQL

DOI: 10.3233/SW-223117

Citation: Semantic Web, vol. 14, no. 2, pp. 323-359, 2023

Authors: Lombardo, Vincenzo | Karatas, Tugce | Gulmini, Monica | Guidorzi, Laura | Angelici, Debora

Article Type: Research Article

Abstract: In recent years, the transdisciplinarity of archaeological studies has greatly increased because of the mature interactions between archaeologists and scientists from different disciplines (called “archaeometers”). A number of diverse scientific disciplines collaborate to get an objective account of the archaeological records. A large amount of digital data support the whole process, and there is a great value in keeping the coherence of information and knowledge, as contributed by each intervening discipline. During the years, a number of representation models have been developed to account for the recording of the archaeological process in data bases. Lately, some semantic models, compliant with …the CRMarchaeo reference model, have been developed to account for linking the institutional forms with the formal knowledge concerning the archaeological excavations and the related findings. On the contrary, the archaeometric processes have not been addressed yet in the Semantic Web community and only an upper reference model, called CRMsci, accounts for the representation of the scientific investigations in general. This paper presents a modular computational ontology for the interlinked representation of all the facts related to the archaeological and archaeometric analyses and interpretations, also connected to the recording catalogues. The computational ontology is compliant with CIDOC-CRM reference models CRMarchaeo and CRMsci and introduces a number of novel classes and properties to merge the two worlds in a joint representation. The ontology is in use in “Beyond Archaeology”, a methodological project for the establishing of a transdisciplinary approach to archaeology and archaeometry, interlinked through a semantic model of processes and objects. Show more

Keywords: Archaeology, CRMarchaeo model, archaeometry modeling, BeArchaeo project

DOI: 10.3233/SW-223016

Citation: Semantic Web, vol. 14, no. 2, pp. 361-383, 2023