Move cultural heritage knowledge graphs in everyone’s pocket

Linked open data in authoring virtual exhibitions

Open Government Data (OGD) is being published by various public administration organizations around the globe. Within the metadata of OGD data catalogs, the publishing organizations (1) are not uniquely and unambiguously identifiable and, even worse, (2) change over time, by public administration units being merged or restructured. In order to enable fine-grained analyzes or searches on Open Government Data on the level of publishing organizations, linking those from OGD portals to publicly available knowledge graphs (KGs) such as Wikidata and DBpedia seems like an obvious solution. Still, as we show in this position paper, organization linking faces significant challenges, both in terms of available (portal) metadata and KGs in terms of data quality and completeness. We herein specifically highlight five main challenges, namely regarding (1) temporal changes in organizations and in the portal metadata, (2) lack of a base ontology for describing organizational structures and changes in public knowledge graphs, (3) metadata and KG data quality, (4) multilinguality, and (5) disambiguating public sector organizations. Based on available OGD portal metadata from the Open Data Portal Watch, we provide an in-depth analysis of these issues, make suggestions for concrete starting points on how to tackle them along with a call to the community to jointly work on these open challenges.KeywordsOpen dataDataset evolutionEntity linkingKnowledge graphsKnowledge graph evolution

Over the past decade, Knowledge Graphs (KG) have emerged as a prominent repository for storing facts about the world in a linked data architecture. Providing machines with the capability of exploring such Knowledge Graphs and answering natural language questions over them, has been an active area of research. The purpose of this work, is to delve further into the research of retrieving information stored in KGs, based on the natural language questions posed by the user. Knowledge Graph Question Answering (KGQA) aims to produce a concise answer to a user question, such that the user is exempt from using KG vocabulary and overheads of learning a formal query language. Existing KGQA systems have achieved excellent results over Simple Questions, where the information required is limited to a single triple and a single formal query pattern. Our motivation is to improve the performance of KGQA over Complex Questions, where formal query patterns significantly vary, and a single triple is not confining for all the required information. Complex KGQA provides several challenges such as understanding semantics and syntactic structure of questions, Entity Linking, Relation Linking and Answer Representation. Lack of suitable datasets for complex question answering further adds to research gaps. Hence, in this thesis, we focus the research objective of laying the foundations for the advancement of the state-of-the-art for Complex Question Answering over Knowledge Graphs, by providing techniques to solve various challenges and provide resources to fill the research gaps. First, we propose Normalized Query Structure (NQS), which is a linguistic analyzer module that helps the QA system to detect inputs and intents and the relation between them in the users' question. NQS acts like an intermediate language between natural language questions and formal expressions to ease the process of query formulation for complex questions. We then developed a framework named LC-QuAD to generate large scale question answering dataset by reversing the process of question answering, thereby translating natural language questions from the formal query using intermediate templates. Our goal is to use this framework for high variations in the query patterns and create a large size dataset with minimum human effort. The first version of the dataset consists of 5,000 complex questions. By extending the LC-QuAD framework to support Reified KGs and crowd-sourcing, we published the second version of the dataset as LC-QuAD 2.0, consisting of 30,000 questions with their paraphrases and has higher complexity and new variations in the questions. To overcome the problem of Entity Linking and Relation Linking in KGQA, we develop EARL, a module performing these two tasks as a single joint task for complex question answering. We develop approaches for this module, first by formalizing the task as an instance of the Generalized Traveling Salesman Problem (GTSP) and the second approach uses machine learning to exploit the connection density between nodes in the Knowledge Graph. Lastly, we create another large scale dataset to answer verbalization and provide results for multiple baseline system s on it. The Verbalization dataset is introduced to make the system's response more human-like. The NQS based KGQA system was next to the best system in terms of accuracy on the QALD-5 dataset. We empirically prove that NQS is robust to tackle paraphrases of the questions. EARL achieves the state of the art results in Entity Linking and Relation Linking for question answering on several KGQA datasets. The dataset curated in this thesis has helped the research community to move forward in the direction of improving the accuracy of complex question answering as a task as other researchers too developed several KGQA systems and modules around these published datasets. With the large-scale datasets, we have encouraged the use of large scale machine learning, deep learning and emergence of new techniques to advance the state-of-the-art in complex question answering over knowledge graphs. We further developed core components for the KGQA pipeline to overcome the challenges of Question Understanding, Entity-Relation Linking and Answer Verbalization and thus achieve our research objective. All the proposed approaches mentioned in this thesis and the published resources are available at https://github.com/AskNowQA and are released under the umbrella project AskNow.

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.

SParseQA: Sequential word reordering and parsing for answering complex natural language questions over knowledge graphs

Entity-centric knowledge graphs (KGs) are now popular to collect facts about entities. KGs have rich schemas with a large number of different types and predicates to describe the entities and their relationships. On these rich schemas, logical rules are used to represent dependencies between the data elements. While rules are useful in query answering, data curation, and other tasks, they usually do not come with the KGs. Such rules have to be manually defined or discovered with the help of rule mining methods. We believe this rule-collection task should be done collectively to better capitalize our understanding of the data and to avoid redundant work conducted on the same KGs. For this reason, we introduce RuleHub , our extensible corpus of rules for public KGs. RuleHub provides functionalities for the archival and the retrieval of rules to all users, with an extensible architecture that does not constrain the KG or the type of rules supported. We are populating the corpus with thousands of rules from the most popular KGs and report on our experiments on automatically characterizing the quality of a rule with statistical measures.

Recently, there has been an upsurge in the number of knowledge graphs (KG) that can only be accessed by experts. Non-expert users lack an adequate understanding of the queried knowledge graph's vocabulary and structure, as well as the syntax of the structured query language used to express the user's information needs. To increase the user base of these KGs, a set of Question Answering (QA) systems that use natural language to query these knowledge graphs have been introduced. However, finding a benchmark that accurately evaluates the quality of a QA system is a difficult task due to (1) the high degree of variation in the fine-grained properties among the existing benchmarks, (2) the static nature of the existing benchmarks versus the evolving nature of KGs, and (3) the limited number of KGs targeted by existing benchmarks, which hinders the usability of QA systems in real-world deployment over KGs that are different from those that were used in the evaluation of the QA systems. In this paper, we introduce Maestro, a benchmark generation system for question answering over knowledge graphs. Maestro can generate a new benchmark for any KG given the KG and, optionally, a text corpus that covers this KG. The benchmark generated by Maestro is guaranteed to cover all the properties of the natural language questions and queries that were encountered in the literature as long as the targeted KG includes these properties. Maestro also generates high-quality natural language questions with various utterances that are on par with manually-generated ones to better evaluate QA systems.

Top-[formula omitted] star queries on knowledge graphs through semantic-aware bounding match scores

Cultural heritage is a challenging domain of application for novel interactive technologies, where varying aspects in the way that cultural assets are delivered play a major role in enhancing the visitor experience, either onsite or online. Technology-supported natural human–computer interaction that is based on multimodalities is a key factor in enabling wider and enriched access to cultural heritage assets. In this paper, we present the design and evaluation of an interactive system that aims to support visitors towards a better understanding of art contexts through the use of a multimodal interface, based on visual and audio interactions. The results of the evaluation study shed light on the dimensions of evoking natural interactions within cultural heritage environments, using micro-narratives for self-exploration and understanding of cultural content, and the intersection between human–computer interaction and artificial intelligence within cultural heritage. We expect our findings to provide useful insights for practitioners and researchers of the broad human–computer interaction and cultural heritage communities on designing and evaluating multimodal interfaces to better support visitor experiences.

We present GQBE, a system that presents a simple and intuitive mechanism to query large knowledge graphs. Answers to tasks such as “list university professors who have designed some programming languages and also won an award in Computer Science” are best found in knowledge graphs that record entities and their relationships. Real-world knowledge graphs are difficult to use due to their sheer size and complexity and the challenging task of writing complex structured graph queries. Toward better usability of query systems over knowledge graphs, GQBE allows users to query knowledge graphs by example entity tuples without writing complex queries. In this demo we present: 1) a detailed description of the various features and user-friendly GUI of GQBE, 2) a brief description of the system architecture, and 3) a demonstration scenario that we intend to show the audience.

Globalization has created new challenges and threats. The article identifies threats and challenges of globalization that have the most devastating effect on cultural and natural heritage. It analyzes efforts of the international community to minimize them. The article studies economic, foreign policy, environmental and cultural threats to the world cultural heritage. Heritage sites can be destroyed as a result of environmental and technological challenges, extremist movements and wars, uncontrolled urbanization, predatory exploitation of nature and cultural objects for commercial purposes, and rapid development of international tourism. All these factors have a negative impact on cultural and natural heritage sites. The problem of protection of natural and cultural heritage is of institutional and documentary nature. The UNESCO international organization deals with World Heritage issues, compiling a list of endangered objects of cultural and natural significance. It combines international efforts to preserve and popularize them because of their special cultural, historical or environmental significance. Damage or extinction of cultural objects is tragic for the world heritage. Due to new dangers, the entire international community must participate in the protection of the natural and cultural heritage sites. Problems that cause irreparable harm to the world heritage are due to human activities. It is necessary to develop measures that could be a response to the problems of cultural heritage protection in the context of globalization. It is necessary to make people respect cultural and natural heritage and inform them about dangers threatening cultural objects.

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 workshop will explore the role of technology support-ing and mediating cultural heritage practices for both pro-febional communities (cultural heritage profebionals, her-itage institutions, etc.) and civic communities (citizen-led heritage initiatives, heritage volunteers, personal and com-munity identified heritage, heritage crowdsourcing, etc.). The workshop-which aims to attract participants from her-itage studies and practice, community engagement, digital humanities and human-centred computing-will discub challenges and future opportunities for technology use and for design and participatory procebes in the context of var-ious heritage communities, and the role of different stake-holders in engaging with heritage in a technologically-mediated way.

The transition of Sketchfab, a widely used platform for hosting and sharing 3D cultural heritage content, to Epic Games’ Fab marketplace has raised concerns within the cultural heritage community about the potential loss of years of work and thousands of 3D models, highlighting the risks of relying on commercial solutions for preservation and dissemination. This shift, together with the unprecedented investments by the European Commission on infrastructures for digitised heritage, present a critical opportunity to restart conversations about the future of 3D scholarship and infrastructures for cultural heritage. Using a mixed-methods approach, this paper analyses data from a literature review, two surveys, a focus group, and community responses to Sketchfab’s announced changes. Our findings reveal critical user requirements, including robust metadata and paradata for transparency, advanced analytical tools for scholarly use, flexible annotation systems, mechanisms for ownership, licensing, and citation, as well as community features for fostering engagement and recognition. This paper proposes models and key features for a new infrastructure and concludes by calling for collaborative efforts among stakeholders to develop a system that will ensure that 3D cultural heritage remains accessible, reusable, and meaningful in an ever-changing technological landscape.

Visual question answering has recently been settled as a fundamental multi-modal reasoning task of artificial intelligence that allows users to get information about visual content by asking questions in natural language. In the cultural heritage domain, this task can contribute to assisting visitors in museums and cultural sites, thus increasing engagement. However, the development of visual question answering models for cultural heritage is prevented by the lack of suitable large-scale datasets. To meet this demand, we built a large-scale heterogeneous and multilingual (Italian and English) dataset for cultural heritage that comprises approximately 500K Italian cultural assets and 6.5M question-answer pairs. We propose a novel formulation of the task that requires reasoning over both the visual content and an associated natural language description, and present baselines for this task. Results show that the current state of the art is reasonably effective but still far from satisfactory; therefore, further research in this area is recommended. Nonetheless, we also present a holistic baseline to address visual and contextual questions and foster future research on the topic.