Ontology Modeling 2.0: Next Steps

Semantic Web as a field of research and applications is concerned with methods and tools for data sharing, discovery, integration, and reuse, both on and off the World Wide Web. In the form of knowledge graphs and their underlying schemas, Semantic Web technologies are currently entering industrial mainstream. At the same time, the ever increasing prevalence of publicly available structured data on the Semantic Web enables new applications in a variety of domains, and as part of this presentation, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept

Community detection applied on big linked data

The Linked Open Data (LOD) Cloud has more than tripled its sources in just six years (from 295 sources in 2011 to 1163 datasets in 2017). The actual Web of Data contains more then 150 Billions of triples. We are assisting at a staggering growth in the production and consumption of LOD and the generation of increasingly large datasets. In this scenario, providing researchers, domain experts, but also businessmen and citizens with visual representations and intuitive interactions can significantly aid the exploration and understanding of the domains and knowledge represented by Linked Data. Various tools and web applications have been developed to enable the navigation, and browsing of the Web of Data. However, these tools lack in producing high level representations for large datasets, and in supporting users in the exploration and querying of these big sources. Following this trend, we devised a new method and a tool called H-BOLD (High level visualizations on Big Open Linked Data). H-BOLD enables the exploratory search and multilevel analysis of Linked Open Data. It offers different levels of abstraction on Big Linked Data. Through the user interaction and the dynamic adaptation of the graph representing the dataset, it will be possible to perform an effective exploration of the dataset, starting from a set of few classes and adding new ones. Performance and portability of H-BOLD have been evaluated on the SPARQL endpoint listed on SPARQL ENDPOINT STATUS. The effectiveness of H-BOLD as a visualization tool is described through a user study

Co-evolution of RDF Datasets

Linking Data initiatives have fostered the publication of large number of RDF datasets in the Linked Open Data (LOD) cloud, as well as the development of query processing infrastructures to access these data in a federated fashion. However, different experimental studies have shown that availability of LOD datasets cannot be always ensured, being RDF data replication required for envisioning reliable federated query frameworks. Albeit enhancing data availability, RDF data replication requires synchronization and conflict resolution when replicas and source datasets are allowed to change data over time, i.e., co-evolution management needs to be provided to ensure consistency. In this paper, we tackle the problem of RDF data co-evolution and devise an approach for conflict resolution during co-evolution of RDF datasets. Our proposed approach is property-oriented and allows for exploiting semantics about RDF properties during co-evolution management. The quality of our approach is empirically evaluated in different scenarios on the DBpedia-live dataset. Experimental results suggest that proposed proposed techniques have a positive impact on the quality of data in source datasets and replicas.Comment: 18 pages, 4 figures, Accepted in ICWE, 201

An Integrated Smart City Platform

Smart Cities aim to create a higher quality of life for their citizens, improve business services and promote tourism experience. Fostering smart city innovation at local and regional level requires a set of mature technologies to discover, integrate and harmonize multiple data sources and the exposure of eective applications for end-users (citizens, administrators, tourists...). In this context, Semantic Web technologies and Linked Open Data principles provide a means for sharing knowledge about cities as physical, economical, social, and technical systems, enabling the development of smart city services. Despite the tremendous effort these communities have done so far, there exists a lack of comprehensive and effective platforms that handle the entire process of identication, ingestion, consumption and publication of data for Smart Cities. In this paper, a complete open-source platform to boost the integration, semantic enrichment, publication and exploitation of public data to foster smart cities in local and national administrations is proposed. Starting from mature software solutions, we propose a platform to facilitate the harmonization of datasets (open and private, static and dynamic on real time) of the same domain generated by dierent authorities. The platform provides a unied dataset oriented to smart cities that can be exploited to offer services to the citizens in a uniform way, to easily release open data, and to monitor services status of the city in real time by means of a suite of web applications

Named Entity Resolution in Personal Knowledge Graphs

Entity Resolution (ER) is the problem of determining when two entities refer to the same underlying entity. The problem has been studied for over 50 years, and most recently, has taken on new importance in an era of large, heterogeneous 'knowledge graphs' published on the Web and used widely in domains as wide ranging as social media, e-commerce and search. This chapter will discuss the specific problem of named ER in the context of personal knowledge graphs (PKGs). We begin with a formal definition of the problem, and the components necessary for doing high-quality and efficient ER. We also discuss some challenges that are expected to arise for Web-scale data. Next, we provide a brief literature review, with a special focus on how existing techniques can potentially apply to PKGs. We conclude the chapter by covering some applications, as well as promising directions for future research.Comment: To appear as a book chapter by the same name in an upcoming (Oct. 2023) book `Personal Knowledge Graphs (PKGs): Methodology, tools and applications' edited by Tiwari et a

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications