Qualitative analysis of vocabulary evolution on the linked open data cloud

We analyse the evolution of vocabularies on the Linked Open Data cloud. Based on the recent statistics of the LOD cloud, we have selected the twelve most dominant vocabularies in terms of their use in different pay-level domains. The number of versions we found for these vocabularies range between 2 to 11. While some ontologies exist for more than 10 years (e.g., FOAF) others are only online since a few years (like DCAT). Our analysis shows that many changes occurred on annotation properties. This reflects a need for more clarification of the terms, especially at early versions of the vocabularies. The majority of changes in the vocabularies are due to changes in other, imported vocabularies. Thus, there is a co-evolution of different vocabularies. This insight has practical impacts to ontology engineers. They not only need to consider the evolution of the vocabularies they directly use, but also those they import and indirectly depend on

Vocabulary Evolution on the Semantic Web: From Changes to Evolution of Vocabularies and its Impact on the Data

The main objective of the Semantic Web is to provide data on the web well-defined meaning. Vocabularies are used for modeling data in the web, provide a shared understanding of a domain and consist of a collection of types and properties. These types and properties are so-called terms. A vocabulary can import terms from other vocabularies, and data publishers use vocabulary terms for modeling data. Importing terms via vocabularies results in a Network of Linked vOcabularies (NeLO). Vocabularies are subject to change during their lifetime. When vocabularies change, the published data become a problem if they are not updated based on these changes. So far, there has been no study that analyzes vocabulary changes over time. Furthermore, it is unknown how data publishers reflect on such vocabulary changes. Ontology engineers and data publishers may not be aware of the changes in the vocabulary terms that have already happened since they occur rather rarely. This work addresses the problem of vocabulary changes and their impact on other vocabularies and the published data. We analyzed the changes of vocabularies and their reuse. We selected the most dominant vocabularies, based on their use by data publishers. Additionally, we analyzed the changes of 994 vocabularies. Furthermore, we analyzed various vocabularies to better understand by whom and how they are used in the modeled data, and how these changes are adopted in the Linked Open Data cloud. We computed the state of the NeLO from the available versions of vocabularies for over 17 years. We analyzed the static parameters of the NeLO such as its size, density, average degree, and the most important vocabularies at certain points in time. We further investigated how NeLO changes over time, specifically measuring the impact of a change in one vocabulary on others, how the reuse of terms changes, and the importance of vocabulary changes. Our results show that the vocabularies are highly static, and many of the changes occurred in annotation properties. Additionally, 16% of the existing terms are reused by other vocabularies, and some of the deprecated and deleted terms are still reused. Furthermore, most of the newly coined terms are adopted immediately. Our results show that even if the change frequency of terms is rather low, it can have a high impact on the data due to a large amount of data on the web. Moreover, due to a large number of vocabularies in the NeLO, and therefore the increase of available terms, the percentage of imported terms compared with the available ones has decreased over time. Additionally, based on the scores of the average number of exports for the vocabularies in the NeLO, some vocabularies have become more popular over time. Overall, understanding the evolution of vocabulary terms is important for ontology engineers and data publishers to avoid wrong assumptions about the data published on the web. Furthermore, it may foster a better understanding of the impact of the changes in vocabularies and how they are adopted to possibly learn from previous experience. Our results provide for the first time in-depth insights into the structure and evolution of the NeLO. Supported by proper tools exploiting the analysis of this thesis, it may help ontology engineers to identify data modeling shortcomings and assess the dependencies implied by the reusing of a specific vocabulary.Das Hauptziel des Semantic Web ist es, den Daten im Web eine klar definierte Bedeutung zu geben. Vokabulare werden zum Modellieren von Daten im Web verwendet. Vokabulare vermitteln ein gemeinsames Verständnis einer Domäne und bestehen aus einer Sammlung von Typen und Eigenschaften. Diese Typen und Eigenschaften sind sogenannte Begriffe. Ein Vokabular kann Begriffe aus anderen Vokabularen importieren, und Datenverleger verwenden die Begriffe der Vokabulare zum Modellieren von Daten. Durch das Importieren von Begriffen entsteht ein Netzwerk verknüpfter Vokabulare (NeLO). Vokabulare können sich im Laufe der Zeit ändern. Wenn sich Vokabulare ändern, kann dies zu Problemen mit bereits veröffentlichten Daten führen, falls diese nicht entsprechend angepasst werden. Bisher gibt es keine Studie, die die Veränderung der Vokabulare im Laufe der Zeit analysiert. Darüber hinaus ist nicht bekannt, inwiefern bereits veröffentlichte Daten an diese Veränderungen angepasst werden. Verantwortliche für Ontologien und Daten sind sich möglicherweise der Änderungen in den Vokabularen nicht bewusst, da solche Änderungen eher selten vorkommen. Diese Arbeit befasst sich mit dem Problem der Änderung von Vokabularen und deren Auswirkung auf andere Vokabulare sowie die Daten. Wir analysieren die Änderung von Vokabularen und deren Wiederverwendung. Für unsere Analyse haben wir diejenigen Vokabulare ausgewählt, die am häufigsten verwendet werden. Zusätzlich analysieren wir die Änderungen von 994 Vokabularen aus dem Verzeichnis "Linked Open Vocabulary". Wir analysieren die Vokabulare, um zu verstehen, von wem und wie sie in den modellierten Daten verwendet werden und inwiefern Änderungen in die Linked Open Data Cloud übernommen werden. Wir beobachten den Status von NeLO aus den verfügbaren Versionen der Vokabulare über einen Zeitraum von 17 Jahren. Wir analysieren statische Parameter von NeLO wie Größe, Dichte, Durchschnittsgrad und die wichtigsten Vokabulare zu bestimmten Zeitpunkten. Wir untersuchen weiter, wie sich NeLO mit der Zeit ändert. Insbesondere messen wir die Auswirkung einer Änderung in einem Vokabular auf andere, wie sich die Wiederverwendung von Begriffen ändert und wie wichtig Änderungen im Vokabular sind. Unsere Ergebnisse zeigen, dass die Vokabulare sehr statisch sind und viele Änderungen an sogenannten Annotations-Properties vorgenommen wurden. Darüber hinaus werden 16% der vorhandenen Begriffen von anderen Vokabularen wiederverwendet, und einige der veralteten und gelöschten Begriffe werden weiterhin wiederverwendet. Darüber hinaus werden die meisten neu erstellten Begriffe unmittelbar verwendet. Unsere Ergebnisse zeigen, dass selbst wenn die Häufigkeit von Änderungen an Vokabularen eher gering ist, so kann dies aufgrund der großen Datenmenge im Web erhebliche Auswirkungen haben. Darüber hinaus hat sich aufgrund einer großen Anzahl von Vokabularen in NeLO und damit der Zunahme der verfügbaren Begriffe der Prozentsatz der importierten Begriffe im Vergleich zu den verfügbaren Begriffen im Laufe der Zeit verringert. Basierend auf den Ergebnissen der durchschnittlichen Anzahl von Exporten für die Vokabulare in NeLO sind einige Vokabulare im Laufe der Zeit immer beliebter geworden. Insgesamt ist es für Verantwortliche für Ontologien und Daten wichtig, die Entwicklung der Vokabulare zu verstehen, um falsche Annahmen über die im Web veröffentlichten Daten zu vermeiden. Darüber hinaus ermöglichen unsere Ergebnisse ein besseres Verständnis der Auswirkungen von Änderungen in Vokabularen, sowie deren Nachnutzung, um möglicherweise aus früheren Erfahrungen zu lernen. Unsere Ergebnisse bieten erstmals detaillierte Einblicke in die Struktur und Entwicklung des Netzwerks der verknüpften Vokabularen. Unterstützt von geeigneten Tools für die Analyse in dieser Arbeit, kann es Verantwortlichen für Ontologien helfen, Mängel in der Datenmodellierung zu identifizieren und Abhängigkeiten zu bewerten, die durch die Wiederverwendung eines bestimmten Vokabulars entstehenden

Linking Data Across Universities: An Integrated Video Lectures Dataset

This paper presents our work and experience interlinking educational information across universities through the use of Linked Data principles and technologies. More specifically this paper is focused on selecting, extracting, structuring and interlinking information of video lectures produced by 27 different educational institutions. For this purpose, selected information from several websites and YouTube channels have been scraped and structured according to well-known vocabularies, like FOAF 1, or the W3C Ontology for Media Resources 2. To integrate this information, the extracted videos have been categorized under a common classification space, the taxonomy defined by the Open Directory Project 3. An evaluation of this categorization process has been conducted obtaining a 98% degree of coverage and 89% degree of correctness. As a result of this process a new Linked Data dataset has been released containing more than 14,000 video lectures from 27 different institutions and categorized under a common classification scheme

A bibliometric study of the research area of videogames using Dimensions.ai database

Videogames are a very interesting area of research for fields as diverse as computer science, health, psychology or even social sciences. Every year a growing number of articles are published in different topics inside this field, so it is very convenient to study the different bibliometric data in order to consolidate the research efforts. Thus, the aim of this work is to conduct a study on the distribution of articles related to videogames in the different fields of research, as well as to measure their interest over time, to identify the sources, countries and authors with the highest scientific production. In order to carry out this analysis, the information system Dimensions.ai has been considered, since it covers a large number of documents and allows for easy downloading and analysis of datasets. According to the study, three countries are the most prolific in this area: USA, Canada and UK. The obtained results also indicate that the fields with the highest number of publications are Information and Computer Sciences, Medical and Health Sciences, and Psychology and Cognitive Sciences, in this order. With regard to the impact of the publications, differences between the number of citations, and the number of Altmetric Attention Score, have been found

Linked USDL Agreement: Effectively Sharing Semantic Service Level Agreements on the Web

As the use of services available on the Web is becoming mainstream, contracts and legal aspects of the relationship between providers and consumers need to be formalized. However, current proposals to model service level agreements are mostly focused on technical aspects, do not explicitly provide semantics to agreement terms, and do not follow Web principles. These limitations prevent take-up, automatic processing, and effective sharing of agreements. Linked USDL Agreement is a Linked Data based semantic model to describe and share service agreements that extends Linked USDL, which offers a family of languages to describe various technical and business aspects of services. We followed a use case driven approach, evaluating the applicability of our proposal in a cloud computing scenario, and comparing its expressiveness with existing models. Finally, we show a concrete tool that helps to model and check the validity of agreements.Junta de Andalucía P12-TIC-1867Ministerio de Economía y Competitividad TIN2012-32273Junta de Andalucía P10-TIC-5906Ministerio de Economía y Competitividad IPT- 2013-0890-

Citizen Inquiry: Engaging Citizens in Online Communities of Scientific Inquiries

Citizen Inquiry has been proposed as an informal science learning approach to enable widespread involvement in science and empower citizens with reasoning and problem-solving skills used by scientists. It combines aspects from citizen science and inquiry-based learning, producing science learning experiences within distributed communities of interest. A central challenge for Citizen Inquiry is to involve citizens in planning and implementing their own investigations, supported and guided by online systems and tools within an inquiry environment, while collaborating with science experts and non-experts. This thesis explores how to create an active and sustainable online community for citizens to engage in scientific investigations. To this end, it investigates the design of online communities, recruitment and retaining of members, factors that engage or disengage members from the community, and whether and how members learn throughout their participation. The intervention comprises two iterations of Citizen Inquiry communities: ‘Inquiring Rock Hunters’ and ‘Weather-it’. The communities were accommodated by the nQuire platform and the nQuire-it toolkit, respectively, software designed and structured to support collaborative personally-meaningful inquiry learning. The findings of this research are explained through an analysis that compared the two design studies with previous research on citizen participation projects and online communities. Results highlight the importance of frequent project communication, multiple ways of participation, software usability, and interaction and collaboration between the members, while indicating disengagement factors such as lack of time, interest and confidence. Different categories of learning are identified (activity, on-topic and community), emphasizing the understanding of inquiry activities as part of a complete scientific process and the balance between fun and learning. The thesis concludes with design considerations for the creation of future Citizen Inquiry and other citizen participation communities

A semantic-based platform for the digital analysis of architectural heritage

This essay focuses on the fields of architectural documentation and digital representation. We present a research paper concerning the development of an information system at the scale of architecture, taking into account the relationships that can be established between the representation of buildings (shape, dimension, state of conservation, hypothetical restitution) and heterogeneous information about various fields (such as the technical, the documentary or still the historical one). The proposed approach aims to organize multiple representations (and associated information) around a semantic description model with the goal of defining a system for the multi-field analysis of buildings

Linked USDL: a vocabulary for web-scale service trading

Real-world services ranging from cloud solutions to consulting currently dominate economic activity. Yet, despite the increasing number of service marketplaces online, service trading on the Web remains highly restricted. Services are at best traded within closed silos that offer mainly manual search and comparison capabilities through a Web storefront. Thus, it is seldom possible to automate the customisation, bundling, and trading of services, which would foster a more efficient and effective service sector. In this paper we present Linked USDL, a comprehensive vocabulary for capturing and sharing rich service descriptions, which aims to support the trading of services over the Web in an open, scalable, and highly automated manner. The vocabulary adopts and exploits Linked Data as a means to efficiently support communication over the Web, to promote and simplify its adoption by reusing vocabularies and datasets, and to enable the opportunistic engagement of multiple cross-domain providers

Modeling Service Level Agreements with Linked USDL Agreement

Nowadays, service trading over the Web is gaining momentum. In this highly dynamic scenario, both providers and consumers need to formalize their contractual and legal relationship, creating service level agreements. Although there exist some proposals that provide models to describe that relationship, they usually only cover technical aspects, not providing explicit semantics to the agreement terms. Furthermore, these models cannot be effectively shared on the Web, since they do not actually follow Web principles. These drawbacks hamper take-up and automatic analysis. In this article, we introduce Linked USDL Agreement, a semantic model to specify, manage and share service level agreement descriptions on the Web. This model is part of the Linked USDL family of ontologies that can describe not only technical but also business related aspects of services, incorporating Web principles. We validate our proposal by describing agreements in computational and non-computational scenarios, namely cloud computing and business process outsourcing services. Moreover, we evaluate the actual coverage and expressiveness of Linked USDL Agreement comparing it with existing models. In order to foster its adoption and effectively manage the service level agreement lifecycle, we present an implemented tool that supports creation, automatic analysis, and publication on the Web of agreement descriptions.Junta de Andalucía P12-TIC-1867Ministerio de Economía y Competitividad TIN2012-32273Junta de Andalucía TIC-5906Ministerio de Economía y Competitividad TIN2015-70560-RComisión Europea FP7-ICT 31786