Software Citation Implementation Challenges

The main output of the FORCE11 Software Citation working group (https://www.force11.org/group/software-citation-working-group) was a paper on software citation principles (https://doi.org/10.7717/peerj-cs.86) published in September 2016. This paper laid out a set of six high-level principles for software citation (importance, credit and attribution, unique identification, persistence, accessibility, and specificity) and discussed how they could be used to implement software citation in the scholarly community. In a series of talks and other activities, we have promoted software citation using these increasingly accepted principles. At the time the initial paper was published, we also provided guidance and examples on how to make software citable, though we now realize there are unresolved problems with that guidance. The purpose of this document is to provide an explanation of current issues impacting scholarly attribution of research software, organize updated implementation guidance, and identify where best practices and solutions are still needed

Volume 39, Number 3, September 2019 OLAC Newsletter

Digitized September 2019 issue of the OLAC Newsletter

Volume 26, Number 3, September 2006 OLAC Newsletter

Digitized September 2006 issue of the OLAC Newsletter

The Model of Reading: Modelling principles, Definitions, Schema, Alignments

This technical report introduces the data model developed to address the systematic collection and use of reading experiences in READ-IT project. The model of reading presented in this document is meant to inform the development of the READ-IT database and tools. This document describes the methodological approach and design principles adopted in the development of the model of reading. Furthermore, this technical report describes the content of the first version of the data model of the reading experience, including a preliminary analysis of the alignments between READ-IT model of reading with CIDOC-CRM, FRBRoo, FoaF and Schema.org

A Quantitative Analysis of the Use of Microdata for Semantic Annotations on Educational Resources

A current trend in the semantic web is the use of embedded markup formats aimed to semantically enrich web content by making it more understandable to search engines and other applications. The deployment of Microdata as a markup format has increased thanks to the widespread of a controlled vocabulary provided by Schema.org. Recently, a set of properties from the Learning Resource Metadata Initiative (LRMI) specification, which describes educational resources, was adopted by Schema.org. These properties, in addition to those related to accessibility and the license of resources included in Schema.org, would enable search engines to provide more relevant results in searching for educational resources for all users, including users with disabilities. In order to obtain a reliable evaluation of the use of Microdata properties related to the LRMI specification, accessibility, and the license of resources, this research conducted a quantitative analysis of the deployment of these properties in large-scale web corpora covering two consecutive years. The corpora contain hundreds of millions of web pages. The results further our understanding of this deployment in addition to highlighting the pending issues and challenges concerning the use of such properties

The Model of Reading : Modelling principles, Definitions, Schema, Alignments

READ-IT Model of Reading -V2Executive Summary This technical report introduces the data model developed to address the systematic collection and use of reading experiences in READ-IT project. The model of reading presented in this document is meant to inform the development of the READ-IT database and tools. This document describes the methodological approach and design principles adopted in the development of the model of reading. Furthermore, this technical report describes the content of the first version of the data model of the reading experience, including a preliminary analysis of the alignments between READ-IT model of reading with CIDOC-CRM, FRBRoo, FoaF and Schema.org

Enacting the Semantic Web: Ontological Orderings, Negotiated Standards, and Human-machine Translations

Artificial intelligence (AI) that is based upon semantic search has become one of the dominant means for accessing information in recent years. This is particularly the case in mobile contexts, as search based AI are embedded in each of the major mobile operating systems. The implications are such that information is becoming less a matter of choosing between different sets of results, and more of a presentation of a single answer, limiting both the availability of, and exposure to, alternate sources of information. Thus, it is essential to understand how that information comes to be structured and how deterministic systems like search based AI come to understand the indeterminate worlds they are tasked with interrogating. The semantic web, one of the technologies underpinning these systems, creates machine-readable data from the existing web of text and formalizes those machine-readable understandings in ontologies. This study investigates the ways that those semantic assemblages structure, and thus define, the world. In accordance with assemblage theory, it is necessary to study the interactions between the components that make up such data assemblages. As yet, the social sciences have been slow to systematically investigate data assemblages, the semantic web, and the components of these important socio-technical systems. This study investigates one major ontology, Schema.org. It uses netnographic methods to study the construction and use of Schema.org to determine how ontological states are declared and how human-machine translations occur in those development and use processes. This study has two main findings that bear on the relevant literature. First, I find that development and use of the ontology is a product of negotiations with technical standards such that ontologists and users must work around, with, and through the affordances and constraints of standards. Second, these groups adopt a pragmatic and generalizable approach to data modeling and semantic markup that determines ontological context in local and global ways. This first finding is significant in that past work has largely focused on how people work around standards’ limitations, whereas this shows that practitioners also strategically engage with standards to achieve their aims. Second, the particular approach that these groups use in translating human knowledge to machines, differs from the formalized and positivistic approaches described in past work. At a larger level, this study fills a lacuna in the collective understanding of how data assemblages are constructed and operate

Volume 24, Number 3, September 2004 OLAC Newsletter

Digitized September 2004 issue of the OLAC Newsletter

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