Within ontology engineering concepts are modeled as classes and relationships, and restrictions as axioms. Reusing ontologies requires assessing if existing ontologies are suited for an application scenario. Different scenarios not only influence concept modeling, but also the use of different restriction types, such as subclass relationships or disjointness between concepts. However, metadata about the use of such restriction types is currently unavailable, preventing accurate assessments for reuse. We created the RDF Data Cube-based dataset MontoloStats, which contains restriction use statistics for 660 LOV and 565 BioPortal ontologies. We analyze the dataset and discuss the findings and their implications for ontology reuse. The MontoloStats dataset reveals that 94% of LOV and 95% of BioPortal ontologies use RDFS-based restriction types, 49% of LOV and 52% of BioPortal ontologies use at least one OWL-based restriction type, and different literal value-related restriction types are not or barely used. Our dataset provides modeling insights, beneficial for ontology reuse to discover and compare reuse candidates, but can also be the basis of new research that investigates novel ontology engineering methodologies with respect to restrictions definition.

Abstract While the Web was designed as a decentralised environment, individual authors still lack the ability to conveniently author and publish documents, and to engage in social interactions with documents of others in a truly decentralised fashion. We present dokieli , a fully decentralised, browser-based authoring and annotation platform with built-in support for social interactions, through which people retain ownership of and sovereignty over their data. The resulting “living” documents are interoperable and independent of dokieli since they follow standards and best practices, such as HTML+RDFa for a fine-grained semantic structure, Linked Data Platform for personal data storage, and Linked Data Notifications for updates. This article describes dokieli’s architecture and implementation, demonstrating advanced document authoring and interaction without a single point of control. Such an environment provides the right technological conditions for independent publication of scientific articles, news, and other works that benefit from diverse voices and open interactions. To experience the described features please open this document in your Web browser under its canonical URI: http://csarven.ca/dokieli-rww .

While humans browse the Web by following links, these hypermedia links can also be used by machines for browsing. While efforts such as Hydra semantically describe the hypermedia controls on Web interfaces to enable smarter interface-agnostic clients, they are largely limited to the input parameters to interfaces, and clients therefore do not know what response to expect from these interfaces. In order to convey such expectations, interfaces need to declaratively describe the response structure of their parameterized hypermedia controls. We therefore explored techniques to represent this parameterized response structure in a generic but expressive way. In this work, we discuss four different approaches for declaring a response structure, and we compare them based on a model that we introduce. Based on this model, we conclude that a SHACL shape-based approach can be used for declaring such a parameterized response structure, as it conforms to the REST architectural style that has helped shape the Web into its current form.

When researchers formulate search queries to find relevant content on the Web, those queries typically consist of keywords that can only be matched in the content or its metadata. The Web of Data extends this functionality by bringing structure and giving well-defined meaning to the content and it enables humans and machines to work together using controlled vocabularies. Due the high degree of mismatches between the structure of the content and the vocabularies in different sources, searching over multiple heterogeneous repositories of structured data is considered challenging. Therefore, the authors present a semantic search engine for researchers facilitating search in research related Linked Data. To facilitate high-precision interactive search, they annotated and interlinked structured research data with ontologies from various repositories in an effective semantic model. Furthermore, the authors' system is adaptive as researchers can synchronize using new social media accounts and efficiently explore new datasets.

Topic Modelling (TM) has gained momentum over the last few years within the humanities to analyze topics represented in large volumes of full text. This paper proposes an experiment with the usage of TM based on a large subset of digitized archival holdings of the European Commission (EC). Currently, millions of scanned and OCRed files are available and hold the potential to significantly change the way historians of the construction and evolution of the European Union can perform their research. However, due to a lack of resources, only minimal metadata are available on a file and document level, seriously undermining the accessibility of this archival collection. The article explores in an empirical manner the possibilities and limits of TM to automatically extract key concepts from a large body of documents spanning multiple decades. By mapping the topics to headings of the EUROVOC thesaurus, the proof of concept described in this paper offers the future possibility to represent the identified topics with the help of a hierarchical search interface for end-users.

Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed—even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness.

In order to assess the trustworthiness of information on social media, a consumer needs to understand where this information comes from, and which processes were involved in its creation. The entities, agents and activities involved in the creation of a piece of information are referred to as its provenance, which was standardized by W3C PROV. However, current social media APIs cannot always capture the full lineage of every message, leaving the consumer with incomplete or missing provenance, which is crucial for judging the trust it carries. Therefore in this paper, we propose an approach to reconstruct the provenance of messages on social media on multiple levels. To obtain a fine-grained level of provenance, we use an approach from prior work to reconstruct information cascades with high certainty, and map them to PROV using the PROV-SAID extension for social media. To obtain a coarse-grained level of provenance, we adapt our similarity-based, fuzzy provenance reconstruction approach -- previously applied on news. We illustrate the power of the combination by providing the reconstructed provenance of a limited social media dataset gathered during the 2012 Olympics, for which we were able to reconstruct a significant amount of previously unidentified connections.

The concept of Linked Data has made its entrance in the cultural heritage sector due to its potential use for the integration of heterogeneous collections and deriving additional value out of existing metadata. However, practitioners and researchers alike need a better understanding of what outcome they can reasonably expect of the reconciliation process between their local metadata and established controlled vocabularies which are already a part of the Linked Data cloud. This paper offers an in‐depth analysis of how a locally developed vocabulary can be successfully reconciled with the Library of Congress Subject Headings (LCSH) and the Arts and Architecture Thesaurus (AAT) through the help of a general‐purpose tool for interactive data transformation (OpenRefine). Issues negatively affecting the reconciliation process are identified and solutions are proposed in order to derive maximum value from existing metadata and controlled vocabularies in an automated manner.