Semantic Web Collaborative Spaces

Linked Data initiatives have fostered the publication of more than one thousand of datasets in the Linking Open Data (LOD) cloud from a large variety of domains, e.g., Life Sciences, Media, and Government. Albeit large in volume, Linked Data is essentially read-only and most collaborative tasks of cleaning, enriching, and reasoning are not dynamically available. Collaboration between data producers and consumers is essential for overcoming these limitations, and for fostering the evolution of the LOD cloud into a more participative and collaborative data space. In this paper, we describe the role that collaborative infrastructures can play in creating and maintaining Linked Data, and the benefits of exploiting knowledge represented in ontologies as well as the main features of Semantic Web technologies to effectively assess the LOD cloud’s evolution. First, the advantages of using ontologies for modelling collaborative spaces are discussed, as well as formalisms for assessing semantic collaboration by sharing annotations from terms in domain ontologies. Then, Semantic MediaWiki communities are described, and illustrated with three applications in the domains of formal mathematics, ontology engineering, and pedagogical content management. Next, the problem of exploiting semantics in collaborative spaces is tackled, and three different approaches are described. Finally, we conclude with an outlook to future directions and problems that remain open in the area of semantically-driven collaborative spaces.

The scenario defined by current Web architectures and paradigms poses challenges and opportunities to users, in particular as far as collaborative resource management is concerned. A support to face such challenges is represented by semantic annotations. However, especially in collaborative environments, disagreements can easily rise, leading to incoherent, poor and ultimately useless annotations. The possibility of keeping track of “private annotations” on shared resources represents a significative improvement for collaborative environments. In this paper, we present a model for managing “personal views” over shared resources on the Web, formally defined as structured sets of semantic annotations, enabling users to apply their individual point of view over a common perspective provided in shared workspaces. This model represents an original contribution and a significative extension with respect to our previous work, even being part of a larger project, SemT++, aimed at developing an environment supporting users in collaborative resource management on the Web.

In the recent years, there have been significant advances in formalization of mathematics, involving a number of large-scale formalization projects. This naturally poses a number of interesting problems concerning how should humans and machines collaborate on such deeply semantic and computer-assisted projects. In this paper we provide an overview of the wikis and web-based systems for such collaboration involving humans and also AI systems over the large corpora of fully formal mathematical knowledge.

This work demonstrates Fresnel Forms by creating a wiki that replicates Wikipedia infobox displays and directly exports the triples that DBpedia indirectly derives from them. Forms assist valid user entry of this data with features such as autocompletion. This unifies infobox displays with DBpedia triples while adding assistive data entry in a unified collaborative space.

In this work, we present an approach that allows educational resources to be collaboratively authored and annotated with well-defined pedagogical semantics using Semantic MediaWiki as collaborative knowledge engineering tool. The approach allows for the exposition of pedagogically annotated learning content as Linked Open Data to enable its reuse across e-learning platforms and its adaptability in different educational contexts. We employ Web Didactics as knowledge organization concept and detail its manifestation in a Semantic MediaWiki system using import and mapping declarations. We also show how the inherent pedagogical semantics of Web Didactics can be retained when learning material is exported as RDF data. The advantage of the presented approach lies in addressing the constructivist view on educational models: The different roles involved in the content development process are not forced to adapt to new vocabularies but can continue using the terms and classification systems they are familiar with. Results of the usability test with computer scientists and education researchers are positive with significantly more positive results for computer scientists.

In the enterprise context, people need to exploit, interpret and mainly visualize different types of interactions between heterogeneous objects. Graph model is an appropriate way to represent those interactions. Nodes represent the individuals or objects and edges represent the relationships between them. However, extracted graphs are in general heterogeneous and large sized which makes it difficult to visualize and to analyze easily. An adaptive aggregation operation is needed to have more understandable graphs in order to allow users discovering underlying information and hidden relationships between objects. Existing graph summarization approaches such as k-SNAP are carried out in homogeneous graphs where nodes are described by the same list of attributes that represent only one community. The aim of this work is to propose a general tool for graph aggregation which addresses both homogeneous and heterogeneous graphs. To do that, we develop a new soft and adaptive approach to aggregate heterogeneous graphs (i.e., composed of different node attributes and different relationship types) using the definition of Rough Set Theory (RST) combined with Formal Concept Analysis (FCA), the well known K-Medoids and the hierarchical clustering methods. Aggregated graphs are produced according to user-selected node attributes and relationships. To evaluate the quality of the obtained summaries, we propose two quality measures that evaluate respectively the similarity and the separability in groups based on the notion of common neighbor nodes. Experimental results demonstrate that our approach is effective for its ability to produce a high quality solution with relevant interpretations.

In the past 10-15 years, a large amount of resources have been devoted to develop highly sophisticated and effective tools for automated and semi-automated schema-vocabulary-ontology matching and alignment. However, very little effort has been made to consolidate the outputs, in particular to share the resulting mappings with the community of researchers and practitioners, support a community-driven revision/evaluation of mappings and make them reusable. Yet, mappings are an extremely valuable asset, as they provide an integration map for the web of data and the “glue” for the Global Giant Graph envisaged by Tim Berners-Lee. Aiming at kicking-off a positive endeavor, we have developed Synapsis, a platform to support a community-driven lifecycle of contextual mappings across ontologies, vocabularies and schemas. Okkam Synapsis offers utilities to load, create, maintain, annotate, subscribe, and define levels of agreement over user-defined contextual mappings. Furthermore, in order to ease the development of Semantic (Web) applications, Synapsis supports the creation of sets of mappings associated with an application placeholder. On the one hand, this allows developers to easily create and manipulate all the mappings required for their own application without affecting other users. On the other hand, a measure of Sharedness for the mappings defined across application contexts is proposed to enable the implementation of ranking metrics that can be used to order the mappings managed through Synapsis. Aiming at supporting a growing number of users, Synapsis was positively tested to be scalable in the order of millions of mappings, performing experiments with synthetic data. Applying the Data-as-a-Service (DaaS) paradigm, the sets of mappings created and managed by Synapsis are also available through REST services, to further facilitate integration into applications working with heterogeneous data.