Due to importance of data FAIRness (Findable, Accessible, Interoperable, Reusable), ontologies as a means to make data FAIR have attracted more and more attention in different communities and are being used in semantically-enabled applications. However, to obtain good results while using ontologies in these applications, high quality ontologies are needed of which completeness is one of the important aspects. An ontology lacking information can lead to missing results. In this paper we present a tool, Phrase2Onto, that supports users in extending ontologies to make the ontologies more complete. It is particularly suited for ontology extension using a phrase-based topic model approach, but the tool can support any extension approach where a user needs to make decisions regarding the appropriateness of using phrases to define new concepts. We describe the functionality of the tool and a user study using Pizza Ontology. The user study showed  a good usability of the system and high task completion. Further, we report on a real application where we extend the Materials Design Ontology.

Success in the life sciences depends on access to information in knowlegde bases and literature. Finding and extracting the relevant information depends on a user’s domain knowledge and the knowledge of the search technology. In this paper we present a system that helps users formulate queries and search the scientific literature. The system coordinates ontologies, knowledge representation, text mining and NLP techniques to generate relevant queries in response to keyword input from the user. Queries are presented in natural language, translated to formal query syntax and issued to a knowledge base of scientific literature, documents or aligned document segments. We describe the components of the system and exemplify using real-world examples.

Circular Economy (CE) aims to reduce value loss and avoid waste by extending the life of products,components, and materials. Circular value networks (CVN), i.e. networks of actors realising partsof the CE, are often complex and involving a multitude of actors, such as suppliers, manufacturers,recyclers, and end-users, from different industry sectors. In addition, the networks enable and managevarious flows of resources, energy, information and value. To set up and operate such networks, datasharing is essential, however, one of the main challenges is semantic interoperability, and as a resultdata are difficult to understand, integrate, and use. Ontologies support semantic interoperability, andcan represent domain knowledge and enable stakeholders to communicate. However, the knowledgedomains involved are many, including sustainability, materials, products, manufacturing, and logistics,where well-established ontologies already exist. In addition, these domains need to be connected torelevant industry sectors. In order to bridge these domains we propose a set of core ontology modules,allowing to express links between existing ontologies as well as filling gaps related to core CE concepts.

Circular economy aims at reducing value loss and avoiding waste, by circulating material or productparts before they become waste. Today, lack of support for sharing data in a secure, quality assured, andautomated way is one of the main obstacles that industry actors point to when attempting to create newcircular value networks. Together with using different terminologies and not having explicit definitions ofthe concepts that appear in data, this makes it very difficult to create new ecosystems of actors in Europetoday. A solution to these challenges needs to leverage open standards for semantic data interoperabilityin establishing a shared vocabulary (ontology network) for data documentation, as well as create adecentralized digital platform that enables collaboration in a secure and confidentiality-preservingmanner. This vocabulary can then be used to construct digital twins of circular value networks to furtherenable open collaboration. Once defined, the blueprints of these digital twins will be reusable as templatesand can be reused with a different set of actors, or used within a different industry domain. This visionincludes a number of open research problems, including the development of ontologies that need to modela wide range of different materials and products, not only providing vertical interoperability but alsohorizontal interoperability, for cross-industry value networks. As well as transdisciplinary research onmethods to find, analyse and assess new circular value chain configurations, and form their decentralizeddigital twins. The solutions will allow for automation of planning, management, and execution of circularvalue networks, at a European scale, and beyond. Thereby supporting the acceleration of the digitaland green transitions, automating the discovery and formation of new collaborations in the circulareconomy.

Ontologies are an important technology for the Semantic Web and many ontologies have already been developed. Many ontologies also contain overlapping information and to be able to use them together effectively, we need to align them. Some of the current alignment techniques use information about the structure of the ontologies, but they have not produced good results in evaluations. We propose an approach where, in contrast to the other approaches, structural information is used as a filtering method in the alignment process. We evaluate the approach in terms of quality and performance.

One of the current challenges in ontology alignment is scalability and one technique to deal with this issue is to reduce the search space for the generation of mapping suggestions. In this paper we develop a method to prune that search space by using clustering techniques and topic identification. Further, we provide experiments showing that we are able to generate partitions that allow for high quality alignments with a highly reduced effort for computation and validation of mapping suggestions for the parts of the ontologies in the partition. Other techniques will still be needed for finding mappings that are not in the partition.

Traffic accidents account for more than half a million casualties every year. The analysis of accidents helps identifying the elements that affect traffic conditions, the relationships between them, and how they may contribute to the occurrence of dangerous situations. In this paper we present a temporal reasoning system for modeling and analyzing various types of accident scenarios. The system is based on Event Calculus and was implemented using Prolog. We exemplify the use of the system by applying it for modeling and analyzing a rear-end accident scenario.

This book presents thoroughly arranged tutorial papers corresponding to lectures given by leading researchers at the Second International Summer School on Reasoning Web in Lisbon, Portugal, in September 2006. Building on the predessor school held in 2005 and published as LNCS 3564, the ten tutorial lectures presented provide competent coverage of current topics in semantic Web research and development.

Ontology matching consists of finding correspondences between se-mantically related entities of two ontologies. OAEI campaigns aim at comparingontology matching systems on precisely defined test cases. These test cases canuse ontologies of different nature (from simple thesauri to expressive OWL on-tologies) and use different modalities, e.g., blind evaluation, open evaluation andconsensus. OAEI 2014 offered 7 tracks with 9 test cases followed by 14 partici-pants. Since 2010, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentationof the OAEI 2014 campaign.

Ontology design patterns (ODPs) and related ontology development methodologies were designed as ways of sharing and reusing best practices in ontology engineering. However, while the use of these reduces the number of issues in the resulting ontologies defects can still be introduced into the ontology due to improper use or misinterpretation of the patterns. Thus, the quality of the developed ontologies is still a major concern. In this paper we address this issue by describing how ontology debugging and matching can be integrated in a state-of-the-art ontology development methodology based on ontology design patterns- the eXtreme Design methodology, and show the advantages in a case study based on a real world ontology.

Ontologies in the biomedical domain are becoming a key element for data integration and search. The usefulness of the applications which use ontologies is often directly influenced by the quality of ontologies, as incorrect or incomplete ontologies might lead to wrong or incomplete results for the applications. Therefore, there is an increasing need for repairing defects in ontologies. In this paper we focus on completing ontologies. We provide an algorithm for completing the is-a structure in TeX ontologies which covers many biomedical ontologies. Further, we present an implemented system based on the algorithm as well as an evaluation using three biomedical ontologies.

Current key management schemes for multicast provide either no resistance to collusion or perfect resistance to collusion. However, resistance to collusion is achieved at the expense of efficiency in terms of the number of transmissions and the number of keys that are used. We argue that applications may have certain assumptions regarding the users and their access to the multicast channel that may be used to provide a broader range of choices for balancing efficiency against resistance to collusion.

We formalize the collusion requirement based upon the users' access to the multicast channel. Different user categorizations give different degrees of collusion resistance and we show that the existing work has focused on special cases of user categorizations. Further, we go on to propose and evaluate a flexible key management strategy for the general case where the accessibility relation defines the order of exclusion of the categories. The theoretical and experimental results show that our scheme has good performance regarding transmissions and keys per controller.

Security and efficiency of rekeying are crucial requirements for multicast key management. However, the two requirements pull in different directions and balancing them to meet the application needs is still an open issue. In this paper we introduce a hybrid key tree scheme to balance security, namely the resistance to collusion, and the efficiency. The resistance to collusion is measured by an integer parameter. The communication and the storage requirements for the controller depend on this parameter too, and they decrease as the resistance to collusion is relaxed. We analytically evaluate the efficiency of our scheme and compare with the previous work. The results show that our scheme allows a fine-tuning of security requirements versus efficiency requirements at run-time, which is not possible with the previous key management schemes.

In multicast group communication, efficiency- and security are competing requirements and balancing them is an acknowledged challenge. In particular, the collusion resistance has an impact on the efficiency of any scheme. In this context, the category-based group key management (category-based GKM) scheme balances the collusion resistance constraints against the communication cost and the group controller storage. However, this scheme increases the storage requirements for users. In this paper we address this problem by introducing a novel technique based on spanning hash key tree (SKT). In the worst case, using our t echnique, the storage requirement remains the same as in the original category-based GKM scheme. However, the experimentalresults show that, in general, the SKT technique greatly reduces the key storage for the users as well as for the controller.

Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

There are very few published studies regarding the performance of persistence mechanisms for systems that use the openEHR multi level modelling approach. This paper addresses the performance and size of XML databases that store openEHR compliant documents. Database size and response times to epidemiological queries are described. An anonymized relational epidemiology database and associated epidemiological queries were used to generate openEHR XML documents that were stored and queried in four opensource XML databases. The XML databases were considerably slower and required much more space than the relational database. For population-wide epidemiological queries the response times scaled in order of magnitude at the same rate as the number of records (total database size) but were orders of magnitude slower than the original relational database. For individual focused clinical queries where patient ID was specified the response times were acceptable. This study suggests that the tested XML database configurations without further optimizations are not suitable as persistence mechanisms for openEHR-based systems in production if population-wide ad hoc querying is needed.

This study provides an experimental performance evaluation on population-based queries of NoSQL databases storing archetype-based Electronic Health Record (EHR) data. There are few published studies regarding the performance of persistence mechanisms for systems that use multilevel modelling approaches, especially when the focus is on population-based queries. A healthcare dataset with 4.2 million records stored in a relational database (MySQL) was used to generate XML and JSON documents based on the openEHR reference model. Six datasets with different sizes were created from these documents and imported into three single machine XML databases (BaseX, eXistdb and Berkeley DB XML) and into a distributed NoSQL database system based on the MapReduce approach, Couchbase, deployed in different cluster configurations of 1, 2, 4, 8 and 12 machines. Population-based queries were submitted to those databases and to the original relational database. Database size and query response times are presented. The XML databases were considerably slower and required much more space than Couchbase. Overall, Couchbase had better response times than MySQL, especially for larger datasets. However, Couchbase requires indexing for each differently formulated query and the indexing time increases with the size of the datasets. The performances of the clusters with 2, 4, 8 and 12 nodes were not better than the single node cluster in relation to the query response time, but the indexing time was reduced proportionally to the number of nodes. The tested XML databases had acceptable performance for openEHR-based data in some querying use cases and small datasets, but were generally much slower than Couchbase. Couchbase also outperformed the response times of the relational database, but required more disk space and had a much longer indexing time. Systems like Couchbase are thus interesting research targets for scalable storage and querying of archetype-based EHR data when population-based use cases are of interest.

Traditionally, theoretical foundations in data structures and algorithms (DSA) courses have been covered through lectures followed by tutorials, where students practise their understanding on pen-and-paper tasks. In this paper, we present findings from a pilot study on using the interactive e-book OpenDSA as the main material in a DSA course. The goal was to redesign an already existing course by building on active learning and continuous examination through the use of OpenDSA. In addition to presenting the study setting, we describe findings from four data sources: final exam, OpenDSA log data, pre and post questionnaires as well as an observation study. The results indicate that students performed better on the exam than during previous years. Students preferred OpenDSA over traditional textbooks and worked actively with the material, although a large proportion of them put off the work until the due date approaches.

Traditionally, theoretical foundations in data structuresand algorithms (DSA) courses have been covered throughlectures followed by tutorials, where students practise theirunderstanding on pen-and-paper tasks. In this paper, we presentfindings from a pilot study on using the interactive e-bookOpenDSA as the main material in a DSA course. The goal was toredesign an already existing course by building on active learningand continuous examination through the use of OpenDSA. Inaddition to presenting the study setting, we describe findings fromfour data sources: final exam, OpenDSA log data, pre- and postcourse questionnaires as well as an observation study. The resultsindicate that students performed better on the exam than duringprevious years. Students preferred OpenDSA over traditionaltextbooks and worked actively with the material, although alarge proportion of them put off the work until the due dateapproaches.

Predicting game or season outcomes is important for clubs as well as for the betting industry. Understanding the critical factors of winning games and championships gives clubs a competitive advantage when selecting players for the team and implementing winning strategies. In this paper, we work with NBA data from 10 seasons and propose an approach for predicting game outcomes that is then used for predicting which team will be champion and which stages a team will reach in the playoffs. We show that our approach has a similar performance as the odds from betting companies and does better than ELO.

Ontology alignment is an area of active research where many algorithms and approaches are being developed. Their performance is usually evaluated by comparing the produced alignments to a reference alignment in terms of precision, recall and F-measure. These measures, however, only provide an overall assessment of the quality of the alignments, but do not reveal differences and commonalities between alignments at a finer-grained level such as, e.g., regions or individual mappings. Furthermore, reference alignments are often unavailable, which makes the comparative exploration of alignments at different levels of granularity even more important. Making such comparisons efficient calls for a “human-in-the-loop” approach, best supported through interactive visual representations of alignments. Our approach extends a recent tool, Matrix Cubes, used for visualizing dense dynamic networks. We first identify use cases for ontology alignment evaluation that can benefit from interactive visualization, and then detail how our Alignment Cubes support interactive exploration of multiple ontology alignments. We demonstrate the usefulness of Alignment Cubes by describing visual exploration scenarios, showing how Alignment Cubes support common tasks identified in the use cases.

With the increased use of ontologies in semantically-enabled applications,the issues of debugging and aligning ontologies have become increasinglyimportant. The quality of the results of such applications is directly dependent onthe quality of the ontologies and mappings between the ontologies they employ. Akey step towards achieving high quality ontologies and mappings is discoveringand resolving modeling defects, e.g., wrong or missing relations and mappings.In this demonstration paper we present a system for aligning taxonomies, the mostused kind of ontologies, and debugging taxonomies and their alignments, whereontology alignment is treated as a special kind of debugging.

Neither developing ontologies nor aligning ontologies are easy tasks, and often the resulting ontologies and alignments are not consistent or complete. Such ontologies and alignments, although often useful, also lead to problems when used in semantically-enabled applications. In this paper we briefly introduce a system that supports domain experts in detecting and repairing wrong and missing is-a relations and mappings.

With the increased use of ontologies in semantically-enabled applications, the issues of debugging and aligning ontologies have become increasingly important. The quality of the results of such applications is directly dependent on the quality of the ontologies and mappings between the ontologies they employ. A key step towards achieving high quality ontologies and mappings is discovering and resolving modeling defects, e.g., wrong or missing relations and mappings. In this paper we present a unified framework for aligning taxonomies, the most used kind of ontologies, and debugging taxonomies and their alignments, where ontology alignment is treated as a special kind of debugging. Our framework supports the detection and repairing of missing and wrong is-a structure in taxonomies, as well as the detection and repairing of missing (alignment) and wrong mappings between ontologies. Further, we implemented a system based on this framework and demonstrate its benefits through experiments with ontologies from the Ontology Alignment Evaluation Initiative.

A picture is worth a thousand words, we often say, yet many areas are in demand of sophisticated visualization techniques, and the Semantic Web is not an exception. The size and complexity of ontologies and Linked Data in the Semantic Web constantly grow and the diverse backgrounds of the users and application areas multiply at the same time. Providing users with visual representations and intuitive user interfaces can significantly aid the understanding of the domains and knowledge represented by ontologies and Linked Data. There is noone size fits all solution but different use cases demand different visualization and interaction techniques. Ultimately, providing better user interfaces and visual representations will foster user engagement and likely lead to higher quality results in different applications employing ontologies and to the proliferation of Linked Data usage.

User interfaces are essential to easily provide access to the increasing diversity of knowledge modeled in ontologies. As ontologies grow in size and complexity, the demand for comprehensive visualization and sophisticated interaction also rises. In particular, user interfaces are an integral part of ontology engineering, to help bridge the gap between domain experts and ontology engineers. Ontology visualization is not a new topic and a number of approaches have become available in recent years, with some being already well-established, particularly in the field of ontology modeling. In other areas of ontology engineering, such as ontology alignment and debugging, although several tools have recently been  developed, few provide a graphical user interface, not to mention navigational aids or comprehensive visualization techniques.

While ontology users usually possess domain and/or knowledge representation expertise, this is not necessarily the case with potential Linked Data consumers who can come from very different backgrounds and have varying levels of expertise. Currently, the main Linked Data consumers are technology experienced users, one of the reasons being the lack of appropriate user interfaces and visualizations to support other user groups. Visual approaches are needed to assist various kinds of users, who pursue diverse goals and pose individual requirements.

In the presence of a huge network of interconnected resources, one of the challenges faced by the Linked Data community is the visualization of the multidimensional datasets to provide for efficient overview, exploration and querying tasks, to mention just a few. With the focus shifting from a Web of Documents to a Web of Data, changes in the interaction paradigms are in demand as well. Novel approaches also need to take into consideration the technological challenges and opportunities given by new interaction contexts, ranging from mobile and touch interaction to visualizations on large displays, and encompassing highly responsive web applications.

The VOILA! workshop addressed these and related issues in its call for papers and attracted 18 submissions in different paper categories. Three reviewers were assigned to each submission. Based on their reviews we selected 12 contributions for presentation at the workshop in the following categories: full research papers (5), position papers (2) and short papers (5).

The first edition of VOILA! is co-located with the 14th International Semantic Web Conference (ISWC 2015) and will take place as a full day event on October 11, 2015 in Bethlehem, Pennsylvania, USA. It will be organized around paper presentations and discussions and will be accompanied by interactive software demonstrations, giving developers a chance to gather feedback from the community.

We thank all authors for their submissions and all members of the VOILA! program committee for their useful reviews and comments. We are grateful to Miriam Fernandez and Krzysztof Janowicz, the ISWC workshop chairs, for their continuous support during theworkshop organization. The workshop would not be possible without all of you!

September 2015

Valentina Ivanova, Patrick Lambrix, Steffen Lohmann, Catia Pesquita

A picture is worth a thousand words, we often say, yet many areas are in demand of sophisticated visualization techniques, and the Semantic Web is not an exception. The size and complexity of ontologies and Linked Data in the Semantic Web constantly grows and the diverse backgrounds of the users and application areas multiply at the same time. Providing users with visual representations and intuitive interaction techniques can significantly aid the exploration and understanding of the domains and knowledge represented by ontologies and Linked Data.

Ontology visualization is not a new topic and a number of approaches have become available in recent years, with some being already well-established, particularly in the field of ontology modeling. In other areas of ontology engineering, such as ontology alignment and debugging, although several tools have been developed, few provide a graphical user interface, not to mention navigational aids or comprehensive visualization and interaction techniques.

In the presence of a huge network of interconnected resources, one of the challenges faced by the Linked Data community is the visualization of multidimensional datasets to provide for efficient overview, exploration and querying tasks, to mention just a few. With the focus shifting from a Web of Documents to a Web of Data, changes in the interaction paradigms are in demand as well. Novel approaches also need to take into consideration the technological challenges and opportunities given by new interaction contexts, ranging from mobile, touch, and gesture interaction to visualizations on large displays, and encompassing highly responsive web applications.

There is no one-size-fits-all solution but different use cases demand different visualization and interaction techniques. Ultimately, providing better user interfaces, visual representations and interaction techniques will foster user engagement and likely lead to higher quality results in different applications employing ontologies and proliferate the consumption of Linked Data.

Special Theme & Topics of Interest

This year, we plan to have a dedicated look on empirical evidence on the benefits and limitations of visualizations and interactions in the context of the Semantic Web. We are particularly interested in success and failure stories, in learning which visualization and interaction approaches work and which do not - to which extent and in which contexts. We would like to hear about novel research findings and insights, backed with empirical data from user studies and use cases. Submissions addressing this special theme could include one or more of the following:

• success stories
• failure stories
• empirical studies

We also welcome other research contributions providing empirical evidence that advances the field.

Apart from that - and as in the last years -, we are looking for submissions addressing one or more of the following topics, subjects, and contexts (or related ones):

• Topics:
• visualizations
• user interfaces
• visual analytics
• requirements analysis
• case studies
• user evaluations
• cognitive aspects
• Subjects:
• ontologies
• linked data
• ontology engineering (development, collaboration, ontology design patterns, alignment, debugging, evolution, provenance, etc.)
• Contexts:
• classical interaction contexts (desktop, keyboard, mouse, etc.)
• novel interaction contexts (mobile, touch, gesture, etc.)
• special settings (large, high-resolution, and multiple displays, etc.)
• specific user groups and needs (people with disabilities, domain experts, etc.)

Currently one of the challenges for the ontology alignment community is the user involvement in the alignment process. At the same time, the focus of the community has shifted towards large-scale matching which introduces an additional dimension to this issue. This paper aims to provide a set of requirements that foster the user involvement for large-scale ontology alignment tasks.Further, we present and discuss the results of a literature study for 7 ontology alignments systems as well as a heuristic evaluation and an observational user study for 3 ontology alignment systems to reveal the coverage of the requirements in the systems and the support for the requirements in the user interfaces.

As part of an initiative to facilitate adequate identification and display of substance-associated health effects a toxicological ontology - ToxOntology - was created. Further, an alignent with MeSH was accomplished to obtain an indirect index to the scientific literature.

To arrive at satisfactory results in the semantically-enabled applications, high-quality ontologies and alignments are both necessary. A key step towards high quality in this area is debugging the ontologies and their alignments. In this paper we present an experience report on the debugging of ToxOntology and MeSH as well as an alignment.

Information integration systems support researchers in bioinformatics to retrieve data from multiple biological data sources. In this paper we argue that the current approaches should be enhanced by ontological knowledge. We identify the dierent types of ontological knowledge that are available on the Web and propose an approach to use this knowledge to support integrated access to multiple biological data sources. We also show that current ontology-based integration approaches only cover parts of our approach.

During the last decade an enormous amount of biological data has been generated and techniques and tools to analyze this data have been developed. Many of these tools use some form of grouping and are used in, for instance, data integration, data cleaning, prediction of protein functionality, and correlation of genes based on microarray data. A number of aspects influence the quality of the grouping results: the data sources, the grouping attributes and the algorithms implementing the grouping procedure. Many methods exist, but it is often not clear which methods perform best for which grouping tasks. The study of the properties, and the evaluation and the comparison of the different aspects that influence the quality of the grouping results, would give us valuable insight in how the grouping procedures could be used in the best way. It would also lead to recommendations on how to improve the current procedures and develop new procedures. To be able to perform such studies and evaluations we need environments that allow us to compare and evaluate different grouping strategies. In this paper we present a framework, KitEGA, for such an environment, and present its current prototype implementation. We illustrate its use by comparing grouping strategies for classifying proteins regarding biological function and isozymes.

Similarity-based grouping of data entries in one or more data sources is a task underlying many different data management tasks, such as, structuring search results, removal of redundancy in databases and data integration. Similarity-based grouping of data entries is not a trivial task in the context of life science data sources as the stored data is complex, highly correlated and represented at different levels of granularity. The contribution of this paper is two-fold. 1) We propose a method for similarity-based grouping and 2) we show results from test cases. As the main steps the method contains specification of grouping rules, pairwise grouping between entries, actual grouping of similar entries, and evaluation and analysis of the results. Often, different strategies can be used in the different steps. The method enables exploration of the influence of the choices and supports evaluation of the results with respect to given classifications. The grouping method is illustrated by test cases based on different strategies and classifications. The results show the complexity of the similarity-based grouping tasks and give deeper insights in the selected grouping tasks, the analyzed data source, and the influence of different strategies on the results.

Management of data to produce scientific knowledge is a key challenge for biological research in the 21st century. Emerging high-throughput technologies allow life science researchers to produce big data at speeds and in amounts that were unthinkable just a few years ago. This places high demands on all aspects of the workflow: from data capture (including the experimental constraints of the experiment), analysis and preservation, to peer-reviewed publication of results. Failure to recognise the issues at each level can lead to serious conflicts and mistakes; research may then be compromised as a result of the publication of non-coherent protocols, or the misinterpretation of published data. In this report, we present the results from a workshop that was organised to create an ontological data-modelling framework for Laboratory Protocol Standards for the Molecular Methods Database (MolMeth). The workshop provided a set of short- and long-term goals for the MolMeth database, the most important being the decision to use the established EXACT description of biomedical ontologies as a starting point.