Like any technology, the Semantic Web crucially depends on its developers. Many of us have created SemWeb software, but only few have gotten the opportunity to talk about what is so special about the code they wrote.

The ESWC2015 Developers Workshop therefore provides a forum for SemWeb and Linked Data developers.

We unite developers on May 31st 2015 to discuss about topics we passionately care about:

- How to develop applications on top of Linked Data?

- How can browser applications influence the Semantic Web?

- How to create libraries for technologies such as RDF (JSON-LD / Turtle / …), SPARQL, PROV?

- What about mobile and native applications?

- How to do semantic development for a specific domain?

In other words, this workshop is about how you made things work. It is about implementations, methods, techniques, about how you solved practical problems for Linked Data.

In contrast to the ESWC main and demo tracks, we do focus on implementation-specific technical concepts. We want to see how you deal with the Semantic Web in JavaScript, Python, Java, C++, Erlang, Perl, Ruby, … how a library or application was designed, and what the rationale behind your design decisions is.

The Multilingual Semantic Web workshop series is concerned with research questions on how current Semantic Web infrastructure can and should be extended to advance the Semantic Web and linked data use and development across language communities around the world. This raises several challenges such as ontology localisation, cross-lingual question answering, cross-lingual ontology and data matching, representation of lexical information on the Web of Data, etc. An infrastructure should be in place for defining ontologies and vocabularies in multiple languages with a transparent semantics across them.

NLP and machine learning for linked data can benefit from exploiting multilingual language resources such as annotated corpora, WordNets, bilingual dictionaries, etc. if they are themselves formally represented and linked by following the linked data principles. We argue that a critical mass of language resources as linked data on the Web can lead to a new generation of linked data-aware NLP techniques and tools which, in turn, will serve as basis for richer multilingual multimedia Web content analysis.

In addressing such research topics, the workshop aims at providing a forum for researchers at the intersection of NLP, multilingual information access, Linked Data and the Semantic Web to exchange ideas on realising the Multilingual Semantic Web.

The WaSABi workshop aims at helping to guide the conversation between the scientific research community, IT practitioners and industry. The workshop helps to establish best practices for the development, deployment and evaluation of Semantic Web based technologies. Both research and industry communities can benefit greatly from this discussion by sharing use cases, user stories, practical development issues, evalutaions and design patterns. The goal of this particular workshop is to analyse the emerging Semantic Technology market, identify and apply appropriate methodologies, highlight success stories and overall support the increase in industry adoption of Semantic Web technologies.

The gap between the Semantic Web research and industry practitioner communities, as evidenced by the limited uptake of very promising technologies in the broader market needs to be addressed. Researchers steadily improve upon modelling, query languages, reasoners, triple stores, and development tooling - but all too often, these improvements are driven not by real business needs, but intrinsically by the interests of researchers to work on and solve interesting challenges, or to obtain public funding. Conversely, practitioners are oftentimes unaware of how existing Semantic Web technologies already can help them to solve their problems (such as data or service integration, conflict detection and resolution, or data processing). Even in cases where they do know about these Semantic Web solutions, most practitioners lack the knowledge about tooling, scalability issues, design patterns that are required in order to successfully apply these technologies.

In order to help analyse and ultimately bridge this gap, the WaSABi organisers believe two things are needed: firstly, a greater understanding of industrial organisations and their needs, guiding them in selecting problems to work on that are of direct relevance to them, and secondly, to establish a set of methodologies, evaluation strategies and best practices for Semantic Web technology development and use, guiding practitioners who want to apply these technologies first hand. The WaSABi workshop provides a forum for discussing and developing solutions to both of these needs, neither of which can be solved by researchers or practitioners on their own.

The World Wide Web has undergone significant changes, developing from a collection of a few interlinked static pages to a global ubiquitous platform for sharing, searching and browsing dynamic and customisable content, in a variety of different media formats. This transformation was triggered by the ever growing number of users and websites and continues to be supported by current developments such as the increased use and popularity of Linked Data and Web APIs. Unfortunately, despite some initial efforts and progress towards integrated use, these two technologies remain mostly disjunct in terms of developing solutions and applications. To this purpose, SALAD aims to explore the possibilities of facilitating a better fusion of Web APIs and Linked Data, thus enabling the harvesting and provisioning of data through applications and services on the Web. In particular, we focus on investigating how both static and dynamic resources (for example, sensor data or streams), exposed via interfaces on the Web, can be used together with semantic data, as a means for enabling a shared use and providing a basis for developing rich applications.

With this workshop, we aim to discover new ways to embrace the opportunities that Web APIs offer in terms of data consumption, processing and provisioning but also to investigate the possibilities of integrating them more closely with Linked Data. We want to challenge researchers towards developing integrated description and implementation approaches through both paper submissions and interactive on-site discussion and dialog. In particular, we are looking for description approaches, implementation solutions, use cases and applications that support a more automated and unified Web API use.

People argue that we have moved into an age of several Webs - the Web of Data, the Web of Documents, the Semantic Web, the Deep Web, the Dark Web, and many more. However, this is an illusion - there exists only the one #WebWeHave, and it is under our control to make it be the #WebWeWant. To emphasize this, the key theme of the 5th edition of the USEWOD workshop is about using the Web in the age of data, bringing together two spaces again that have recently been investigated separately: classical Web usage mining and Web usage mining in the context of the Web of Data.

The purpose of the USEWOD workshop series has been to create and maintain a forum for researchers to investigate the synergy between the Web of Data and Web usage mining. This required the analysis of semantic data usage. With the next edition we respond to the fact that publishing and consuming raw data on the Web is an established paradigm today and turn the USEWOD workshop into a forum to discuss more general questions about the usage of the Web. How will the analysis of Web usage benefit from the possibility to blend the classical log with the structure sourcing from Linked Data? Can the progress that has been made on (Read/Write) Linked Data change the way we interact with the Web and what does that mean for the usage analysis capabilities we have at hand today?

We believe that now is the right time to gain in depth and breadth by shifting the focus on bridging, reusing, and extending methods to solve common problems. Within this general ambition, we remain motivated by the observation made long ago but no less valid today: that the proof of the pudding Web is in the using. Thus, Web usage analysis will remain USEWOD’s methodological core.

To account for this broadened scope, the USEWOD dataset will be extended by usage data from sources that do not fall into the Web of Data category. Wikipedia data will be a critical component of this, allowing for analysis that crosses the boundary between the Web of Data and the Web of Documents, for example when DBpedia and Wikipedia logs are used in combination.

While the Web of Data, and in particular Linked Data, has seen tremendous growth over the past years, take-up, usage and reuse of data is still limited and is often focused on well-known reference datasets. As the Linked Open Data (LOD) Cloud includes data from a variety of domains spread across hundreds of datasets containing billions of entities and facts and is constantly evolving, manual assessment of dataset features is not feasible or sustainable, leading to brief and often outdated dataset metadata. Given the dynamic nature of the LOD Cloud, particular focus should be on the development of scalable automated approaches, which facilitate the frequent assessment and profiling of large-scale datasets to enable the selection of suitable datasets for query federation and, more generally, dataset recommendation for specific applications.

The PROFILES’15 workshop is a continuation of the workshop series successfully started as PROFILES’14 @ ESWC 2014. These workshops aims to gather innovative query and search approaches for large-scale, distributed and heterogeneous linked datasets inline with dedicated approaches to analyse, describe and discover endpoints, as an inherent task of query distribution and dataset recommendation. The PROFILES’15 workshop aims to become a highly interactive research forum for researchers. PROFILES’15 will bring together researchers and practitioners in the fields of Semantic Web and Linked Data, Databases, Semantic Search, Text Mining, NLP as well as Information Retrieval. PROFILES’15 will gather novel works from the fields of semantic query interpretation and federated search for Linked Data, dataset selection and discovery as well as automated profiling of datasets using scalable data assessment and profiling techniques. PROFILES’15 will equally consider both novel scientific methods and techniques for querying, assessment, profiling, discovery of distributed datasets as well as the application perspective, such as the innovative use of tools and methods for providing structured knowledge about distributed datasets, their evolution and fundamentally, means to search and query the Web of Data. We will seek application-oriented, as well as more theoretical papers and position papers.

The relationship between the Web and philosophy is now at a crucial turning point. While a group of philosophers and philosophically-influenced scholars are increasingly interested in the Web, we are facing unprecedented challenges around its future that requires concerted efforts between researcher and disciplines to be properly addressed. With both Internet governance and the very architecture of the Web undergoing rapid change, now is the time for a philosophy of the Web to help to fulfill the Web’s full potential, expanding upon its fundamental principles in new terrains ranging from mass surveillance to the impact of the Internet of things.

Even swifter is the Web-driven transformation of many previously unquestioned philosophical concepts of privacy, authority, meaning, identity, belief, intelligence, cognition, and even embodiment in surprising ways. In response, we hope to provoke the properly philosophical question of whether or not philosophy that can weave these changes to technology and society into a coherent whole that can adapt the principles of the Web to the age of surveillance.

Developing ontologies is not an easy task and, as the ontologies grow in size, they are likely to show a number of defects. Such ontologies, although often useful, also lead to problems when used in semantically-enabled applications. Wrong conclusions may be derived or valid conclusions may be missed. Defects in ontologies can take different forms. Syntactic defects are usually easy to find and to resolve. Defects regarding style include such things as unintended redundancy. More interesting and severe defects are the modeling defects which require domain knowledge to detect and resolve such as defects in the structure, and semantic defects such as unsatisfiable concepts and inconsistent ontologies.

Further, during the recent years more and more mappings between ontologies with overlapping information have been generated, e.g. using ontology alignment systems, thereby connecting the ontologies in ontology networks. This has led to a new opportunity to deal with defects as the mappings and other ontologies in the network may be used in the debugging of a particular ontology in the network. It also has introduced a new difficulty as the mappings may not always be correct and need to be debugged themselves.

Since the start of the Linked Open Data (LOD) Cloud, we have seen an unprecedented volume of structured data published on the web, in most cases as RDF and Linked (Open) Data. The integration across this LOD Cloud, however, is hampered by the ‘publish first, refine later’ philosophy. This is due to various quality problems existing in the published data such as incompleteness, inconsistency, incomprehensibility, etc. These problems affect every application domain, be it scientific (e.g., life science, environment), governmental, or industrial applications.

We see linked datasets originating from crowdsourced content like Wikipedia and OpenStreetMap such as DBpedia and LinkedGeoData and also from highly curated sources e.g. from the library domain. Quality is defined as “fitness for use”, thus DBpedia currently can be appropriate for a simple end-user application but could never be used in the medical domain for treatment decisions. However, quality is a key to the success of the data web and a major barrier for further industry adoption.

Despite the quality in Linked Data being an essential concept, few efforts are currently available to standardize how data quality tracking and assurance should be implemented. Particularly in Linked Data, ensuring data quality is a challenge as it involves a set of autonomously evolving data sources. Additionally, detecting the quality of datasets available and making the information explicit is yet another challenge. This includes the (semi-)automatic identification of problems. Moreover, none of the current approaches uses the assessment to ultimately improve the quality of the underlying dataset.

The goal of the Workshop on Linked Data Quality is to raise the awareness of quality issues in Linked Data and to promote approaches to assess, monitor, maintain and improve Linked Data quality.

Knowledge discovery is an well-established field with a large community investigating methods for the discovery of patterns and regularities in large data sets, including relational databases and unstructured text. Research in this field has led to the development of practically relevant and scalable approaches such as association rule mining, subgroup discovery, graph mining or clustering. At the same time, the Web of Data has grown to one of the largest publicly available collections of structured, cross-domain data sets. While the growing success of Linked Data and its use in applications, e.g., in the e-Government area, has provided numerous novel opportunities, its scale and heterogeneity are posing challenges to knowledge discovery and data mining:

- The extraction and discovery of knowledge from very large data sets;

- The maintenance of high quality data and provenance information;

- The scalability of processing and mining the distributed Web of Data;

- and The discovery of novel links, both on the instance and the schema level.

Contributions from the knowledge discovery field may help foster the future growth of Linked Open Data. Some recent works on statistical schema induction, mapping, and link mining have already shown that there is a fruitful intersection of both fields. With the proposed workshop, we want to investigate possible synergies between the Linked Data and Knowledge Discovery communities, and to explore novel directions for joint research. On the one hand, we wish to stimulate a discussion about how state-of-the-art algorithms for knowledge discovery and data mining can be adapted to fit the characteristics of Linked Data, such as its distributed nature, incompleteness (incl. absence of negative examples), and identify concrete use cases and applications. On the other hand, we hope to show that Linked Data can support traditional knowledge discovery tasks (e.g., as a source of additional background knowledge and of predictive features) for mining from existing, not natively linked data like, for instance, in business intelligence settings.

The workshop addresses researchers and practitioners from the fields of knowledge discovery in databases and data mining, as well as researchers from the Semantic Web community applying such techniques to Linked Data. The goal of the workshop is to provide a platform for knowledge exchange between the different research communities, and to foster future collaborations. We expect at least 30 participants. Authors of contributed papers are especially encouraged to publish their data sets and/or the implementation of their algorithms, and to discuss these implementations and data sets with other attendees. The goal is to establish a common benchmark that can be used for competitive evaluations of algorithms and tools.

Every Semantic Web researcher has been there: you spend days of work, but the results just don’t give the answer you were hoping for. The work ends up, like so many, part of the File Drawer Effect: they never get reported because of negative or inconclusive outcome. This occurs as a result of a publication bias towards positive results in Semantic Web, as in other fields. However, negative or inconclusive results are fundamental to the research process and can be as valuable as positive results.

This workshop provides a forum for such attempted approaches, methodologies, or implementations. Researchers are urged to report null, disappointing or inconclusive attempts in the Semantic Web and Linked Open Data research field. We specifically target sound approaches and, scientifically and technically relevant contributions, that produced negative or inconclusive experimental results.

At SePublica we are interested in addressing the question, how is Semantic Web technology being used as part of publication workflows. Advances in technology have made it possible for publications to adopt electronic dissemination channels, from paper-based to purely electronic formats—e.g. EPUB, HTML, PDF. However, in spite of improvements in the distribution, accessibility and retrieval of information, the connective tissue promised by the Semantic Web is still rare in most publications. We want to help understand how the Semantic Web is supporting publication workflows in, but not limited to, scholarly communication, e-science, and new trends in journalism.

The Web has succeeded as a dissemination platform for news, scientific and non-scientific papers, and communication in general. However, most of that information remains locked up in discrete digital documents that are, sometimes, replicates of their print ancestors. Without machine-friendly content, the level in which data can be explored is limited. For instance, data journalism reflects the increased interaction between content producers (journalists) and several other fields such as design, computer science and statistics. From the point of view of journalists, data journalism represents "an overlapping set of competencies drawn from disparate fields". Journalists are adapting data-driven arguments.

Likewise, the validation of scientific results requires reproducible methods: for reproducibility, data, processes, and algorithms used in the original experiments should be made available in a complete and computationally amenable form. Although biomedical journals often ask for “Materials and Methods” and datasets to be made available, reproducing experiments, sharing, reusing and leveraging scientific data is becoming increasingly difficult. Experimental data in scientific disciplines is a Big Data problem; how can we make effective use of scientific data, how should it be semantically represented, interlinked, reused, how can we effectively represent experiments in scientific publications? How to bridge the gap between publications and data repositories? As both Europe and the US are embarking on big science, e.g., Brain Activity Map (BAM), Human Brain Project (HBP), CERN experiments, massive amounts of data are being generated. Just like in the Human Genome Project, as data is produced, the needs for data management grows exponentially, eventually surpassing those inherent to laboratory work. Thus, data standards and ontologies will become more and more necessary to laboratory sciences. Gaining a deeper understanding of disorders such as schizophrenia, Alzheimer's, suicide and PTSD, amongst others, will require a much more sophisticated infrastructure than those we have so far seen. How are the Semantic Web and ontologies supporting reproducibility and replicability in e-research infrastructures? How is this translated to scholarly publications? Scholarly data and documents are of most value when they are interconnected rather than independent.

Without machine processable data, the possibilities of the Web will remain limited. The network effect of data organically grows in the context of the Web. For open data to succeed the ability to interconnect and join, to summarise and compare, to monitor, extrapolate, and to infer is central. In this way we will soon be able to see paradigm shifts taking place across domains; how is this happening in data journalism? scholarly communication? in web-based communication in general? The Web becomes a platform; we are starting to see this in some e-science domains, however, open challenges remain ahead.

Data streams are an increasingly prevalent source of information in a wide range of domains and applications, e.g. environmental monitoring, disaster response, or smart cities. The RDF model is based on a traditional persisted-data paradigm, where the focus is on maintaining a bounded set of data items in a knowledge base. This paradigm does not fit the case of data streams, where data items flow continuously over time, forming unbounded sequences of data. In this context, the W3C RDF Stream Processing (RSP) Community Group has taken the task to explore the existing technical and theoretical proposals that incorporate streams to the RDF model, and to its query language, SPARQL. More concretely, one of the main goals of the RSP Group is to define a common, but extensible core model for RDF stream processing. This core model can serve as a starting point for RSP engines to be able to talk to each other and interoperate.

The goal of this workshop is to bring together interested members of the community to:

- Demonstrate their latest advances in stream processing systems for RDF.

- Foster discussion for agreeing on a core model and query language for RDF streams.

- Involve and attract people from related research areas to actively participate in the RSP Community Group.

Each of these objectives will intensify interest and participation in the community to ultimately broaden its impact and allow for going towards a standardization process. As a result of this workshop the authors will contribute to the W3C RSP Community Group Report that will be published as part of the group activities. The workshop results, including the best contributions from the submissions will be published in the ESWC Satellite Events proceedings.

To advance and consolidate the development of Semantic Web (SW) technologies and applications, a strong interdisciplinary approach is required. Amongst others, SW technologies imply new challenges for user interaction - and especially end user interaction - beyond those already posed by Web technologies and interactive systems in general. Some of the challenges to Semantic Web Interfaces are:

- SW technologies provide a new set of innovative functionalities, which require equally innovative interfaces to be enjoyed

- Interfaces are required for datasets of which the schemas are not yet fully known at design time

- UIs for datasets where there is as much information in the links between the resources as in the resources themselves

- Interfaces need to be able to deal with different levels of granularity of data and information.

- UIs for information that is automatically derived from potentially incomplete and imperfect data

- Control over data/information delivery, due to size and complexity of data sources

The HSWI workshop aims to explore and evaluate good practices in interface design, and ultimately feed into possible recommendations for standardizing and consolidating knowledge and good practices into a set of guidelines for semantic web developers, to ensure the usability and reasonably functional user experiences of the next generation of Semantic Web applications.

As the Web rapidly evolves, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, wikis, and the like. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the social Web to expand exponentially.

To identify the emotions (e.g. sentiment polarity, sadness, happiness, anger, irony, sarcasm, etc.) and the modality (e.g. doubt, certainty, obligation, liability, desire, etc.) expressed in this continuously growing content is critical to enable the correct interpretation of the opinions expressed or reported about social events, political movements, company strategies, marketing campaigns, product preferences, etc.

This has raised growing interest both within the scientific community, by providing it with new research challenges, as well as in the business world, as applications such as marketing and financial prediction would gain remarkable benefits.

One of the main application tasks in this context is opinion mining, which is addressed by a significant number of Natural Language Processing techniques, e.g. for distinguishing objective from subjective statements, as well as for more fine-grained analysis of sentiment, such as polarity and emotions. Recently, this has been extended to the detection of irony, humor, and other forms of figurative language. In practice, this has led to the organisation of a series of shared tasks on sentiment analysis, including irony and figurative language detection, with the production of annotated data and development of running systems.

However, existing solutions still have many limitations leaving the challenge of emotions and modality analysis still open. For example, there is the need for building/enriching semantic/cognitive resources for supporting emotion and modality recognition and analysis. Additionally, the joint treatment of modality and emotion is, computationally, trailing behind, and therefore the focus of ongoing, current research. Also, while we can produce rather robust deep semantic analysis of natural language, we still need to tune this analysis towards the processing of sentiment and modalities, which cannot be addressed by means of statistical models only, currently the prevailing approaches to sentiment analysis in NLP. The hybridization of NLP techniques with Semantic Web technologies is therefore a direction worth exploring.

This workshop intends to be a discussion forum gathering researchers from Cognitive Linguistics, NLP, Semantic Web, and related areas for presenting their ideas on the relation between Semantic Web and the study of emotions and modalities.

In recent years, researchers in several communities involved in aspects of information science have begun to realise the potential benefits of assigning an important role to events in the representation and organisation of knowledge and media-benefits which can be compared to those of representing entities such as persons or locations instead of just dealing with more superficial objects such as proper names and geographical coordinates. While a good deal of relevant research for example, on the modeling of events has been done in the semantic web community, much complementary research has been done in other, partially overlapping communities, such as those involved in multimedia processing, information extraction, sensor processing and information retrieval research. However, these areas often deal with events with a different perspective.

The attendance at previous DERIVE workshops proved that there is a great interest from many different communities in the role of events. The results presented in there also indicated that dealing with events is still an emerging topic. The goal of this workshop is to advance research on the role of events within the information extraction and semantic web communities, both building on existing work and integrating results and methods from other areas, while focusing on issues of special importance for the semantic web.

Research into both how events can be detected / extracted and modelled / represented for the semantic web is being implemented in various application domains. We encourage submissions about the visualization of events, search and browsing of event data, and interaction with event data within a particular domain. This will contribute to a discussion on the possibly different requirements of models and tools in these domains.

The Open Data and Semantic Web efforts have been promoting and facilitating the publication and integration of data from diverse sources, giving rise to a large and increasing volume of machine-readable data available on the Web. Even though such raw data and its ontological schema enable the interoperability among Web applications, problems arise when exposing them to human users, as to how to present such large-scale, structured data in a user-friendly manner.

To meet the challenge, we invite research contributions on all aspects of ranking, summarization, visualization, and exploration of entities, ontologies, knowledge bases and Web Data, with a particular focus on their summarization and presentation. We also welcome submissions on novel applications of these techniques. The workshop is expected to be a forum that brings together researchers and practitioners from both academia and industry in the areas of Semantic Web, information retrieval, data engineering, and human-computer interaction, to discuss high-quality research and emerging applications, to exchange ideas and experience, and to identify new opportunities for collaboration.

The simplicity with which users can publish content nowadays has made the Web the world’s largest database. Keyword-based search has become the de-facto standard for information discovery in this ocean of data, mainly due to its simplicity that makes it attractive to technically novice users. To answer keyword queries, existing search engines rely on effective indexes of the content that allow them to return the documents that best match the user’s search criteria. This generally leaves out the structure of the data, its semantic dimension, as well as the social aspects to which it may relate. We believe that, in order to exploit the full potential of the Web, structured and rich data will have to receive the same search and retrieve capabilities as the text data from Web documents. However, due to their highly structured nature, the rich semantics, and the data structures by which they are typically managed, a great deal of issues needs to be studied. As the problem is in general of a multifaceted nature, it requires synergies from many different disciplines.

This workshop is jointly organized by the Working Groups 3 and 4 of the COST Action KEYSTONE, which is dedicated to launching and establishing a cooperative network of researchers, practitioners, and application domain specialists working in fields related to semantic data management, the Semantic Web, information retrieval, artificial intelligence, machine learning and natural language processing that coordinates collaboration among them to enable research activity and technology transfer in the area of keyword-based search over structured data sources.

There is a vast and rapidly increasing quantity of scientific, corporate, government and crowd-sourced data published on the emerging Data Web. Open Data are expected to play a catalyst role in the way structured information is exploited in the large scale. This offers a great potential for building innovative products and services that create new value from already collected data. It is expected to foster active citizenship (e.g., around the topics of journalism, greenhouse gas emissions, food supply-chains, smart mobility, etc.) and world-wide research according to the fourth paradigm of science. The most noteworthy advantage of the Data Web is that, rather than documents, facts are recorded, which become the basis for discovering new knowledge that is not contained in any individual source, and solving problems that were not originally anticipated. In particular, Open Data published according to the Linked Data Paradigm are essentially transforming the Web into a vibrant information ecosystem.

Published datasets are openly available on the Web. A traditional view of digitally preserving them by pickling them and locking them away for future use, like groceries, would conflict with their evolution. There are a number of approaches and frameworks, such as the LOD2 stack, that manage a full life-cycle of the Data Web. More specifically, these techniques are expected to tackle major issues such as the synchronisation problem (how can we monitor changes), the curation problem (how can data imperfections be repaired), the appraisal problem (how can we assess the quality of a dataset), the citation problem (how can we cite a particular version of a linked dataset), the archiving problem (how can we retrieve the most recent or a particular version of a dataset), and the sustainability problem (how can we spread preservation ensuring long-term access).

Preserving linked open datasets poses a number of challenges, mainly related to the nature of the LOD principles and the RDF data model. In LOD, datasets representing real-world entities are structured; thus, in LOD, when managing and representing facts we need to take into consideration possible constraints that may hold. Since resources might be interlinked, effective citation measures are required to be in place to enable, for example, the ranking of datasets according to their measured quality. Another challenge is to determine the consequences that changes to one LOD dataset may have to other datasets linked to it. The distributed nature of LOD datasets furthermore makes archiving a headache.

The first DIACHRON workshop aims at addressing the above mentioned challenges and issues by providing a forum for researchers and practitioners who apply linked data technologies to discuss, exchange and disseminate their work. More broadly, this forum will enable communities interested in data, knowledge and ontology dynamics to network and cross-fertilise. The workshop will also serve as a platform for disseminating results of the DIACHRON EU FP7 project (managing the evolution and preservation of the Data Web).

This workshop will provide a leading international and interdisciplinary forum for disseminating the latest research in the field of Semantic Web for the preservation and exploitation of our scientific heritage, the study of the history of ideas and their transmission.

Classicists and historians are interested in developing textual databases, in order to gather and explore large amounts of primary source materials. For a long time, they mainly focused on text digitization and markup. They only recently decided to try to explore the possibility of transferring some analytical processes they previously thought incompatible with automation to knowledge engineering systems, thus taking advantage of the growing set of tools and techniques based on the languages and standards of the semantic Web, such as linked data, ontologies, and automated reasoning. The iconographic data, which are also relevant in history of science and arise similar problematic could be addressed as well and offer suggestive insights for a global methodology for diverse media.

On the other hand, Semantic Web researchers are willing to take up more ambitious challenges than those arising in the native context of the Web in terms of anthropological complexity, addressing meta-semantic problems of flexible, pluralist or evolutionary ontologies, sources heterogeneity, hermeneutic and rhetoric dimensions. Thus the opportunity for a fruitful encounter of knowledge engineers with computer-savvy historians and classicists has come. This encounter may be inscribed within the more general context of digital humanities, a research area at the intersection of computing and the humanities disciplines which is gaining an ever-increasing momentum and where the Linked Open Data is playing an increasingly prominent role.

The purpose of the workshop is to provide a forum for discussion about the methodological approaches to the specificity of annotating “scientific” texts (in the wide sense of the term, including disciplines such as history, architecture, or rhetoric), and to support a collaborative reflection, on possible guidelines or specific models for building historical ontologies. A key goal of the workshop is to emphasize, through precise projects and up-to-date investigation in digital humanities, the benefit of a multidisciplinary research to create and operate on relevantly structured data. One of the main interests of the very topic of pre-modern historical data management lies in historical semantics, and the opportunity to jointly consider how to identify and express lexical, theoretical and material evolutions. Dealing with historical material, a major problem is indeed to handle the discrepancy of the historical terminology compared to the modern one, and, in the case of massive, diachronic data, to take into account the contextual and theoretical meaning of words and sentences and their semantics. Papers on ancient and medieval biological science and zoology are particularly welcome.

The two research areas of Legal Knowledge and Information Systems, and the Semantic Web are very much interconnected. The legal domain is an ideal field of study for Semantic Web researchers, as it uses and contributes to most of the topics that are relevant to the community. Given the complex interactions of legal actors, legal sources and legal processes, as well as the relevance and potential impact of decisions in the juridical and social processes of a country, it provides a challenging context and an important opportunity for groundbreaking research results. At the same time, Semantic Web formalisms and technologies provide a set of technical instruments which can be fruitfully adopted by legal experts to represent, interlink, and reason over legal knowledge and related aspects such as provenance, privacy, and trust. In particular, Semantic Web technology facilitates standards-based legal knowledge representation, which enables the possibility of legal information reuse over the Web.

Ontologies, knowledge extraction and reasoning techniques have been studied by the Artificial Intelligence & Law community for years, but only few and sparse connections with the Semantic Web community have resulted from these interactions. The aim of this workshop is to study the challenges that the legal domain poses to Semantic Web research, and how Semantic Web technologies and formalisms can contribute to address these open issues. This way, we promote the use of legal knowledge for addressing Semantic Web research questions and, vice versa, to use Semantic Web technologies as tools for reasoning over legal knowledge.

With the rapid advancement of mobile technologies, people are using their mobile devices as primary computing platforms and are generating and consuming vast amounts of data on them. However, the lack of Linked Data tools for these devices has left much of this data unstructured and unfit for reuse or integration with other datasets. In this paper, we present Punya, an open source platform that enables easy and rapid development of mobile Linked Data apps. The platform also enables app users to contribute linked data, as well as consume existing linked data sets, paving the path to a burgeoning linked data ecosystem.

The adoption of Linked Data technologies in enterprises in order to improve methods of Data Integration and to introduce new approaches for a more efficient Information Management is becoming increasingly popular. This trend is complemented by activities aimed at commercializing Interlinked (Open) Data, mainly undertaken by publishers and media companies. While professional information providers work on Linked Data infrastructures to be able to deliver better linkable information assets on a more granular level, enterprises are in the process of redesigning information strategies and content workflows. Ultimate goals are heading towards a more efficient reuse and enrichment of knowledge assets and the personalisation of information services.

In this tutorial, we will give several demos and concrete examples of how Linked Data can be used by enterprises in various industries. We will classify those to give a coherent picture over ‘typical application scenarios’ and corresponding benefits arguments. From an information provider’s perspective, we will discuss how Linked Data has become a game changer for the whole content industry. In addition, we will shed light on new business models for Linked Data at the intersection of Linked Data assets, stakeholders and revenue models by introducing the Linked Data Business Cube.

The W3C Linked Data Platform (LDP) is an initiative to produce a standard protocol and a set of best practices for the development of read-write Linked Data applications. The LDP protocol provides clarifications on the use of the HTTP protocol in Linked Data applications, proposes several extensions in the context of Linked Data applications, and enforces further restrictions. As consequence, the LDP specification enables interoperable read-write Linked data applications based on HTTP access to web resources that describe their state using the RDF data model. The standardization of this protocol represents a step forward in the Linked Data community as it lays the ground for the development of interoperable read-write Linked Data applications.

This tutorial provides an overview to the Linked Data Platform including resources, different types of containers, and other features such as paging, patching, etc., and discusses the different design considerations one should take into account when building read-write Linked Data applications. In addition to the theoretical background on the LDP specification and application design, the tutorial consists of a hands on session on how to build a read-write Linked Data application from scratch using LDP4j, an open source Java framework for developing LDP-enabled applications.

In this tutorial we present the life cycle of linked geospatial data and we focus on two important steps: the publication of geospatial data as RDF graphs and the discovery of spatial and temporal links between linked geospatial data. Given the proliferation of geospatial information on the Web many kinds of geospatial data are now becoming available as linked datasets (e.g., Google and Bing maps, user-generated geospatial content etc.). The topic of the tutorial is related to all core research areas of the Semantic Web (e.g., semantic information extraction, transformation of data into RDF graphs, interlinking linked data etc.) since there is often a need to re-consider existing core techniques when we deal with geospatial information. Thus, it is timely to train Semantic Web researchers, especially the ones that are in the early stages of their careers, on the state of the art of this area and invite them to contribute to it.

In this tutorial we give a comprehensive background on data models, query languages, implemented systems for linked geospatial data, we discuss recent approaches on publishing geospatial data as RDF graphs and we discuss techniques for interlinking linked data by taking into account their spatial and temporal extent. The tutorial is complemented with a hands on session that will familiarize the audience with the state-of-the-art tools in publishing and interlinking geospatial information.

The increasing availability of essential information as Linked Open Data has empowered ordinary citizens and organisations with means to increase their understanding of their political, social, economic and cultural environment. Social Media, on the other hand, offer means to share experiences, connect with friends/relatives, keep updated with situations and understand the public perception of events, places or individuals. With the availability of large datasets, a considerable challenge is how to quickly and efficiently consume such datasets. Visual Analytics solutions offer a promising direction of research for consuming large semantic datasets and quickly identify hidden trends, patterns and anomalies, obtain overviews of large data spaces and promptly reach data points of interest. The goal of the tutorial is to bring together researchers and practitioners interested in visual and interactive techniques for exploring Social Media, Linked Open Data and Web Content. The hands-on tutorial will cover technical aspects from two perspectives: data collection and data analysis using Visualisation techniques. Data collection hand-on will include gathering (i) Social Media content, (ii) Linked Data and (iii) performing Information Extraction on Web content. The data analysis hands-on will introduce various open source visualisation tools and will cover design and implementation of web-based visual analytic solutions.

In this tutorial, we will (1) give a comprehensive overview and hands-on training on the conceptual structures of schema.org for e-commerce, including patterns for ownership and demand, (2) present the full tool chain for producing and consuming respective data, (3) explain the long-term vision of linked open commerce, (4) describe the main challenges for future research in the field, and (5) discuss advanced topics, like access control, identity and authentication (e.g. with WebID); micropayment services (like Payswarm), and data management issues from the publisher and consumer perspective. We will also cover research opportunities resulting from the growing adoption and the respective amount of data in RDFa and Microdata syntaxes.

Social media is fast becoming a crucial part of our everyday lives, not only as a fun and practical way to share our interests and activities with geographically distributed networks of friends, but as an important part of our business lives also. Processing social media is particularly problematic for automated tools, because it is a strong departure from the tradition of newswire that many tools were developed with and evaluated against, and also due to the terse and low-context language it typically comprises.

This tutorial will address these issues in the context of the semantic web by introducing some of the problems faced by using NLP tools on social media, and solutions to these problems, including those specifically implemented in GATE and recently made publicly available. It will demonstrate techniques for extracting the relevant information from unstructured text in social media, so that participants will be equipped with the necessary building blocks of knowledge to build their own tools and tackle complex issues. Since all of the NLP tools to be presented are open source, the tutorial will provide the attendees with skills which are easy to apply and do not require special software or licenses.

This tutorial covers the basics of computational social sciences from a computer science perspective, and its application to Semantic Web research. The tutors will teach the basics of applying theory, methods and techniques across various scenarios. We will cover the application of theory as a means to interrogate the start-to-end process of experimentation, and teach how to work in the qualitative and quantitative paradigm, and how to add real-world meaning to experiments.

We are witnessing an era where advancements in networking, software and hardware technologies are enabling billions of smart devices and objects to be connected to the Internet. While the new technologies pave the way towards a new generation of smart city applications and services, the variation in modality, quality, formats and representations of smart city data are amongst the crucial issues that hinder effective use of the real world data collected by the sensory devices. Typically in a smart city framework, higher­ level abstractions need to be created from real world data streams using data analytic methods for event processing and knowledge extraction purposes in a scalable way. Additionally, Semantic Web technologies and Linked Data play a key role in providing interoperability, association analysis, information and knowledge extractions, and reasoning about trust, privacy, provenance, and security in smart city frameworks. This tutorial will explore different processing steps for smart city data from data acquisition and semantic annotation to event processing, information extraction and pattern analysis in different smart city scenarios and using available cutting­ edge tools.

In this workshop, we want to provide a forum for people who are interested in running semantic technology on/with the help of small devices, like Raspberry Pi, MicroPython, ATmega/Arduino, PIC/PICAXE. The main purpose is to exchange ideas and experiences and maybe develop nice demoes / prototypes during the event. So bring all your Rasberry Pis, breadboards, sensors, displays, cabling with you, and we will for sure find exiting new ways of using this stuff for semantic technologies! Soldering irons welcome, but not mandatory.
Topics:

• - Running triplestores on small devices
• - Wireless discovery of small devices (RFID, Bluetooth, 433 MHz, IR, etc.)
• - Internet-of-Things Hands-on
• - Practical challenges
• - Theoretical challenges
• - Fun

Chairs:

• Hepp, Martin Universität der Bundeswehr München, Germany
• Kjetil Kjernsmo, The University of Oslo, Norway