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When it comes to complex machine learning models, commonly referred to as black boxes, understanding the underlying decision making process is crucial for domains such as healthcare and financial services, as well as when they are used in connection with safety critical systems such as autonomous vehicles. As a result, interest in explainable artificial intelligence (xAI) tools and techniques has increased in recent years. However, the user experience (UX) effectiveness of existing xAI frameworks, especially concerning algorithms that work with data as opposed to images, is still an open research question. In order to address this gap, we examine the UX effectiveness of the Local Interpretable Model-Agnostic Explanations (LIME) xAI framework, one of the most popular model agnostic frameworks found in the literature, with a specific focus on its performance in terms of making tabular models more interpretable. In particular, we apply several state of the art machine learning algorithms on a tabular dataset, and demonstrate how LIME can be used to supplement conventional performance assessment methods. Based on this experience, we evaluate the understandability of the output produced by LIME both via a usability study, involving participants who are not familiar with LIME, and its overall usability via a custom made assessment framework, called Model Usability Evaluation (MUsE), which is derived from the International Organisation for Standardisation 9241-11:2018 standard.

The Internet of Things (IoT) refers to a network of connected devices that collects and exchanges data through the Internet. These things can be artificial or natural and interact as autonomous agents that form a complex system. In turn, business process management (BPM) was established to analyze, discover, design, implement, execute, monitor, and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and in practice, we strongly believe that, on the one hand, the management of IoT applications will greatly benefit from BPM concepts, methods, and technologies. On the other hand, the IoT poses challenges that will require enhancements and extensions of the current state of the art in the BPM field. In this article, we question the extent to which these two paradigms can be combined, and we discuss emerging challenges and intersections from a research and practitioner's point of view in terms of complex software systems development.

A fundamental assumption of Business Process Management (BPM) is that redesign delivers refined and improved versions of business processes. This assumption, however, does not necessarily hold, and any required compensatory action may be delayed until a new round in the BPM life-cycle completes. Current approaches to process redesign face this problem in one way or another, which makes rapid process improvement a central research problem of BPM today. In this paper, we address this problem by integrating concepts from process execution with ideas from DevOps. More specifically, we develop a methodology called AB-BPM that offers process improvement validation in two phases: simulation and AB tests. Our simulation technique extracts decision probabilities and metrics from the event log of an existing process version and generates traces for the new process version based on this knowledge. The results of simulation guide us towards AB testing where two versions (A and B) are operational in parallel and any new process instance is routed to one of them. The routing decision is made at runtime on the basis of the achieved results for the registered performance metrics of each version. Our routing algorithm provides for ultimate convergence towards the best performing version, no matter if it is the old or the new version. We demonstrate the efficacy of our methodology and techniques by conducting an extensive evaluation based on both synthetic and real-life data.

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems.

Semantic web technologies have shown their effectiveness, especially when it comes to knowledge representation, reasoning , and data integration. However, the original semantic web vision, whereby machine readable web data could be automatically actioned upon by intelligent software web agents, has yet to be realised. In order to better understand the existing technological opportunities and challenges, in this paper we examine the status quo in terms of intelligent software web agents, guided by research with respect to requirements and architectural components, coming from the agents community. We use the identified requirements to both further elaborate on the semantic web agent motivating use case scenario, and to summarise different perspectives on the requirements from the semantic web agent literature. We subsequently propose a hybrid semantic web agent architecture, and use the various components and subcomponents in order to provide a focused discussion in relation to existing semantic web standards and community activities. Finally, we highlight open research opportunities and challenges and take a broader perspective of the research by discussing the potential for intelligent software web agents as an enabling technology for emerging domains, such as digital assistants, cloud computing, and the internet of things.

The analysis of court decisions and associated events is part of the daily life of many legal practitioners. Unfortunately, since court decision texts can often be long and complex, bringing all events relating to a case in order, to understand their connections and durations is a time-consuming task. Automated court decision timeline generation could provide a visual overview of what happened throughout a case by representing the main legal events, together with relevant temporal information. Tools and technologies to extract events from court decisions however are still underdeveloped. To this end, in the current paper we compare the effectiveness of three different extraction mechanisms, namely deep learning, conditional random fields, and rule-based method, to facilitate automated extraction of events and their components (i.e., the event type, who was involved, and when it happened). In addition, we provide a corpus of manually annotated decisions of the European Court of Human Rights, which shall serve as a gold standard not only for our own evaluation, but also for the research community for comparison and further experiments.

The success of crowdsourcing based annotation of text corpora depends on ensuring that crowdworkers are sufficiently well-trained to perform the annotation task accurately. To that end, a frequent approach to train annotators is to provide instructions and a few example cases that demonstrate how the task should be performed (referred to as the CONTROL approach). These globally defined "task-level examples", however, (i) often only cover the common cases that are encountered during an annotation task; and (ii) require effort from crowdworkers during the annotation process to find the most relevant example for the currently annotated sample. To overcome these limitations, we propose to support workers in addition to task-level examples, also with "task-instance level" examples that are semantically similar to the currently annotated data sample (referred to as Dynamic Examples for Annotation, DEXA). Such dynamic examples can be retrieved from collections previously labeled by experts, which are usually available as gold standard dataset. We evaluate DEXA on a complex task of annotating participants, interventions, and outcomes (known as PIO) in sentences of medical studies. The dynamic examples are retrieved using BioSent2Vec, an unsupervised semantic sentence similarity method specific to the biomedical domain. Results show that (i) workers of the DEXA approach reach on average much higher agreements (Cohen's Kappa) to experts than workers of the the CONTROL approach (avg. of 0.68 to experts in DEXA vs. 0.40 in CONTROL); (ii) already three per majority voting aggregated annotations of the DEXA approach reach substantial agreements to experts of 0.78/0.75/0.69 for P/I/O (in CONTROL 0.73/0.58/0.46). Finally, (iii) we acquire explicit feedback from workers and show that in the majority of cases (avg. 72%) workers find the dynamic examples useful., 4 pages, 1 figure, 3 tables, accepted to SIGIR2020

Empirical software engineering (ESE) focuses on gathering evidence through measurements and experiments involving humans and software systems (software products, processes, and resources). While empirical studies often include considerable human effort for study planning, execution, and data analysis, human computation (HC) methods, such as crowdsourcing, are increasingly used to address human input intensive tasks in software engineering and beyond. Therefore, in this chapter, we explore the use of HC techniques to support ESE experiments. We address researchers from both research communities and provide (1) introductory notions into both fields, (2) an analysis of ESE experiment requirements and HC capabilities that could match those, and (3) a concrete example of an ESE experiment that compares the effects of using HC in software inspection with respect to a traditional inspection process preformed using pen and paper. Our focus is on software inspection for detecting defects in software engineering models (namely, extended entity relationship models). This chapter will enable ESE researchers to apply HC in their work and HC researchers to explore ESE as a new application area to further improve their methods and tools.

This editorial paper introduces a special issue that solicited papers at the intersection of Semantic Web and Human Computation research. Research in that inter-disciplinary space dates back a decade, and has been acknowledged as a research line of its own by a seminal research manifesto published in 2015. But where do we stand in 2018? How did this research line evolve during the last decade? How do the papers in this special issue align with the main lines of work of the community? In this editorial we inspect and reflect on the evolution of research at the intersection of Semantic Web and Human Computation. We use a methodology based on Systematic Mapping Studies to collect quantitative bibliographic data which we analyze through the lens of research topics envisioned by the research manifesto to characterize the evolution of research in this area, thus providing a context for introducing the papers of this special issue. We found evidences of a thriving research field; while steadily maturing, the field offers a number of open research opportunities for work where Semantic Web best practices and techniques are applied to support and improve the state-of-the-art in Human Computation, but also for work that exploits the strength of both areas to address scientifically and societally relevant issues.

Algorithm fairness is an established line of research in the machine learning domain with substantial work while the equivalent in the recommender system domain is relatively new. In this article, we consider rating-based recommender systems which model the recommendation process as a prediction problem. We consider different types of biases that can occur in this setting, discuss various fairness definitions, and also propose a novel bias mitigation strategy to address potential unfairness in a rating-based recommender system. Based on an analysis of fairness metrics used in machine learning and a discussion of their applicability in the recommender system domain, we map the proposed metrics from the two domains and identify commonly used concepts and definitions of fairness. Finally, to address unfairness and potential bias against certain groups in a recommender system, we develop a bias mitigation algorithm and conduct case studies on one synthetic and one empirical dataset to show its effectiveness. Our results show that unfairness can be significantly lowered through our approach and that bias mitigation is a fruitful area of research for recommender systems.

International audience; This editorial introduces the special issue based on the best papers from ESWC 2015. And since ESWC'15 marked 15 years of Semantic Web research, we extended this editorial to a position paper that reflects the path that we, as a community, traveled so far with the goal of transforming the Web of Pages to a Web of Resources. We discuss some of the key challenges, research topics and trends addressed by the Semantic Web community in its journey. We conclude that the symbiotic relation of our community with the Web requires a truly multidisciplinary research approach to support the Web's diversity.

In recent years we have seen significant advances in the technology used to both publish and consume structured data using the existing web infrastructure, commonly referred to as the Linked Data Web. However, in order to support the next generation of e-business applications on top of Linked Data suitable forms of access control need to be put in place. This paper provides an overview of the various access control models, standards and policy languages, and the different access control enforcement strategies for the Resource Description Framework (the data model underpinning the Linked Data Web). A set of access control requirements that can be used to categorise existing access control strategies is proposed and a number of challenges that still need to be overcome are identified.

Extracting and analyzing affective knowledge from social media in a structured manner is a challenging task. Decision makers require insights into the public perception of a company's products and services, as a strategic feedback channel to guide communication campaigns, and as an early warning system to quickly react in the case of unforeseen events. The approach presented in this article goes beyond bipolar metrics of sentiment. It combines factual and affective knowledge extracted from rich public knowledge bases to analyze emotions expressed toward specific entities (targets) in social media. The authors obtain common and common-sense domain knowledge from DBpedia and ConceptNet to identify potential sentiment targets. They employ affective knowledge about emotional categories available from SenticNet to assess how those targets and their aspects (such as specific product features) are perceived in social media. An evaluation shows the usefulness and correctness of the extracted domain knowledge, which is used in a proof-of-concept data analytics application to investigate the perception of car brands on social media in the period between September and November 2015.

Blockchain technology provides basic building blocks to support the execution of collaborative business processes involving mutually untrusted parties in a decentralized environment. Several research proposals have demonstrated the feasibility of designing blockchain-based collaborative business processes using a high-level notation, such as the Business Process Model and Notation (BPMN), and thereon automatically generating the code artifacts required to execute these processes on a blockchain platform. In this paper, we present the conceptual foundations of model-driven approaches for blockchain-based collaborative process execution and we compare two concrete approaches, namely Caterpillar and Lorikeet.

The European General Data Protection Regulation (GDPR) brings new challenges for companies, who must demonstrate that their systems and business processes comply with usage constraints specified by data subjects. However, due to the lack of standards, tools, and best practices, many organizations struggle to adapt their infrastructure and processes to ensure and demonstrate that all data processing is in compliance with users' given consent. The SPECIAL EU H2020 project has developed vocabularies that can formally describe data subjects' given consent as well as methods that use this description to automatically determine whether processing of the data according to a given policy is compliant with the given consent. Whereas this makes it possible to determine whether processing was compliant or not, integration of the approach into existing line of business applications and ex-ante compliance checking remains an open challenge. In this short paper, we demonstrate how the SPECIAL consent and compliance framework can be integrated into Linked Widgets, a mashup platform, in order to support privacy-aware ad-hoc integration of personal data. The resulting environment makes it possible to create data integration and processing workflows out of components that inherently respect usage policies of the data that is being processed and are able to demonstrate compliance. We provide an overview of the necessary meta data and orchestration towards a privacy-aware linked data mashup platform that automatically respects subjects' given consents. The evaluation results show the potential of our approach for ex-ante usage policy compliance checking within the Linked Widgets Platforms and beyond.

Business process compliance checking enables organisations to assess whether their processes fulfil a given set of constraints, such as regulations, laws, or guidelines. Whilst many process analysts still rely on ad-hoc, often handcrafted per-case checks, a variety of constraint languages and approaches have been developed in recent years to provide automated compliance checking. A salient example is Declare, a well-established declarative process specification language based on temporal logics. Declare specifies the behaviour of processes through temporal rules that constrain the execution of tasks. So far, however, automated compliance checking approaches typically report compliance only at the aggregate level, using binary evaluations of constraints on execution traces. Consequently, their results lack granular information on violations and their context, which hampers auditability of process data for analytic and forensic purposes. To address this challenge, we propose a novel approach that leverages semantic technologies for compliance checking. Our approach proceeds in two stages. First, we translate Declare templates into statements in SHACL, a graph-based constraint language. Then, we evaluate the resulting constraints on the graph-based, semantic representation of process execution logs. We demonstrate the feasibility of our approach by testing its implementation on real-world event logs. Finally, we discuss its implications and future research directions.

Process models are an important means to capture information on organizational operations and often represent the starting point for process analysis and improvement. Since the manual elicitation and creation of process models is a time-intensive endeavor, a variety of techniques have been developed that automatically derive process models from textual process descriptions. However, these techniques, so far, only focus on the extraction of traditional, imperative process models. The extraction of declarative process models, which allow to effectively capture complex process behavior in a compact fashion, has not been addressed. In this paper we close this gap by presenting the first automated approach for the extraction of declarative process models from natural language. To achieve this, we developed tailored Natural Language Processing techniques that identify activities and their inter-relations from textual constraint descriptions. A quantitative evaluation shows that our approach is able to generate constraints that closely resemble those established by humans. Therefore, our approach provides automated support for an otherwise tedious and complex manual endeavor.

Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases. European Union (FP7/2007-2013) - 318275 (GET Service)

This paper presents an expert system to monitor social sustainability compliance in supply chains. The system allows to continuously rank suppliers based on their risk of breaching sustainability standards on child labor. It uses a Bayesian network to determine the breach likelihood for each supplier location based on the integration of statistical data, audit results and public reports of child labor incidents. Publicly available statistics on the frequency of child labor in different regions and industries are used as contextual prior. The impact of audit results on the breach likelihood is calibrated based on expert input. Child labor incident observations are included automatically from publicly available news sources using text mining algorithms. The impact of an observation on the breach likelihood is determined by its relevance, credibility and frequency. Extensive tests reveal that the expert system correctly replicates the decisions of domain experts in the fields supply chain management, sustainability management, and risk management.

Business process improvement ideas can be validated through sequential experiment techniques like AB Testing. Such approaches have the inherent risk of exposing customers to an inferior process version, which is why the inferior version should be discarded as quickly as possible. In this paper, we propose a contextual multi-armed bandit algorithm that can observe the performance of process versions and dynamically adjust the routing policy so that the customers are directed to the version that can best serve them. Our algorithm learns the best routing policy in the presence of complications such as multiple process performance indicators, delays in indicator observation, incomplete or partial observations, and contextual factors. We also propose a pluggable architecture that supports such routing algorithms. We evaluate our approach with a case study. Furthermore, we demonstrate that our approach identifies the best routing policy given the process performance and that it scales horizontally.

Blockchain technology opens up new opportunities for Business Process Management. This is mainly due to its unprecedented capability to let transactions be automatically executed and recorded by Smart Contracts in multi-peer environments, in a decentralised fashion and without central authoritative players to govern the workflow. In this way, blockchains also provide traceability. Traceability of information plays a pivotal role particularly in those supply chains where multiple parties are involved and rigorous criteria must be fulfilled to lead to a successful outcome. In this paper, we investigate how to run a business process in the context of a supply chain on a blockchain infrastructure so as to provide full traceability of its run-time enactment. Our approach retrieves information to trace process instances execution solely from the transactions written on-chain. To do so, hash-codes are reverse-engineered based on the Solidity Smart Contract encoding of the generating process. We show the results of our investigation by means of an implemented software prototype, with a case study on the reportedly challenging context of the pharmaceutical supply chain.

The amount of raw data exchanged via web protocols is steadily increasing. Although the Linked Data infrastructure could potentially be used to selectively share RDF data with different individuals or organisations, the primary focus remains on the unrestricted sharing of public data. In order to extend the Linked Data paradigm to cater for closed data, there is a need to augment the existing infrastructure with robust security mechanisms. At the most basic level both access control and encryption mechanisms are required. In this paper, we propose a flexible and dynamic mechanism for securely storing and efficiently querying RDF datasets. By employing an encryption strategy based on Functional Encryption (FE) in which controlled data access does not require a trusted mediator, but is instead enforced by the cryptographic approach itself, we allow for fine-grained access control over encrypted RDF data while at the same time reducing the administrative overhead associated with access control management.

Novel social media collaboration platforms, such as games with a purpose and mechanised labour marketplaces, are increasingly used for enlisting large populations of non-experts in crowdsourced knowledge acquisition processes. Climate Quiz uses this paradigm for acquiring environmental domain knowledge from non-experts. The game’s usage statistics and the quality of the produced data show that Climate Quiz has managed to attract a large number of players but noisy input data and task complexity led to low player engagement and suboptimal task throughput and data quality. To address these limitations, the authors propose embedding the game into a hybrid-genre workflow, which supplements the game with a set of tasks outsourced to micro-workers, thus leveraging the complementary nature of games with a purpose and mechanised labour platforms. Experimental evaluations suggest that such workflows are feasible and have positive effects on the game’s enjoyment level and the quality of its output.

Ontologies are key Semantic Web technologies (SWTs) that provide means to formally and explicitly represent domain knowledge in terms of key domain concepts and their relations. Therefore, the creation of intelligent engineering applications (IEAs) that rely on SWTs depends on the creation of a suitable ontology that semantically models engineering knowledge and the representation of engineering data in terms of this ontology (i.e., through a knowledge acquisition process). The tasks of semantic modellingSemantic modelling and acquisition of engineering knowledge are, however, complex tasks that rely on specialized skills provided by a knowledge engineer and can therefore be daunting for those SWT adopters that do not possess this skill set. This chapter aims to support these SWT adopters by summing up essential knowledge for creating and populating ontologies including: ontology engineering methodologies and methods for assessing the quality of the created ontologies. The chapter provides examples of concrete engineering ontologies, and classifies these engineering ontologies in a framework based on the Product-Process-Resource abstraction. The chapter also contains examples of best practices for modelling common situations in the engineering domain using ontology design patterns, and gives an overview of the current tools that engineers ca use to lift engineering data stored in legacy formats (such as, spreadsheets, XML files, and databases, etc.) to a semantic representation.

The process of engineering cyber-physical production systems (CPPS) relies on the collaborative work of multiple and diverse teams of engineers who need to exchange and synchronize data created by their domain-specific tools. Building applications that support the CPPS engineering process requires technologies that enable integrating and making sense of heterogeneous datasets produced by various engineering disciplines. Are Semantic Web technologies suitable to fulfill this task? This chapter aims to answer this question by introducing the reader to key Semantic Web concepts in general and core Semantic Web technologies (SWT) in particular, including ontologies, Semantic Web knowledge representation languages, and Linked Data. The chapter concludes this technology overview with a specific focus on core SWT capabilities that qualify them for intelligent engineering applications. These capabilities include (i) formal and flexible semantic modeling, (ii) intelligent, web-scale knowledge integration, (iii) browsing and exploration of distributed data sets, (iv) knowledge quality assurance and (v) knowledge reuse.

This is the first book to explore how Semantic Web technologies (SWTs) can be used to create intelligent engineering applications (IEAs). Technology-specific chapters reflect the state of the art in relevant SWTs and offer guidelines on how they can be applied in multi-disciplinary engineering settings characteristic of engineering production systems. In addition, a selection of case studies from various engineering domains demonstrate how SWTs can be used to create IEAs that enable, for example, defect detection or constraint checking. Part I Background and Requirements of Industrie 4.0 for Semantic Web Solutions provides the background information needed to understand the book and addresses questions concerning the semantic challenges and requirements of Industrie 4.0, and which key SWT capabilities may be suitable for implementing engineering applications. In turn, Part II Semantic Web-Enabled Data Integration in Multi-Disciplinary Engineering focuses on how SWTs can be used for data integration in heterogeneous, multi-disciplinary engineering settings typically encountered in the creation of flexible production systems. Part III Creating Intelligent Applications for Multi-Disciplinary Engineering demonstrates how the integrated engineering data can be used to support the creation of IEAs, while Part IV Related and Emerging Trends in the Use of Semantic Web in Engineering presents an overview of the broader spectrum of approaches that make use of SWTs to support engineering settings. A final chapter then rounds out the book with an assessment of the strengths, weaknesses and compatibilities of SWTs and an outlook on future opportunities for applying SWTs to create IEAs in flexible industrial production systems. This book seeks to build a bridge between two communities: industrial production on one hand and Semantic Web on the other. Accordingly, stakeholders from both communities should find this book useful in their work. Semantic Web researchers will gain a better understanding of the challenges and requirements of the industrial production domain, offering them guidance in the development of new technologies and solutions for this important application area. In turn, engineers and managers from engineering domains will arrive at a firmer grasp of the benefits and limitations of using SWTs, helping them to select and adopt appropriate SWTs more effectively. In addition, researchers and students interested in industrial production-related issues will gain valuable insights into how and to what extent SWTs can help to address those issues.

With the recent rapid growth of the Semantic Web (SW), the processes of searching and querying content that is both massive in scale and heterogeneous have become increasingly challenging. User-friendly interfaces, which can support end users in querying and exploring this novel and diverse, structured information space, are needed to make the vision of the SW a reality. We present a survey on ontology-based Question Answering (QA), which has emerged in recent years to exploit the opportunities offered by structured semantic information on the Web. First, we provide a comprehensive perspective by analyzing the general background and history of the QA research field, from influential works from the artificial intelligence and database communities developed in the 70s and later decades, through open domain QA stimulated by the QA track in TREC since 1999, to the latest commercial semantic QA solutions, before tacking the current state of the art in open userfriendly interfaces for the SW. Second, we examine the potential of this technology to go beyond the current state of the art to support end-users in reusing and querying the SW content. We conclude our review with an outlook for this novel research area, focusing in particular on the R&D directions that need to be pursued to realize the goal of efficient and competent retrieval and integration of answers from large scale, heterogeneous, and continuously evolving semantic sources.

The increased availability and robustness of sensors, the wide-spread use of the internet as a communication environment and the intensified adoption of semantic technologies foster the vision of embedding intelligence in physical objects. The race of realizing this vision is pervasive to a variety of research fields, most notably ambient intelligence and semantic web, and leads to the proliferation of several overlapping definitions and terminologies: smart products, semantic devices, semantic gadgets - to which we collectively refer to as smart objects. What exactly are smart objects? And what are the research challenges in realizing them? We hereby explore the answers to these questions. Part of the work reported in this paper has been performed by the author in the context of the FP7 SmartProducts project (231204) while working at the Knowledge Media Institute(KMi), The Open University, UK.

Although research on integrating semantics with the Web started almost as soon as the Web was in place, a concrete Semantic Web that is, a large-scale collection of distributed semantic metadata emerged only over the past four to five years. The Semantic Web's embryonic nature is reflected in its existing applications. Most of these applications tend to produce and consume their own data, much like traditional knowledge- based applications, rather than actually exploiting the Semantic Web as a large-scale information source. These first-generation semantic Web applications typically use a single ontology that supports integration of resources selected at design time.

In the context of the semantic Web, notions of collaboration, interoperability, and reuse are intrinsically interrelated: interoperability implies reuse, which in turn is a form of collaboration. If we adopt this viewpoint, the semantic Web becomes essentially a medium for knowledge exchange, in which the knowledge produced by one agent is consumed by another agent, possibly to create new knowledge. A collaboration-centric perspective on the semantic Web introduces both challenges and interesting research directions. Clearly, it's no longer desirable that the process of authoring semantic content necessarily take place in a room full of experts and ontologists. Indeed, as we move from classic Web 2.0 technologies to scenarios in which semantic content is created, our technologies need to change so that it becomes possible to create and exchange knowledge transparently, in accordance with the medium's openness and the knowledge producers' various goals and skill levels. In this article, we examine three modalities for collaborative semantic authoring based on selecting and reusing external, open semantic resources. These three ways of creating semantic content correspond to three levels of interaction between the user and the created resources. We illustrate each level by describing a tool we designed in the context of our research on semantic Web applications at the Knowledge Media Institute.

The success of the Web services technology has brought topics as software reuse and discovery once again on the agenda of software engineers. While there are several efforts towards automating Web service discovery and composition, many developers still search for services via online Web service repositories and then combine them manually. However, from our analysis of these online repositories, it yields that, unlike traditional software libraries, they rely on little metadata to support service discovery. We believe that the major cause is the difficulty of automatically deriving metadata that would describe rapidly changing Web service collections. In this paper, we discuss the major shortcomings of state of the art Web service repositories and as a solution, we report on ongoing work and ideas on how to use techniques developed in the context of the Semantic Web (ontology learning, matching, metadata based presentation) to improve the current situation.

Complementing studies on the economic impact of Open Government Data (OGD), we investigate how this novel Web-enabled movement supports sustainability. An analysis of OGD-based applications reveals that: (1) OGD supports all three pillars of sustainability; (2) citizens and app developers alike are receptive and motivated by sustainability implications of OGD; and (3) few regional differences exist between Vienna and New York City. We derive recommendations for further improving the sustainability impact of OGD.

Vocabularies, Schemas, Ontologies.- Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment.- RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration.- VocBench: a Web Application for Collaborative Development of Multilingual Thesauri.- Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning.- Reasoning.- A Context-Based Semantics for SPARQL Property Paths over the Web.- Distributed and Scalable OWL EL Reasoning.- Large scale rule-based Reasoning using a Laptop.- RDF Digest: Efficient Summarization of RDF/S KBs.- Linked Data.- A Comparison of Data Structures to Manage URIs on the Web of Data.- Heuristics for Fixing Common Errors in Deployed schema.org Microdata.- Semantic Web and Web Science.- LOD-based Disambiguation of Named Entities in @tweets through Context enrichment.- Knowledge Enabled Approach to Predict the Location of Twitter Users.- Semantic Data Management, Big data, Scalability.- A Compact In-Memory Dictionary for RDF data.- Quality Assessment of Linked Datasets using Probabilistic Approximations.- Cooperative Techniques for SPARQL Query Relaxation in RDF Databases.- HDT-MR: A Scalable Solution for RDF Compression with HDT and MapReduce.- Processing Aggregate Queries in a Federation of SPARQL Endpoints.- A survey of HTTP caching implementations on the open Semantic Web.- Query Execution Optimization for Clients of Triple Pattern Fragments.- Natural Language Processing and Information Retrieval LIME: the Metadata Module for OntoLex.- Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text.- HAWK Hybrid Question Answering using Linked Data.- Machine Learning.- Automating RDF Dataset Transformation and Enrichment.- Semi-supervised Instance Matching Using Boosted Classifiers.- Assigning Semantic Labels to Data Sources.- Inductive Classification through Evidence-based Models and their Ensembles.- Mobile Web, Internet of Things and Semantic Streams.- Standardized and Efficient RDF Encoding for Constrained Embedded Networks.- Services, Web APIs, and the Web of Things SPSC: Efficient Composition of Semantic Services in Unstructured P2P Networks.- Data as a Service: The Semantic Web Redeployed.- Cognition and Semantic Web.- On Coherent Indented Tree Visualization of RDF Graphs.- Gagg: A Graph Aggregation Operator.- FrameBase: Representing N-ary Relations using Semantic Frames.- Human Computation and Crowdsourcing.- Towards hybrid NER: a study of content and crowdsourcing-related performance factors.- Ranking Entities in the Age of Two Webs, An Application to Semantic Snippets.- In-Use Industrial Track.- Troubleshooting and Optimizing Named Entity Resolution Systems in the Industry.- Using Ontologies For Modeling Virtual Reality Scenarios.- Supporting Open Collaboration in Science through Explicit and Linked Semantic Description of Processes.- Crowdmapping Digital Social Innovation with Linked data.- Desperately searching for travel offers? Formulate better queries with some help from Linked Data.- Towards the Linked Russian Heritage Cloud: Data enrichment and Publishing.- From Symptoms to Diseases - Creating the Missing Link.- Using semantic web technologies for enterprise architecture analysis.- PADTUN - Using Semantic Technologies in Tunnel Diagnosis and Maintenance Domain.

Mechanised labour and games with a purpose are the two most popular human computation genres, frequently employed to support research activities in fields as diverse as natural language processing, semantic web or databases. Research projects typically rely on either one or the other of these genres, and therefore there is a general lack of understanding of how these two genres compare and whether and how they could be used together to offset their respective weaknesses. This paper addresses these open questions. It first identifies the differences between the two genres, primarily in terms of cost, speed and result quality, based on existing studies in the literature. Secondly, it reports on a comparative study which involves performing the same task through both genres and comparing the results. The study's findings demonstrate that the two genres are highly complementary, which not only makes them suitable for different types of projects, but also opens new opportunities for building cross-genre human computation solutions that exploit the strengths of both genres simultaneously.

Although the field has led to promising early results, the use of crowdsourcing as an integral part of science projects is still regarded with skepticism by some, largely due to a lack of awareness of the opportunities and implications of utilizing these new techniques. We address this lack of awareness, firstly by highlighting the positive impacts that crowdsourcing has had on Natural Language Processing research. Secondly, we discuss the challenges of more complex methodologies, quality control, and the necessity to deal with ethical issues. We conclude with future trends and opportunities of crowdsourcing for science, including its potential for disseminating results, making science more accessible, and enriching educational programs.

With more than 800 million monthly active users, Facebook bears significant potential for science projects. Climate Quiz is an interactive Web application in the tradition of "games with a purpose" that leverages this potential to create metadata through a crowdsourcing-based approach. It presents participants with two types of challenges: (1) selecting the correct relation to connect two environmental concepts, and (2) answering climate-related multiple choice questions. Climate Quiz aims to create shared meaning through collaborative ontology building, a process that captures emergent semantics and elicits formal knowledge in the form of a domain model. As an innovative survey instrument, the application leverages social networking platforms to capture indicators of environmental attitudes, lifestyles and behaviors.

Ontology evaluation refers to the activity of checking the technical quality of an ontology against a frame of reference. As such, it is of core importance for ontology engineering supporting scenarios such as ontology validation, knowledge selection, or the evaluation of knowledge extraction algorithms. In this chapter, we provide methodological guidelines for evaluating stand-alone ontologies as well as ontology networks. Our goal is not only to present the NeOn perspective on this issue but to also provide a practical outlook to the vast area of work in the area of ontology evaluation. Without performing an extensive state-of-the-art analysis of this research field, we aim to illustrate how various evaluation methods developed by the NeOn project, and not only, can be used at different stages of the evaluation process. We conclude the chapter with some concrete examples of performing ontology evaluation.

We investigate the problem of evaluating the correctness of a semantic relation and propose two methods which explore the increasing number of online ontologies as a source of evidence for predicting correctness. We obtain encouraging results, with some of our measures reaching average precision values of 75%.

PowerAqua is a Question Answering system, which takes as input a natural language query and is able to return answers drawn from relevant semantic resources found anywhere on the Semantic Web. In this paper we provide two novel contributions: First, we detail a new component of the system, the Triple Similarity Service, which is able to match queries effectively to triples found in different ontologies on the Semantic Web. Second, we provide a first evaluation of the system, which in addition to providing data about PowerAqua's competence, also gives us important insights into the issues related to using the Semantic Web as the target answer set in Question Answering. In particular, we show that, despite the problems related to the noisy and incomplete conceptualizations, which can be found on the Semantic Web, good results can already be obtained.

The Semantic Web community has expressed its interest on how the Semantic Web technology can be applied more efficiently in a manner that supports real world applications. Additionally, the popular- ity of social tagging systems has demonstrated a clear need for organi- sation and more flexible ways of querying the user contributed content. This work presents FLOR, a folksonomy enrichment algorithm, which exploits a variety of knowledge sources to apply structure on the user tagspaces. In addition, a query mechanism is presented demonstrating how the enriched folksonomies structures can be interrogated by trans- forming the user keyword queries on folksonomies to formal queries on semantic structures. The first prototype of the FLOR enrichment algo- rithm and a first instance of the query mechanism have been implemented and a demonstration is available online

The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e., by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicitly provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape.

Problems with large monolithical ontologies in terms of reusability, scalability and maintenance have led to an increasing interest in modularization techniques for ontologies. Currently, existing work suffers from the fact that the notion of modularization is not as well understood in the context of ontologies as it is in software engineering. In this paper, we experiment on applying state-of-the-art tools for ontology modularization in the context of a concrete application: the automatic selection of knowledge components to be used for Web page annotation and semantic browsing. We conclude that, in a broader context, an evaluation framework is required to guide the choice of a modularization tool, in accordance with the requirements of the considered application.

In this short paper, we examine current Semantic Web application and we highlight what we see as a shift away from first generation Semantic Web applications, towards a new generation of applications, designed to exploit the large amounts of heterogeneous semantic markup, which are increasingly becoming available Our analysis aims both to highlight the main features that can be used to compare and contrast current Semantic Web applications, as well as providing an initial blueprint for characterizing the nature of Semantic Web applications Indeed, our ultimate goal is to specify a number of criteria, which Semantic Web applications ought to satisfy, if we want to move away from conventional semantic systems and develop a new generation of Semantic Web applications, which can succeed in applying semantic technology to the challenging context provided by the World-Wide-Web.

The Semantic Web is an extension of the current World Wide Web, based on the idea of exchanging information with explicit, formal, and machine-accessible descriptions of meaning. Providing information with such semantics will enable the construction of applications that have an increased awareness of what is contained in the information they process and that can therefore operate more accurately. This has the potential of improving the way we deal with information in the broadest sense possible, for example, better search engines, mobile agents for various tasks, or even applications yet unheard of. Rather than being merely a vision, the Semantic Web has significant backing from various institutes such as DARPA, the European Union, and the W3C, all of which have performed a variety of Semantic Web activities. In order to be able to exchange the semantics of information, one first needs to agree on how to explicitly model it. Ontologies are a mechanism for representing such formal and shared domain descriptions. They can be used to annotate data with labels (metadata) indicating their meaning, thereby making their semantics explicit and machine-accessible. Many Semantic Web initiatives emphasize the capability of machines to exchange the meaning of information. Although their efforts will lead to an increased quality of the application's results, their user interfaces often take little or no advantage of the increased semantics. For example, an ontology-based search engine could use its ontology to enrich the presentation of the resulting list to the end user, for example, by replacing the endless list of hits with a navigation structure based on the semantics of the hits. Visualization is becoming increasingly important in Semantic Web tools. In par-ticular, visualization is used in tools that support the development of ontologies, such as ontology extraction tools (OntoLift, Text-to-Onto) or ontology editors (Protégé, OntoLift). The intended users of these tools are ontology engineers that need to gain an insight into the complexity of the ontology. Therefore, these tools employ schema visualization techniques that primarily focus on the structure of the ontology (i.e., its concepts and their relationships). We presented a detailed overview of these tools in Fluit et al. (2003). The Cluster Map visualization technique, developed by the Dutch software vendor Aduna (http://aduna.biz), bridges the gap between complex semantic structures and 45 46 Visualizing the Semantic Web their simple, intuitive user-oriented presentation. It presents semantic data to end users who want to leverage the benefits of Semantic Web technology without being burdened with the complexity of the underlying metadata. For end users, instance information is often more important than the structure of the ontology that is used to describe these instances. Accordingly, the Cluster Map technique focuses on visualizing instances and their classifications according to the concepts of the ontology. We have reported in previous work (Fluit et al., 2002; 2003) on case studies that exploit the expressive power of this technique. Since then, the growth of the Semantic Web has made it possible to take this technology a step further and integrate it in three different applications. Two of them are employed within Semantic Web research projects. The third is a commercial information retrieval application. These appli-cations exhibit the characteristics of a typical Semantic Web tool: they provide easy (visual) access to a set of heterogeneous, distributed data sources and rely on Semantic Web encoding languages and storage facilities for the manipulation of the visualized data. This chapter is centered on the description of these three applications. First, we will explain the contents of the Cluster Map visualization and the kind of ontologies it visualizes in Section 3.2. Section 3.3 presents the three real-life applications that incorporate the visualization. These two sections lead to a discussion in Section 3.4 on how the visualization can support several user tasks, such as analysis, search, and exploration. Some considerations for future work and a summary conclude this chapter. 3.2 Cluster Map Basics

High quality domain ontologies are essential for successful employment of semantic Web services. However, their acquisition is difficult and costly, thus hampering the development of this field. In this paper we report on the first stage of research that aims to develop (semi-)automatic ontology learning tools in the context of Web services that can support domain experts in the ontology building task. The goal of this first stage was to get a better understanding of the problem at hand and to determine which techniques might be feasible to use. To this end, we developed a framework for (semi-)automatic ontology learning from textual sources attached to Web services. The framework exploits the fact that these sources are expressed in a specific sublanguage, making them amenable to automatic analysis. We implement two methods in this framework, which differ in the complexity of the employed linguistic analysis. We evaluate the methods in two different domains, verifying the quality of the extracted ontologies against high quality hand-built ontologies of these domains. Our evaluation lead to a set of valuable conclusions on which further work can be based. First, it appears that our method, while tailored for the Web services context, might be applicable across different domains. Second, we concluded that deeper linguistic analysis is likely to lead to better results. Finally, the evaluation metrics indicate that good results can be achieved using only relatively simple, off the shelf techniques. Indeed, the novelty of our work is not in the used natural language processing methods but rather in the way they are put together in a generic framework specialized for the context of Web services.

Service interface description languages such as WSDL, and related standards, are evolving rapidly to provide a foundation for interoperation between Web services. At the same time, Semantic Web service technologies, such as the Ontology Web Language for Services (OWL-S), are developing the means by which services can be given richer semantic specifications. Richer semantics can enable fuller, more flexible automation of service provision and use, and support the construction of more powerful tools and methodologies. Both sets of technologies can benefit from complementary uses and cross-fertilization of ideas. This paper shows how to use OWL-S in conjunction with Web service standards, and explains and illustrates the value added by the semantics expressed in OWL-S.

Web service composition can provide a value-chain between customers and suppliers. The increasing number of services, and thus possible combinations, demands the development of dynamic and automatic techniques for their composition. Current commercial solutions are limited and are primarily static and manual. Automation requires reasoning about (semantic descriptions of) the services. This paper describes our initial work which brings together agents, Web service and semantic Web technology. Our knowledge-based software engineering approach to the design of agents, known as the Agent Factory, is applied to the composition of Web services. Using semantic descriptions of Web services written in DAML-S, the design process in our Agent Factory derives a Web service configuration. This paper also includes some observations regarding our experiences with DAML-S, UDDI and WSDL for this purpose.

Easy integration of available tools will be a key success factor for the future of the Semantic Web. We envision that semantic descriptions of the tools themselves will play an important part in addressing this issue. Motivated by this vision we used DAML-S (a key initiative to support automated management of Web services) to describe Sesame (an RDF(S) storage and query engine). This paper discusses the major problems that we encountered as well as suggested solutions. This work is relevant to other Semantic Web research groups who wish to annotate their tools or to use annotated tools. Also, we hope to offer helpful input to the DAML-S coalition in the further development of their standard.

Describing software entities using Semantic Web technology is a growing research area. Our work investigates the semantic description of software entities that provide an application programmer’s interface (API) to allow reasoning with the descriptions and their interrelationships. We present an ontology for our Application Server for the Semantic Web [3], where it is used to facilitate implementation tasks and semantic discovery. Building on an emerging standard from the Semantic Web community, our work includes a number of extensions to DAML-S [1] which currently allows semantic description of a particular type of software entities, viz. web-services, in order to facilitate their automatic discovery and integration.

Evaluations of semantic search systems are generally small scale and ad hoc due to the lack of appropriate resources such as test collections, agreed performance criteria and independent judgements of performance. By analysing our work in building and evaluating semantic tools over the last five years, we conclude that the growth of the semantic web led to an improvement in the available resources and the consequent robustness of performance assessments. We propose two directions for continuing evaluation work: the development of extensible evaluation benchmarks and the use of logging parameters for evaluating individual components of search systems.