Elena Simperl

Reusing ontologies on the Semantic Web: A feasibility study

Technologies for the efficient and effective reuse of ontological knowledge are one of the key success factors for the Semantic Web. Putting aside matters of cost or quality, being reusable is an intrinsic property of ontologies, originally conceived of as a means to enable and enhance the interoperability between computing applications. This article gives an account, based on empirical evidence and real-world findings, of the methodologies, methods and tools currently used to perform ontology-reuse processes. We study the most prominent case studies on ontology reuse, published in the knowledge-/ontology-engineering literature from the early nineties. This overview is complemented by two self-conducted case studies in the areas of eHealth and eRecruitment in which we developed Semantic Web ontologies for different scopes and purposes by resorting to existing ontological knowledge on the Web. Based on the analysis of the case studies, we are able to identify a series of research and development challenges which should be addressed to ensure reuse becomes a feasible alternative to other ontology-engineering strategies such as development from scratch. In particular, we emphasize the need for a context- and task-sensitive treatment of ontologies, both from an engineering and a usage perspective, and identify the typical phases of reuse processes which could profit considerably from such an approach. Further on, we argue for the need for ontology-reuse methodologies which optimally exploit human and computational intelligence to effectively operationalize reuse processes.

CrowdMap: crowdsourcing ontology alignment with microtasks

The last decade of research in ontology alignment has brought a variety of computational techniques to discover correspondences between ontologies. While the accuracy of automatic approaches has continuously improved, human contributions remain a key ingredient of the process: this input serves as a valuable source of domain knowledge that is used to train the algorithms and to validate and augment automatically computed alignments. In this paper, we introduce CrowdMap, a model to acquire such human contributions via microtask crowdsourcing. For a given pair of ontologies, CrowdMap translates the alignment problem into microtasks that address individual alignment questions, publishes the microtasks on an online labor market, and evaluates the quality of the results obtained from the crowd. We evaluated the current implementation of CrowdMap in a series of experiments using ontologies and reference alignments from the Ontology Alignment Evaluation Initiative and the crowdsourcing platform CrowdFlower. The experiments clearly demonstrated that the overall approach is feasible, and can improve the accuracy of existing ontology alignment solutions in a fast, scalable, and cost-effective manner.

Human Intelligence in the Process of Semantic Content Creation

Despite significant progress over the last years the large-scale adoption of semantic technologies is still to come. One of the reasons for this state of affairs is assumed to be the lack of useful semantic content, a prerequisite for almost every IT system or application using semantics. Through its very nature, this content can not be created fully automatically, but requires, to a certain degree, human contribution. The interest of Internet users in semantics, and in particular in creating semantic content, is, however, low. This is understandable if we think of several characteristics exposed by many of the most prominent semantic technologies, and the applications thereof. One of these characteristics is the high barrier of entry imposed. Interacting with semantic technologies today requires specific skills and expertise on subjects which are not part of the mainstream IT knowledge portfolio. A second characteristic are the incentives that are largely missing in the design of most semantic applications. The benefits of using machine-understandable content are in most applications fully decoupled from the effort of creating and maintaining this content. In other words, users do not have a motivation to contribute to the process. Initiatives in the areas of the Social Semantic Web acknowledged this problem, and identified mechanisms to motivate users to dedicate more of their time and resources to participate in the semantic content creation process. Still, even if incentives are theoretically in place, available human labor is limited and must only be used for those tasks that are heavily dependent on human intervention, and cannot be reliably automated. In this article, we concentrate on this step in between. As a first contribution, we analyze the process of semantic content creation in order to identify those tasks that are inherently human-driven. When building semantic applications involving these specific tasks, one has to install incentive schemes that are likely to encourage users to perform exactly these tasks that crucially rely on manual input. As a second contribution of the article, we propose incentives or incentive-driven tools that can be used to increase user interest in semantic content creation tasks. We hope that our findings will be adopted as recommendations for establishing a fundamentally new form of design of semantic applications by the semantic technologies community.

Crowdsourcing Linked Data Quality Assessment

In this paper we look into the use of crowdsourcing as a means to handle Linked Data quality problems that are challenging to be solved automatically. We analyzed the most common errors encountered in Linked Data sources and classified them according to the extent to which they are likely to be amenable to a specific form of crowdsourcing. Based on this analysis, we implemented a quality assessment methodology for Linked Data that leverages the wisdom of the crowds in different ways: (i) a contest targeting an expert crowd of researchers and Linked Data enthusiasts; complemented by (ii) paid microtasks published on Amazon Mechanical Turk.We empirically evaluated how this methodology could efficiently spot quality issues in DBpedia. We also investigated how the contributions of the two types of crowds could be optimally integrated into Linked Data curation processes. The results show that the two styles of crowdsourcing are complementary and that crowdsourcing-enabled quality assessment is a promising and affordable way to enhance the quality of Linked Data.

Ontology engineering: a reality check

The theoretical results achieved in the ontology engineering field in the last fifteen years are of incontestable value for the prospected large scale take-up of semantic technologies Their range of application in real-world projects is, however, so far comparatively limited, despite the growing number of ontologies online available This restricted impact was confirmed in a three month empirical study, in which we examined over 34 contemporary ontology development projects from a process- and costs-oriented perspective In this paper we give an account of the results of this study We conclude that ontology engineering research should strive for a unified, lightweight and component-based methodological framework, principally targeted at domain experts, in addition to consolidating the existing approaches.

Argumentation-Based Ontology Engineering

The Diligent argumentation framework helps capture design deliberations in ontology-engineering discussions. It makes consensus-building tasks more efficient and provides detailed guidance for nonexperts. The semantic Web envisions an infrastructure in which humans and machines seamlessly exchange information on the Web. During the discussions participants raise new issues or elaborate on existing ones. Once a discussion evolves, participants can group issues and act on them accordingly.

ONTOCOM: a cost estimation model for ontology engineering

The technical challenges associated with the development and deployment of ontologies have been subject to a considerable number of research initiatives since the beginning of the nineties. The economical aspects of these processes are, however, still poorly exploited, impeding the dissemination of ontology-driven technologies beyond the boundaries of the academic community. This paper aims at contributing to the alleviation of this situation by proposing ONTOCOM (Ontology Cost Model), a model to predict the costs arising in ontology engineering processes. We introduce a methodology to generate a cost model adapted to a particular ontology development strategy, and an inventory of cost drivers which influence the amount of effort invested in activities performed during an ontology life cycle. We further present the results of the model validation procedure, which covered an expert-driven evaluation and a statistical calibration on 36 data points collected from real-world projects. The validation revealed that ontology engineering processes have a high learning rate, indicating that the building of very large ontologies is feasible from an economic point of view. Moreover, the complexity of ontology evaluation, domain analysis and conceptualization activities proved to have a major impact on the final ontology engineering process duration.

Labels in the web of data

Entities on theWeb of Data need to have labels in order to be exposable to humans in a meaningful way. These labels can then be used for exploring the data, i.e., for displaying the entities in a linked data browser or other front-end applications, but also to support keywordbased or natural-language based search over the Web of Data. Far too many applications fall back to exposing the URIs of the entities to the user in the absence of more easily understandable representations such as human-readable labels. In this work we introduce a number of labelrelated metrics: completeness of the labeling, the efficient accessibility of the labels, unambiguity of labeling, and the multilinguality of the labeling. We report our findings from measuring the Web of Data using these metrics. We also investigate which properties are used for labeling purposes, since many vocabularies define further labeling properties beyond the standard property from RDFS.

Semantic Web Services

A paradigm shift is taking place in computer science: one generation ago, we learned to abstract from hardware to software, now we are abstracting from software to serviceware implemented through service-oriented computing. Yet ensuring interoperability in open, heterogeneous, and dynamically changing environments, such as the Internet, remains a major challenge for actual machine-to-machine integration. Usually significant problems in aligning data, processes, and protocols appear as soon as a specific piece of functionality is used within a different application context. The Semantic Web Services (SWS) approach is about describing services with metadata on the basis of domain ontologies as a means to enable their automatic location, execution, combination, and use. Fensel and his coauthors provide a comprehensive overview of SWS in line with actual industrial practice. They introduce the main sociotechnological components that ground the SWS vision (like Web Science, Service Science, and service-oriented architectures) and several approaches that realize it, e.g. the Web Service Modeling Framework, OWL-S, and RESTful services. The real-world relevance is emphasized through a series of case studies from large-scale R&D projects and a business-oriented proposition from the SWS technology provider Seekda.Each chapter of the book is structured according to a predefined template, covering both theoretical and practical aspects, and including walk-through examples and hands-on exercises. Additional learning material is available on the book website www.swsbook.org. With its additional features, the book is ideally suited as the basis for courses or self-study in this field, and it may also serve as a reference for researchers looking for a state-of-the-art overview of formalisms, methods, tools, and applications related to SWS.

Designing for Citizen Data Analysis: A Cross-Sectional Case Study of a Multi-Domain Citizen Science Platform

Designing an effective and sustainable citizen science (CS)project requires consideration of a great number of factors. This makes the overall process unpredictable, even when a sound, user-centred design approach is followed by an experienced team of UX designers. Moreover, when such systems are deployed, the complexity of the resulting interactions challenges any attempt to generalisation from retrospective analysis. In this paper, we present a case study of the largest single platform of citizen driven data analysis projects to date, the Zooniverse. By eliciting, through structured reflection, experiences of core members of its design team, our grounded analysis yielded four sets of themes, focusing on Task Specificity, Community Development, Task Design and Public Relations and Engagement, supported by two-to-four specific design claims each. For each, we propose a set of design claims (DCs), drawing comparisons to the literature on crowdsourcing and online communities to contextualise our findings.