Linked Data Quality Assessment and its Application to Societal Progress Measurement

In recent years, the Linked Data (LD) paradigm has emerged as a simple mechanism for employing the Web as a medium for data and knowledge integration where both documents and data are linked. Moreover, the semantics and structure of the underlying data are kept intact, making this the Semantic Web. LD essentially entails a set of best practices for publishing and connecting structure data on the Web, which allows publish- ing and exchanging information in an interoperable and reusable fashion. Many different communities on the Internet such as geographic, media, life sciences and government have already adopted these LD principles. This is confirmed by the dramatically growing Linked Data Web, where currently more than 50 billion facts are represented. With the emergence of Web of Linked Data, there are several use cases, which are possible due to the rich and disparate data integrated into one global information space. Linked Data, in these cases, not only assists in building mashups by interlinking heterogeneous and dispersed data from multiple sources but also empowers the uncovering of meaningful and impactful relationships. These discoveries have paved the way for scientists to explore the existing data and uncover meaningful outcomes that they might not have been aware of previously. In all these use cases utilizing LD, one crippling problem is the underlying data quality. Incomplete, inconsistent or inaccurate data affects the end results gravely, thus making them unreliable. Data quality is commonly conceived as fitness for use, be it for a certain application or use case. There are cases when datasets that contain quality problems, are useful for certain applications, thus depending on the use case at hand. Thus, LD consumption has to deal with the problem of getting the data into a state in which it can be exploited for real use cases. The insufficient data quality can be caused either by the LD publication process or is intrinsic to the data source itself. A key challenge is to assess the quality of datasets published on the Web and make this quality information explicit. Assessing data quality is particularly a challenge in LD as the underlying data stems from a set of multiple, autonomous and evolving data sources. Moreover, the dynamic nature of LD makes assessing the quality crucial to measure the accuracy of representing the real-world data. On the document Web, data quality can only be indirectly or vaguely defined, but there is a requirement for more concrete and measurable data quality metrics for LD. Such data quality metrics include correctness of facts wrt. the real-world, adequacy of semantic representation, quality of interlinks, interoperability, timeliness or consistency with regard to implicit information. Even though data quality is an important concept in LD, there are few methodologies proposed to assess the quality of these datasets. Thus, in this thesis, we first unify 18 data quality dimensions and provide a total of 69 metrics for assessment of LD. The first methodology includes the employment of LD experts for the assessment. This assessment is performed with the help of the TripleCheckMate tool, which was developed specifically to assist LD experts for assessing the quality of a dataset, in this case DBpedia. The second methodology is a semi-automatic process, in which the first phase involves the detection of common quality problems by the automatic creation of an extended schema for DBpedia. The second phase involves the manual verification of the generated schema axioms. Thereafter, we employ the wisdom of the crowds i.e. workers for online crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) to assess the quality of DBpedia. We then compare the two approaches (previous assessment by LD experts and assessment by MTurk workers in this study) in order to measure the feasibility of each type of the user-driven data quality assessment methodology. Additionally, we evaluate another semi-automated methodology for LD quality assessment, which also involves human judgement. In this semi-automated methodology, selected metrics are formally defined and implemented as part of a tool, namely R2RLint. The user is not only provided the results of the assessment but also specific entities that cause the errors, which help users understand the quality issues and thus can fix them. Finally, we take into account a domain-specific use case that consumes LD and leverages on data quality. In particular, we identify four LD sources, assess their quality using the R2RLint tool and then utilize them in building the Health Economic Research (HER) Observatory. We show the advantages of this semi-automated assessment over the other types of quality assessment methodologies discussed earlier. The Observatory aims at evaluating the impact of research development on the economic and healthcare performance of each country per year. We illustrate the usefulness of LD in this use case and the importance of quality assessment for any data analysis

Ontology Mapping for Life Science Linked Data

Abstract. Bio2RDF is an open-source project that offers a large and connected knowledge graph of Life Science Linked Data. Each dataset is expressed using its own vocabulary, thereby hindering integration, search, query, and browse data across similar or identical types of data. With growth and content changes in source data, a manual approach to maintain mappings has proven untenable. The aim of this work is to develop a (semi)automated procedure to generate high quality mappings between Bio2RDF and SIO using BioPortal ontologies. Our preliminary results demonstrate that our approach is promising in that it can find new mappings using a transitive closure between ontology mappings. Further development of the methodology coupled with improvements in the ontology will offer a better-integrated view of the Life Science Linked Data. Keywords: biomedical data, life science, ontology, integration, semantic web, linked data, RDF, Bio2RDF, SIO Life Science Data Integration The life sciences have a long history of producing open data. Unfortunately, these data are represented using a wide variety of incompatible formats (e.g. CSV, XML, custom flat files etc.). Linked Data (LD) offers a new paradigm of using community standards to represent and provide a data in a uniform and semantic manne

Venturing the Definition of Green Energy Transition:A systematic literature review

The issue of climate change has become increasingly noteworthy in the past years, the transition towards a renewable energy system is a priority in the transition to a sustainable society. In this document, we explore the definition of green energy transition, how it is reached, and what are the driven factors to achieve it. To answer that firstly, we have conducted a literature review discovering definitions from different disciplines, secondly, gathering the key factors that are drivers for energy transition, finally, an analysis of the factors is conducted within the context of European Union data. Preliminary results have shown that household net income and governmental legal actions related to environmental issues are potential candidates to predict energy transition within countries. With this research, we intend to spark new research directions in order to get a common social and scientific understanding of green energy transition

CrowdED: Guideline for Optimal Crowdsourcing Experimental Design

Crowdsourcing involves the creating of HITs (Human Intelligent Tasks), submitting them to a crowdsourcing platform and providing a monetary reward for each HIT. One of the advantages of using crowdsourcing is that the tasks can be highly parallelized, that is, the work is performed by a high number of workers in a decentralized setting. The design also offers a means to cross-check the accuracy of the answers by assigning each task to more than one person and thus relying on majority consensus as well as reward the workers according to their performance and productivity. Since each worker is paid per task, the costs can significantly increase, irrespective of the overall accuracy of the results. Thus, one important question when designing such crowdsourcing tasks that arise is how many workers to employ and how many tasks to assign to each worker when dealing with large amounts of tasks. That is, the main research questions we aim to answer is: 'Can we a-priori estimate optimal workers and tasks' assignment to obtain maximum accuracy on all tasks?'. Thus, we introduce a two-staged statistical guideline, CrowdED, for optimal crowdsourcing experimental design in order to a-priori estimate optimal workers and tasks' assignment to obtain maximum accuracy on all tasks. We describe the algorithm and present preliminary results and discussions. We implement the algorithm in Python and make it openly available on Github, provide a Jupyter Notebook and a R Shiny app for users to re-use, interact and apply in their own crowdsourcing experiments

TripleCheckMate: A Tool for Crowdsourcing the Quality Assessment of Linked Data

Linked Open Data (LOD) comprises of an unprecedented volume of structured datasets on the Web. However, these datasets are of varying quality ranging from extensively curated datasets to crowdsourced and even extracted data of relatively low quality. We present a methodology for assessing the quality of linked data resources, which comprises of a manual and a semi-automatic process. In this paper we focus on the manual process where the first phase includes the detection of common quality problems and their representation in a quality problem taxonomy. The second phase comprises of the evaluation of a large number of individual resources, according to the quality problem taxonomy via crowdsourcing. This process is implemented by the tool TripleCheckMate wherein a user assesses an individual resource and evaluates each fact for correctness. This paper focuses on describing the methodology, quality taxonomy and the tools’ system architecture, user perspective and extensibility

Center of Excellence in Research Reporting in Neurosurgery - Diagnostic Ontology

Motivation: Evidence-based medicine (EBM), in the field of neurosurgery, relies on diagnostic studies since Randomized Controlled Trials (RCTs) are uncommon. However, diagnostic study reporting is less standardized which increases the difficulty in reliably aggregating results. Although there have been several initiatives to standardize reporting, they have shown to be sub-optimal. Additionally, there is no central repository for storing and retrieving related articles. Results: In our approach we formulate a computational diagnostic ontology containing 91 elements, including classes and sub-classes, which are required to conduct Systematic Reviews - Meta Analysis (SR-MA) for diagnostic studies, which will assist in standardized reporting of diagnostic articles. SR-MA are studies that aggregate several studies to come to one conclusion for a particular research question. We also report high percentage of agreement among five observers as a result of the interobserver agreement test that we conducted among them to annotate 13 articles using the diagnostic ontology. Moreover, we extend our existing repository CERR-N to include diagnostic studies. Availability: The ontology is available for download as an.owl file at: http://bioportal.bioontology.org/ontologies/3013