Sahar Vahdati

Semantic Publishing Challenge – Assessing the Quality of Scientific Output by Information Extraction and Interlinking

The Semantic Publishing Challenge series aims at investigating novel approaches for improving scholarly publishing using Linked Data technology. In 2014 we had bootstrapped this effort with a focus on extracting information from non-semantic publications - computer science workshop proceedings volumes and their papers - to assess their quality. The objective of this second edition was to improve information extraction but also to interlink the 2014 dataset with related ones in the LOD Cloud, thus paving the way for sophisticated end-user services.

Mapping Large Scale Research Metadata to Linked Data: A Performance Comparison of HBase, CSV and XML

OpenAIRE, the Open Access Infrastructure for Research in Europe, comprises a database of all EC FP7 and H2020 funded research projects, including metadata of their results (publications and datasets). These data are stored in an HBase NoSQL database, post-processed, and exposed as HTML for human consumption, and as XML through a web service interface. As an intermediate format to facilitate statistical computations, CSV is generated internally. To interlink the OpenAIRE data with related data on the Web, we aim at exporting them as Linked Open Data (LOD). The LOD export is required to integrate into the overall data processing workflow, where derived data are regenerated from the base data every day. We thus faced the challenge of identifying the best-performing conversion approach. We evaluated the performances of creating LOD by a MapReduce job on top of HBase, by mapping the intermediate CSV files, and by mapping the XML output.

OpenResearch: Collaborative Management of Scholarly Communication Metadata

Scholars often need to search for matching, high-profile scientific events to publish their research results. Information about topical focus and quality of events is not made sufficiently explicit in the existing communication channels where events are announced. Therefore, scholars have to spend a lot of time on reading and assessing calls for papers but might still not find the right event. Additionally, events might be overlooked because of the large number of events announced every day. We introduce OpenResearch, a crowd sourcing platform that supports researchers in collecting, organizing, sharing and disseminating information about scientific events in a structured way. It enables quality-related queries over a multidisciplinary collection of events according to a broad range of criteria such as acceptance rate, sustainability of event series, and reputation of people and organizations. Events are represented in different views using map extensions, calendar and time-line visualizations. We have systematically evaluated the timeliness, usability and performance of OpenResearch.

OpenAIRE LOD Services: Scholarly Communication Data as Linked Data

OpenAIRE, the Open Access Infrastructure for Research in Europe, enables search, discovery and monitoring of publications and datasets from more than 100,000 research projects. Increasing the reusability of the OpenAIRE research metadata, connecting it to other open data about projects, publications, people and organizations, and reaching out to further related domains requires better technical interoperability, which we aim at achieving by exposing the OpenAIRE Information Space as Linked Data. We present a scalable and maintainable architecture that converts the OpenAIRE data from its original HBase NoSQL source to RDF. We furthermore explore how this novel integration of data about research can facilitate scholarly communication.

Towards a Knowledge Graph Representing Research Findings by Semantifying Survey Articles

Despite significant advances in technology, the way how research is done and especially communicated has not changed much. We have the vision that ultimately researchers will work on a common knowledge base comprising comprehensive descriptions of their research, thus making research contributions transparent and comparable. The current approach for structuring, systematizing and comparing research results is via survey or review articles. In this article, we describe how surveys for research fields can be represented in a semantic way, resulting in a knowledge graph that describes the individual research problems, approaches, implementations and evaluations in a structured and comparable way. We present a comprehensive ontology for capturing the content of survey articles. We discuss possible applications and present an evaluation of our approach with the retrospective, exemplary semantification of a survey. We demonstrate the utility of the resulting knowledge graph by using it to answer queries about the different research contributions covered by the survey and evaluate how well the query answers serve readers’ information needs, in comparison to having them extract the same information from reading a survey paper.

OpenCourseWare observatory: does the quality of OpenCourseWare live up to its promise?

A vast amount of OpenCourseWare (OCW) is meanwhile being published online to make educational content accessible to larger audiences. The awareness of such courses among users and the popularity of systems providing such courses are increasing. However, from a subjective experience, OCW is frequently cursory, outdated or non-reusable. In order to obtain a better understanding of the quality of OCW, we assess the quality in terms of fitness for use. Based on three OCW use case scenarios, we define a range of dimensions according to which the quality of courses can be measured. From the definition of each dimension a comprehensive list of quality metrics is derived. In order to obtain a representative overview of the quality of OCW, we performed a quality assessment on a set of 100 randomly selected courses obtained from 20 different OCW repositories. Based on this assessment we identify crucial areas in which OCW needs to improve in order to deliver up to its promises.

Analysing Scholarly Communication Metadata of Computer Science Events.

Over the past 30 years we have observed the impact of the ubiquitous availability of the Internet, email, and web-based services on scholarly communication. The preparation of manuscripts as well as the organisation of conferences, from submission to peer review to publication, have become considerably easier and efficient. A key question now is what were the measurable effects on scholarly communication in computer science? Of particular interest are the following questions: Did the number of submissions to conferences increase? How did the selection processes change? Is there a proliferation of publications? We shed light on some of these questions by analysing comprehensive scholarly communication metadata from a large number of computer science conferences of the last 30 years. Our transferable analysis methodology is based on descriptive statistics analysis as well as exploratory data analysis and uses crowd-sourced, semantically represented scholarly communication metadata from OpenResearch.org.

Semantic Publishing Challenge: Bootstrapping a Value Chain for Scientific Data

The objective of the Semantic Publishing (SemPub) challenge series is to bootstrap a value chain for scientific data to enable services, such as assessing the quality of scientific output with respect to novel metrics. The key idea was to involve participants in extracting data from heterogeneous resources and producing datasets on scholarly publications, which can be exploited by the community itself. Differently from other challenges in the semantic publishing domain, whose focus is on exploiting semantically enriched data, SemPub focuses on producing Linked Open Datasets. The goal of this paper is to review both (i) the overall organization of the Challenge, and (ii) the results that the participants have produced in the first two challenges of 2014 and 2015 – in terms of data, ontological models and tools – in order to better shape future editions of the challenge, and to better serve the needs of the semantic publishing community.

Exploiting Interlinked Research Metadata

OpenAIRE, the Open Access Infrastructure for Research in Europe, aggregates metadata about research (projects, publications, people, organizations, etc.) into a central Information Space. OpenAIRE aims at increasing interoperability and reusability of this data collection by exposing it as Linked Open Data (LOD). By following the LOD principles, it is now possible to further increase interoperability and reusability by connecting the OpenAIRE LOD to other datasets about projects, publications, people and organizations. Doing so required us to identify link discovery tools that perform well, as well as candidate datasets that provide comprehensive scholarly communication metadata, and then to specify linking rules. We demonstrate the added value that interlinking provides for end users by implementing visual frontends for looking up publications to cite, and publication statistics, and evaluating their usability on top of interlinked vs. non-interlinked data.

Semantic Publishing Challenge – Assessing the Quality of Scientific Output in Its Ecosystem

The Semantic Publishing Challenge aims to involve participants in extracting data from heterogeneous sources on scholarly publications, and produce Linked Data which can be exploited by the community itself. The 2014 edition was the first attempt to organize a challenge to enable the assessment of the quality of scientific output. The 2015 edition was more explicit regarding the potential techniques, i.e., information extraction and interlinking. The current 2016 edition focuses on the multiple dimensions of scientific quality and the great potential impact of producing Linked Data for this purpose. In this paper, we discuss the overall structure of the Semantic Publishing Challenge, as it is for the 2016 edition, as well as the submitted solutions and their evaluation.