Researcher Or Crowd Member? Why Not Both! the Open Research Knowledge Graph for Applying and Communicating Crowdre Research
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Researcher or Crowd Member? Why not both! The Open Research Knowledge Graph for Applying and Communicating CrowdRE Research Oliver Karras∗, Eduard C. Groenyz, Javed Ali Khanx, Sören Auer∗ ∗Leibniz Information Centre for Science and Technology, Germany, {oliver.karras, soeren.auer}@tib.eu yFraunhofer IESE, Germany, [email protected] zDepartment of Information and Computing Sciences, Utrecht University, Netherlands xDepartment of Software Engineering, University of Science and Technology Bannu, Pakistan, [email protected] Abstract—In recent decades, there has been a major shift while this representation is easy for humans to process, it is towards improved digital access to scholarly works. However, poorly interlinked and not machine-actionable [3]. The next step even now that these works are available in digital form, they in the digital transformation of scholarly knowledge requires remain document-based, making it difficult to communicate the knowledge they contain. The next logical step is to extend a more flexible, fine-grained, semantic, and context-sensitive these works with more flexible, fine-grained, semantic, and representation that allows humans to quickly compare research context-sensitive representations of scholarly knowledge. The results, and machines to process them. This representation can Open Research Knowledge Graph (ORKG) is a platform that hardly be created automatically, but requires domain experts [1] structures and interlinks scholarly knowledge, relying on crowd- and an infrastructure for acquiring, curating, publishing, and sourced contributions from researchers (as a crowd) to acquire, curate, publish, and process this knowledge. In this experience processing scholarly knowledge [3]. report, we consider the ORKG in the context of Crowd-based Numerous projects [6]–[13] provide corresponding solutions Requirements Engineering (CrowdRE) from two perspectives: using knowledge graphs (see Section II) as structured, inter- (1) As CrowdRE researchers, we investigate how the ORKG linked, and semantically rich representations of knowledge. practically applies CrowdRE techniques to involve scholars in While established knowledge graphs exist for representing its development to make it align better with their academic work. We determined that the ORKG readily provides social and encyclopedic and factual knowledge, e.g., in DBpedia [14] and financial incentives, feedback elicitation channels, and support WikiData [15], the use of knowledge graphs for scholarly for context and usage monitoring, but that there is improvement knowledge is a rather new approach [2]. One of these potential regarding automated user feedback analyses and a projects is developing the Open Research Knowledge Graph1 holistic CrowdRE approach. (2) As crowd members, we explore (ORKG) [13]. The ORKG is a platform that uses crowdsourcing how the ORKG can be used to communicate scholarly knowl- edge about CrowdRE research. For this purpose, we curated to acquire and curate scholarly knowledge. The project explores qualitative and quantitative scholarly knowledge in the ORKG how scholarly knowledge can be acquired along the research based on papers contained in two previously published systematic lifecycle, relying on the manual acquisition and curation of literature reviews (SLRs) on CrowdRE. This knowledge can scholarly knowledge through crowdsourced contributions from be explored and compared interactively, and with more data experts [1], [3], [13]. Researchers from various research fields than what the SLRs originally contained. Therefore, the ORKG improves access and communication of the scholarly knowledge form a crowd that acquires, curates, publishes, and processes about CrowdRE research. For both perspectives, we found the scholarly knowledge. Based on the publicly available beta ORKG to be a useful multi-tool for CrowdRE research. version of the ORKG1, the ORKG project team has two long- arXiv:2108.05085v1 [cs.DL] 11 Aug 2021 Index Terms—Crowd, crowd-based requirements engineering, term goals. First, they aim to integrate more strategies for crowdsourcing, knowledge graph, open research crowdsourcing to enable crowd members to contribute their research results to the ORKG in a more flexible and lightweight I. INTRODUCTION manner [13, p. 9]. Second, the team aims to tailor the platform Historically, research results were published in collections of to the needs and requirements of the expert crowd by involving printed works, such as journals [1]. Studying the literature on the crowd members in the development and soliciting feedback a particular topic required hours or days of browsing through a from them on problems and features [13, p. 5]. Although library’s collection. In recent decades, the research community these goals align with CrowdRE, the ORKG project team has has made great efforts to improve access to scholarly knowledge not yet consciously applied CrowdRE. This fact aroused our as part of the digital transformation [2], [3]. Digitizing interest to research the implementation of CrowdRE in the real articles was the first step in this transformation. However, development setting of the ORKG platform. In this experience even digitized articles are just digital representatives of their report, we address two research questions: printed counterparts [4]. For this reason, digital articles impede scholarly communication by still being document-based [5]; 1http://orkg.org/ RQ1: What potential does the ORKG have as a platform The organization of scholarly communication and knowledge for applying CrowdRE research in a real development based on the structured, standardized, and semantic represen- setting? tation form of a knowledge graph offers several benefits [20]. It increases the unique identification of the relevant artifacts, RQ2: What potential does the ORKG have as a platform concepts, attributes, and relationships of research results. All for communicating scholarly knowledge about CrowdRE these elements can be linked with each other, considering the research? constraints imposed by the underlying ontology. It increases Regarding RQ1, we take the perspective of CrowdRE traceability through improved and explicit linking of the researchers. We describe the current state and features of artifacts and information sources. In this way, graph-based the ORKG as a crowdsourcing platform that involves its crowd knowledge sharing also helps to reduce redundancy and members in the development along the four key activities duplication because repetitive content, such as related work on a of CrowdRE (motivating crowd members, eliciting feedback, topic, can be continuously described, stored, and communicated analyzing feedback, monitoring context & usage data) [16]. over time. This type of scholarly communication curbs the This overview shows to what extent and how the ORKG already proliferation of scientific publications and leads to an increase addresses CrowdRE, and highlights improvement potential. in efficiency by avoiding media discontinuities in the various CrowdRE researchers benefit from this overview as it enables phases of scientific work. It reduces ambiguity through more them to assess the suitability of the ORKG as a basis for their fu- terminological and conceptual clarity because the concepts and ture work and studies on CrowdRE research, while the efficacy relationships can be reused across disciplinary boundaries. It of CrowdRE in a real development setting can be demonstrated increases machine actionability on the content of scientific to practitioners. Regarding RQ2, we take the perspective of publications, enabling machines to understand the structure crowd members. We provide insights into our experiences with and semantics of the content of scientific publications. Finally, acquiring, curating, and publishing qualitative and quantitative because the content is machine-actionable, it increases develop- scholarly knowledge about CrowdRE research in the ORKG. ment opportunities for applications that provide search, retrieval, Using two examples from CrowdRE research [17], [18], we mining, and assistance features for scholarly knowledge. These illustrate how (CrowdRE) researchers can use the ORKG to applications could support open science by making knowledge improve access and communication of scholarly knowledge more accessible, e.g., to early career scientists, lay people, or about their research. Based on the two perspectives, we assess researchers with a visual impairment. the potential of the ORKG as a platform for applying and communicating CrowdRE research. We gained the following III. RELATED WORK insights: In recent years, the role of a crowd has attracted much attention across many disciplines through means such as The ORKG has a two-fold potential for CrowdRE: crowdsourcing. Numerous projects have emerged offering (1) The platform and its features provide a solid basis for platforms that involve a crowd as an inherent part of their applying CrowdRE research in a real development setting systems [21]–[23]. The digital transformation has compelled and in close collaboration with the ORKG project team. the domain of requirements engineering (RE) to respond in (2) The platform enables the communication of CrowdRE various ways, including placing greater emphasis on automated research by allowing more comprehensive curation of RE [24]. A particular kind of data-driven RE [25], [26] relies on scholarly knowledge than document-based works do. the involvement of a crowd as a source of data, and is typically