New trends in scientific knowledge graphs and research impact assessment

In recent decades, we have experienced a continuously increasing publication rate of scientific articles and related research objects (e.g., data sets, software packages). As this trend keeps growing, practitioners in the field scholarly knowledge are confronted with several challenges. special issue, focus on two major categories such challenges: (a) those to organization achieve flexible, context-sensitive, fine-grained, machine-actionable representation that at same time is structured, interlinked, semantically rich, (b) design novel, reliable, comprehensive metrics assess impact.To address challenges first category, new technical infrastructures becoming increasingly popular, organizing representing through graphs (SKG). These large networks describing actors authors, organizations), documents publications, patents), other outputs data, software) topics, concepts, tasks, technologies) space as well their reciprocal relationships. resources provide substantial benefits researchers, companies, policymakers by powering data-driven services for navigating, analyzing, making sense dynamics. Some examples include Microsoft Academic Graph (MAG) (Sinha, Shen et al., 2015), AMiner (Tang, Zhang 2008), Open (Sinha 2015; Tang ScholarlyData.org (Nuzzolese, Gentile 2016), Semantic Scholar (Ammar, Groeneveld 2018), PID (Fenner & Aryani, 2019), Research Knowledge (Jaradeh, Oelen OpenCitations (Peroni, Shotton, Vitali, 2017), OpenAIRE graph (Manghi, Atzori 2019). Despite popularity, SKGs has lot open challenges, ontologies able conceptualize knowledge, model its representation, enable exchange across different SKGs; extraction entities integration information from heterogeneous sources, identification duplicates, finding connections between entities, identifying conceptual inconsistencies; development exploit provided one or more discover, monitor, measure, consume outcomes (Aryani, Fenner 2020; Auer, 2018).With regard second seek effective precise assessment. context, there need reliable indicators impact merit institutions, individual relevant entities. refers attention work receives inside respective disciplines (Kanellos, Vergoulis social/mass media (Galligan Dyas-Correia, 2013), so on. A work’s merit, hand, quality aspects novelty, reproducibility, compliance Findable, Accessible, Interoperable, Reusable [FAIR] initiative promoting discovery reuse, readability). Nowadays, due growing popularity Science initiatives, number useful science-related sets been made openly available, paving way synthesis sophisticated (and, consequently, assessment). For instance, years, systematic effort various developing teams, variety providing very rich relatively clean source about academics, metadata can be used assessment approaches (Chatzopoulos, 2020).The proposal issue originated collaboration workshops, Scientific Graphs Workshop (SKG 2020), Assessing Impact Merit (AIMinScience held (virtually) conjunction 2020 edition International Conference Theory Practice Digital Libraries (TPDL) August 25, 2020. SKG offered forum discuss themes surrounding set namely methods extracting relationships publications; models description data; exploration, retrieval, visualization graphs, applications data. On AIMinScience focused which scientometrics bibliometrics; utilizing community industry; mining machine learning facilitate assessment; insightful techniques utilize assessment.Given both workshops because indeed support assessment, it was joint decision edit Assessment, aim all interested current advances these particular aspects. addition, catalyzed creation Knowledge: Representation, Discovery, Assessment1 (Sci-K), event replaced AIMinScience, focusing wider subject audience. Sci-K aims explore innovative solutions ideas generation approaches, models, graphs), supporting, directing, monitoring, assessing knowledge. Its edition, 2021, April 13, co-organized The Web 20212. It successful 11 presented papers keynote talks Prof. Ludo Waltman Staša Milojević.This includes 10 contributions, equally balanced category introduce enrich classic articles, patents, further exploring efficiently, insights, creating analyses trends. propose key field, modeling evolution credit over time, citing analyzing trends social networks, predicting citation-based popularity. contributions domains, including computer science, phenomenon-oriented studies, opioids, COVID-19. follow, briefly summarize each contribution.Menin, Michel al. (2021) Covid-on-the-Web, tool assists users accessing, querying, COVID-19-related literature. effort, authors built “COVID-19 Dataset” (Lu Wang, Lo then enriched using entity linking argument mining, finally an interface, “Linked Data Visualizer” (LDViz), querying visual exploration referred set.Färber Lamprecht Set (DSKG), disciplines. graph, connected modeled 2015). DSKG ORCID IDs Wikidata.Angioni, Salatino Academia/Industry DynAmics (AIDA) Graph, generated automatic pipeline integrating MAG, Dimensions, English DBpedia, GRID, Computer Ontology (CSO) (Salatino, Thanapalasingam 2018). Currently, AIDA describes 21 million publications 8 patents according topics drawn CSO. 5.1 5.6 also characterized type affiliations academia, industry) 66 industrial sectors automotive, financial, energy, electronics).Buneman, Dosso regarding citation graphs. do not appropriately time—for when assigned versions (preprint peer reviewed). Usually, prefer citations receive merged. challenge ability cite whole (single object) constituents. To tackle suggest extension model, based citable units reference subsumption, will improve practices bibliometric computations.Kelley Garijo present SOftware Metadata Extraction Framework (SOMEF), approach automatically extract documentation, specifically readme file. Next, they methodology structuring extracted within software. Finally, browsing comparing contents graph.On Vergoulis, Kanellos BIP4COVID19, offers measures coronavirus-related articles. exploited added-value aiming use case, publicly accessible keyword-based search interface leverages BIP4COVID19 rank results calculated indicators.Rothenberger, Pasta, Mayerhoffer analyze measure fields. Specifically, analyzed migration research, focuses conceptualizing, capturing, documenting observed phenomenon (i.e., migration). analysis, fields, up framework acknowledge novel factor.Haunschild, Bornmann investigate opioid received public Twitter. generate topic co-occurring author keywords), tweets tweeted accounts. showed Twitter tend generic terms compared publications.Ghosal, Tiwary proposed method identifies significant citations, developed trace lineage given via transitively article. This retrievability literature, true influence beyond counts.Finally, Chatzopoulos, problem estimating expected (or short-term impact) papers. State-of-the-art attempt leverage paper. However, prone inaccuracies recently published papers, limited history. ArtSim+, applied top any estimation accuracy, accurate estimations most considering similar older ones.Many people supported us producing would like take opportunity thank them. First all, reviewers outstanding reviews (listed alphabetical order): Mehwish Alam, Alberto Baccini, Alessia Bardi, Nikos Bikakis, Arthur Brack, Davide Buscaldi, Rodrigo Costas, Marilena Daquino, Danilo Dessì, Angelo Di Iorio, Michael Färber, Patricia Feeney, Marcia Ferreira, Vladimir A. Fomichov, Yannis Foufoulas, Daniel Garijo, Tirthankar Ghosal, Drahomira Herrmannova, Ilias Kanellos, Dimitrios Katsaros, Barbara S. Lancho-Barrantes, Shubhanshu Mishra, Christos Papatheodorou, Andres Pavas, Giovanni Pizzi, David Pride, Paraskevi Raftopoulou, Diego Reforgiato, Daniele Rotolo, Jodi Schneider, Anna Severin, Skoutas, Markus Stocker, Vincent Antonio Traag, Tryfonopoulos, Nikolay Yakovets. We Professor welcoming Quantitative Studies his excellent guidance throughout process. Last but least, grateful Technische Informationsbibliothek (TIB)—Leibniz Information Centre Technology partially covering APCs issue.