2018, Musyaffa, Fathoni A., Halilaj, Lavdim, Li, Yakun, Orlandi, Fabrizio, Jabeen, Hajira, Auer, Sören, Vidal, Maria-Esther

A paper describing the details of OpenBudgets.eu platform implementation. Pre-print version of the paper accepted at International Conference On Web Engineering (ICWE) 2018 in Caceres, Spain.

2018-11-29, Elias, Mirette, Lohmann, Steffen, Auer, Sören

OpenCourseWare (OCW) systems have been established to provide open educational resources that are accessible by anyone, including learners with special accessibility needs and preferences. We need to find a formal and interoperable way to describe these preferences in order to use them in OCW systems and retrieve relevant educational resources. This formal representation should use standard accessibility definitions of OCW that can be reused by other OCW systems to represent accessibility concepts. In this article, we present an ontology to represent the accessibility needs of learners with respect to the IMS AfA specifications. The ontology definitions together with rule-based queries are used to retrieve relevant educational resources. Related to this, we developed a user interface component that enables users to create accessibility profiles representing their individual needs and preferences based on our ontology. We evaluated the approach with five examples profiles.

2018, Auer, Sören, Blümel, Ina, Ewerth, Ralph, Garatzogianni, Alexandra, Heller,, Lambert, Hoppe, Anett, Kasprzik, Anna, Koepler, Oliver, Nejdl, Wolfgang, Plank, Margret, Sens, Irina, Stocker, Markus, Tullney, Marco, Vidal, Maria-Esther, van Wezenbeek, Wilma

The document-oriented workflows in science have reached (or already exceeded) the limits of adequacy as highlighted for example by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. Despite an improved and digital access to scientific publications in the last decades, the exchange of scholarly knowledge continues to be primarily document-based: Researchers produce essays and articles that are made available in online and offline publication media as roughly granular text documents. With current developments in areas such as knowledge representation, semantic search, human-machine interaction, natural language processing, and artificial intelligence, it is possible to completely rethink this dominant paradigm of document-centered knowledge exchange and transform it into knowledge-based information flows by representing and expressing knowledge through semantically rich, interlinked knowledge graphs. The core of the establishment of knowledge-based information flows is the distributed, decentralized, collaborative creation and evolution of information models, vocabularies, ontologies, and knowledge graphs for the establishment of a common understanding of data and information between the various stakeholders as well as the integration of these technologies into the infrastructure and processes of search and knowledge exchange in the research library of the future. By integrating these information models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This revolutionizes scientific work because information and research results can be seamlessly interlinked with each other and better mapped to complex information needs. As a result, scientific work becomes more effective and efficient, since results become directly comparable and easier to reuse. In order to realize the vision of knowledge-based information flows in scholarly communication, comprehensive long-term technological infrastructure development and accompanying research are required. To secure information sovereignty, it is also of paramount importance to science – and urgency to science policymakers – that scientific infrastructures establish an open counterweight to emerging commercial developments in this area. The aim of this position paper is to facilitate the discussion on requirements, design decisions and a minimum viable product for an Open Research Knowledge Graph infrastructure. TIB aims to start developing this infrastructure in an open collaboration with interested partner organizations and individuals.

2019, Thakkar, Harsh, Auer, Sören, Vidal, Maria-Esther, Samavi, Reza, Consens, Mariano P., Khatchadourian, Shahan, Nguyen, Vinh, Sheth, Amit, Giménez-García, José M., Thakkar, Harsh

Graph data management (also called NoSQL) has revealed beneficial characteristics in terms of flexibility and scalability by differ-ently balancing between query expressivity and schema flexibility. This peculiar advantage has resulted into an unforeseen race of developing new task-specific graph systems, query languages and data models, such as property graphs, key-value, wide column, resource description framework (RDF), etc. Present-day graph query languages are focused towards flex-ible graph pattern matching (aka sub-graph matching), whereas graph computing frameworks aim towards providing fast parallel (distributed) execution of instructions. The consequence of this rapid growth in the variety of graph-based data management systems has resulted in a lack of standardization. Gremlin, a graph traversal language, and machine provide a common platform for supporting any graph computing sys-tem (such as an OLTP graph database or OLAP graph processors). In this extended report, we present a formalization of graph pattern match-ing for Gremlin queries. We also study, discuss and consolidate various existing graph algebra operators into an integrated graph algebra.

2018, Singh, K., Radhakrishna, A.S., Both, A., Shekarpour, S., Lytra, I., Usbeck, R., Vyas, A., Khikmatullaev, A., Punjani, D., Lange, C., Vidal, Maria-Esther, Lehmann, J., Auer, Sören

Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.

2015, Auer, Sören, Heitzig, J., Kornek, U., Schöll, E., Kurths, J.

2018, Musyaffa, Fathoni A., Engels, Christiane, Vidal, Maria-Esther, Orlandi, Fabrizio, Auer, Sören

A pre-print paper detailing recommendation for publishing fiscal data, including assessment framework for fiscal datasets. This paper has been accepted at ACM Journal of Data and Information Quality (JDIQ) in 2018.

2018, Koepler, Oliver, Jung, Nicole, Kraft, Angelina, Neumann, Janna, Auer, Sören, Bach, Felix, Bähr, Thomas, Engel, Thomas, Kettner, Carsten, Kowol-Santen, Johanna, Liermann, Johannes, Lipp, Anne, Porzel, Andrea, Razum, Matthias, Schlörer, Niels, Solle, Dörte, Winkler, Torsten

“Der stufenweise Aufbau einer Nationalen Forschungsdateninfrastruktur in Netzwerkform hat das Ziel, ein verlässliches und nachhaltiges Dienste-Portfolio zu schaffen, welches generische und fachspezifische Bedarfe des Forschungsdatenmanagements in Deutschland abdeckt.” Für das Fachgebiet Chemie ermöglicht eine solche nationale Forschungsdateninfrastruktur, öffentlich-finanzierte Forschungsdaten effizient zu erheben, standardisiert zu beschreiben, dauerhaft zu speichern und durch Persistent Identifier (PID) eindeutig referenzierbar und auffindbar zu machen. Sie unterstützt gemäß den Vorgaben des RfII die Reproduzierbarkeit und Nachnutzbarkeit der Daten zum Zwecke einer perpetuierten Wissensgenerierung. Mit der Reproduzierbarkeit von Forschungsergebnissen unterstützt eine solche Forschungsdateninfrastruktur den Peer Reviewing Prozess zur Förderung der wissenschaftlichen Selbstkontrolle und erhöht die Datenqualität, insbesondere in wissenschaftlichen Publikationen. Die NFDI4Chem ist ein gemeinschaftlicher Ansatz von Wissenschaftlern aus der Chemie, der Fachgesellschaft Gesellschaft Deutscher Chemiker und deren Fachgruppen, Einrichtungen aus der Forschungsförderung und Infrastruktureinrichtungen (Technische Informationsbibliothek). Eine Gruppe von Vertretern dieser Stakeholder hat sich Ende April 2018 zu einem Auftakttreffen “Fachgespräch NFDI4Chem” in Hannover getroffen. Dieses Papier fasst die Erkenntnisse des Fachgespräches zusammen. Weitere Stakeholder wie Verlage oder Datenbankanbieter sind im folgenden Diskurs willkommen.

2019, Fathalla, Said, Lange, Christoph, Auer, Sören, Hitzler, Pascal, Fernández, Miriam, Janowicz, Krzysztof, Zaveri, Amrapali, Gray, Alasdair J.G., Lopez, Vanessa, Haller, Armin, Hammar, Karl

Metadata of scientific events has become increasingly available on the Web, albeit often as raw data in various formats, disregarding its semantics and interlinking relations. This leads to restricting the usability of this data for, e.g., subsequent analyses and reasoning. Therefore, there is a pressing need to represent this data in a semantic representation, i.e., Linked Data. We present the new release of the EVENTSKG dataset, comprising comprehensive semantic descriptions of scientific events of eight computer science communities. Currently, EVENTSKG is a 5-star dataset containing metadata of 73 top-ranked event series (almost 2,000 events) established over the last five decades. The new release is a Linked Open Dataset adhering to an updated version of the Scientific Events Ontology, a reference ontology for event metadata representation, leading to richer and cleaner data. To facilitate the maintenance of EVENTSKG and to ensure its sustainability, EVENTSKG is coupled with a Java API that enables users to add/update events metadata without going into the details of the representation of the dataset. We shed light on events characteristics by analyzing EVENTSKG data, which provides a flexible means for customization in order to better understand the characteristics of renowned CS events.

2018, Fathalla, Said, Vahdati, Sahar, Auer, Sören, Lange, Christoph, Fensel, Anna, de Boer, Victor, Pellegrini, Tassilo, Kiesling, Elmar, Haslhofer, Bernhard, Hollink, Laura, Schindler, Alexander

The way how research is communicated using text publications has not changed much over the past decades. We have the vision that ultimately researchers will work on a common structured knowledge base comprising comprehensive semantic and machine-comprehensible descriptions of their research, thus making research contributions more transparent and comparable. We present the SemSur ontology for semantically capturing the information commonly found in survey and review articles. SemSur is able to represent scientific results and to publish them in a comprehensive knowledge graph, which provides an efficient overview of a research field, and to compare research findings with related works in a structured way, thus saving researchers a significant amount of time and effort. The new release of SemSur covers more domains, defines better alignment with external ontologies and rules for eliciting implicit knowledge. 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 SemSur ontology to answer queries about the different research contributions covered by the survey. SemSur is currently used and maintained at OpenResearch.org.