Comparing research contributions in a scholarly knowledge graph

Conducting a scientific literature review is a time consuming activity. This holds for both finding and comparing the related literature. In this paper, we present a workflow and system designed to, among other things, compare research contributions in a scientific knowledge graph. In order to compare contributions, multiple tasks are performed, including finding similar contributions, mapping properties and visualizing the comparison. The presented workflow is implemented in the Open Research Knowledge Graph (ORKG) which enables researchers to find and compare related literature. A preliminary evaluation has been conducted with researchers. Results show that researchers are satisfied with the usability of the user interface, but more importantly, they acknowledge the need and usefulness of contribution comparisons

Generate FAIR Literature Surveys with Scholarly Knowledge Graphs

Reviewing scientific literature is a cumbersome, time consuming but crucial activity in research. Leveraging a scholarly knowledge graph, we present a methodology and a system for comparing scholarly literature, in particular research contributions describing the addressed problem, utilized materials, employed methods and yielded results. The system can be used by researchers to quickly get familiar with existing work in a specific research domain (e.g., a concrete research question or hypothesis). Additionally, it can be used to publish literature surveys following the FAIR Data Principles. The methodology to create a research contribution comparison consists of multiple tasks, specifically: (a) finding similar contributions, (b) aligning contribution descriptions, (c) visualizing and finally (d) publishing the comparison. The methodology is implemented within the Open Research Knowledge Graph (ORKG), a scholarly infrastructure that enables researchers to collaboratively describe, find and compare research contributions. We evaluate the implementation using data extracted from published review articles. The evaluation also addresses the FAIRness of comparisons published with the ORKG

Interactive multidimensional modeling of linked data for exploratory OLAP

Exploratory OLAP aims at coupling the precision and detail of corporate data with the information wealth of LOD. While some techniques to create, publish, and query RDF cubes are already available, little has been said about how to contextualize these cubes with situational data in an on-demand fashion. In this paper we describe an approach, called iMOLD, that enables non-technical users to enrich an RDF cube with multidimensional knowledge by discovering aggregation hierarchies in LOD. This is done through a user-guided process that recognizes in the LOD the recurring modeling patterns that express roll-up relationships between RDF concepts, then translates these patterns into aggregation hierarchies to enrich the RDF cube. Two families of aggregation patterns are identified, based on associations and generalization respectively, and the algorithms for recognizing them are described. To evaluate iMOLD in terms of efficiency and effectiveness we compare it with a related approach in the literature, we propose a case study based on DBpedia, and we discuss the results of a test made with real users.Peer ReviewedPostprint (author's final draft

Knowledge management and Discovery for advanced Enterprise Knowledge Engineering

2012 - 2013The research work addresses mainly issues related to the adoption of models, methodologies and knowledge management tools that implement a pervasive use of the latest technologies in the area of Semantic Web for the improvement of business processes and Enterprise 2.0 applications. The first phase of the research has focused on the study and analysis of the state of the art and the problems of Knowledge Discovery database, paying more attention to the data mining systems. The most innovative approaches which were investigated for the "Enterprise Knowledge Engineering" are listed below. In detail, the problems analyzed are those relating to architectural aspects and the integration of Legacy Systems (or not). The contribution of research that is intended to give, consists in the identification and definition of a uniform and general model, a "Knowledge Enterprise Model", the original model with respect to the canonical approaches of enterprise architecture (for example with respect to the Object Management - OMG - standard). The introduction of the tools and principles of Enterprise 2.0 in the company have been investigated and, simultaneously, Semantic Enterprise based appropriate solutions have been defined to the problem of fragmentation of information and improvement of the process of knowledge discovery and functional knowledge sharing. All studies and analysis are finalized and validated by defining a methodology and related software tools to support, for the improvement of processes related to the life cycles of best practices across the enterprise. Collaborative tools, knowledge modeling, algorithms, knowledge discovery and extraction are applied synergistically to support these processes. [edited by author]XII n.s

Leveraging human-computer interaction and crowdsourcing for scholarly knowledge graph creation

The number of scholarly publications continues to grow each year, as well as the number of journals and active researchers. Therefore, methods and tools to organize scholarly knowledge are becoming increasingly important. Without such tools, it becomes increasingly difficult to conduct research in an efficient and effective manner. One of the fundamental issues scholarly communication is facing relates to the format in which the knowledge is shared. Scholarly communication relies primarily on narrative document-based formats that are specifically designed for human consumption. Machines cannot easily access and interpret such knowledge, leaving machines unable to provide powerful tools to organize scholarly knowledge effectively. In this thesis, we propose to leverage knowledge graphs to represent, curate, and use scholarly knowledge. The systematic knowledge representation leads to machine-actionable knowledge, which enables machines to process scholarly knowledge with minimal human intervention. To generate and curate the knowledge graph, we propose a machine learning assisted crowdsourcing approach, in particular Natural Language Processing (NLP). Currently, NLP techniques are not able to satisfactorily extract high-quality scholarly knowledge in an autonomous manner. With our proposed approach, we intertwine human and machine intelligence, thus exploiting the strengths of both approaches. First, we discuss structured scholarly knowledge, where we present the Open Research Knowledge Graph (ORKG). Specifically, we focus on the design and development of the ORKG user interface (i.e., the frontend). One of the key challenges is to provide an interface that is powerful enough to create rich knowledge descriptions yet intuitive enough for researchers without a technical background to create such descriptions. The ORKG serves as the technical foundation for the rest of the work. Second, we focus on comparable scholarly knowledge, where we introduce the concept of ORKG comparisons. ORKG comparisons provide machine-actionable overviews of related literature in a tabular form. Also, we present a methodology to leverage existing literature reviews to populate ORKG comparisons via a human-in-the-loop approach. Additionally, we show how ORKG comparisons can be used to form ORKG SmartReviews. The SmartReviews provide dynamic literature reviews in the form of living documents. They are an attempt address the main weaknesses of the current literature review practice and outline how the future of review publishing can look like. Third, we focus designing suitable tasks to generate scholarly knowledge in a crowdsourced setting. We present an intelligent user interface that enables researchers to annotate key sentences in scholarly publications with a set of discourse classes. During this process, researchers are assisted by suggestions coming from NLP tools. In addition, we present an approach to validate NLP-generated statements using microtasks in a crowdsourced setting. With this approach, we lower the barrier to entering data in the ORKG and transform content consumers into content creators. With the work presented, we strive to transform scholarly communication to improve machine-actionability of scholarly knowledge. The approaches and tools are deployed in a production environment. As a result, the majority of the presented approaches and tools are currently in active use by various research communities and already have an impact on scholarly communication.Die Zahl der wissenschaftlichen Veröffentlichungen nimmt jedes Jahr weiter zu, ebenso wie die Zahl der Zeitschriften und der aktiven Forscher. Daher werden Methoden und Werkzeuge zur Organisation von wissenschaftlichem Wissen immer wichtiger. Ohne solche Werkzeuge wird es immer schwieriger, Forschung effizient und effektiv zu betreiben. Eines der grundlegenden Probleme, mit denen die wissenschaftliche Kommunikation konfrontiert ist, betrifft das Format, in dem das Wissen publiziert wird. Die wissenschaftliche Kommunikation beruht in erster Linie auf narrativen, dokumentenbasierten Formaten, die speziell für Experten konzipiert sind. Maschinen können auf dieses Wissen nicht ohne weiteres zugreifen und es interpretieren, so dass Maschinen nicht in der Lage sind, leistungsfähige Werkzeuge zur effektiven Organisation von wissenschaftlichem Wissen bereitzustellen. In dieser Arbeit schlagen wir vor, Wissensgraphen zu nutzen, um wissenschaftliches Wissen darzustellen, zu kuratieren und zu nutzen. Die systematische Wissensrepräsentation führt zu maschinenverarbeitbarem Wissen. Dieses ermöglicht es Maschinen wissenschaftliches Wissen mit minimalem menschlichen Eingriff zu verarbeiten. Um den Wissensgraphen zu generieren und zu kuratieren, schlagen wir einen Crowdsourcing-Ansatz vor, der durch maschinelles Lernen unterstützt wird, insbesondere durch natürliche Sprachverarbeitung (NLP). Derzeit sind NLP-Techniken nicht in der Lage, qualitativ hochwertiges wissenschaftliches Wissen auf autonome Weise zu extrahieren. Mit unserem vorgeschlagenen Ansatz verknüpfen wir menschliche und maschinelle Intelligenz und nutzen so die Stärken beider Ansätze. Zunächst erörtern wir strukturiertes wissenschaftliches Wissen, wobei wir den Open Research Knowledge Graph (ORKG) vorstellen.Insbesondere konzentrieren wir uns auf das Design und die Entwicklung der ORKG-Benutzeroberfläche (das Frontend). Eine der größten Herausforderungen besteht darin, eine Schnittstelle bereitzustellen, die leistungsfähig genug ist, um umfangreiche Wissensbeschreibungen zu erstellen und gleichzeitig intuitiv genug ist für Forscher ohne technischen Hintergrund, um solche Beschreibungen zu erstellen. Der ORKG dient als technische Grundlage für die Arbeit. Zweitens konzentrieren wir uns auf vergleichbares wissenschaftliches Wissen, wofür wir das Konzept der ORKG-Vergleiche einführen. ORKG-Vergleiche bieten maschinell verwertbare Übersichten über verwandtes wissenschaftliches Wissen in tabellarischer Form. Außerdem stellen wir eine Methode vor, mit der vorhandene Literaturübersichten genutzt werden können, um ORKG-Vergleiche mit Hilfe eines Human-in-the-Loop-Ansatzes zu erstellen. Darüber hinaus zeigen wir, wie ORKG-Vergleiche verwendet werden können, um ORKG SmartReviews zu erstellen. Die SmartReviews bieten dynamische Literaturübersichten in Form von lebenden Dokumenten. Sie stellen einen Versuch dar, die Hauptschwächen der gegenwärtigen Praxis des Literaturreviews zu beheben und zu skizzieren, wie die Zukunft der Veröffentlichung von Reviews aussehen kann. Drittens konzentrieren wir uns auf die Gestaltung geeigneter Aufgaben zur Generierung von wissenschaftlichem Wissen in einer Crowdsourced-Umgebung. Wir stellen eine intelligente Benutzeroberfläche vor, die es Forschern ermöglicht, Schlüsselsätze in wissenschaftlichen Publikationen mittles Diskursklassen zu annotieren. In diesem Prozess werden Forschende mit Vorschlägen von NLP-Tools unterstützt. Darüber hinaus stellen wir einen Ansatz zur Validierung von NLP-generierten Aussagen mit Hilfe von Mikroaufgaben in einer Crowdsourced-Umgebung vor. Mit diesem Ansatz senken wir die Hürde für die Eingabe von Daten in den ORKG und setzen Inhaltskonsumenten als Inhaltsersteller ein. Mit der Arbeit streben wir eine Transformation der wissenschaftlichen Kommunikation an, um die maschinelle Verwertbarkeit von wissenschaftlichem Wissen zu verbessern. Die Ansätze und Werkzeuge werden in einer Produktionsumgebung eingesetzt. Daher werden die meisten der vorgestellten Ansätze und Werkzeuge derzeit von verschiedenen Forschungsgemeinschaften aktiv genutzt und haben bereits einen Einfluss auf die wissenschaftliche Kommunikation.EC/ERC/819536/E

Populating a Linked Data Entity Name System

Resource Description Framework (RDF) is a graph-based data model used to publish data as a Web of Linked Data. RDF is an emergent foundation for large-scale data integration, the problem of providing a unified view over multiple data sources. An Entity Name System (ENS) is a thesaurus for entities, and is a crucial component in a data integration architecture. Populating a Linked Data ENS is equivalent to solving an Artificial Intelligence problem called instance matching, which concerns identifying pairs of entities referring to the same underlying entity. This dissertation presents an instance matcher with four properties, namely automation, heterogeneity, scalability and domain independence. Automation is addressed by employing inexpensive but well-performing heuristics to automatically generate a training set, which is employed by other machine learning algorithms in the pipeline. Data-driven alignment algorithms are adapted to deal with structural heterogeneity in RDF graphs. Domain independence is established by actively avoiding prior assumptions about input domains, and through evaluations on ten RDF test cases. The full system is scaled by implementing it on cloud infrastructure using MapReduce algorithms.Computer Science