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- ItemUnveiling Relations in the Industry 4.0 Standards Landscape Based on Knowledge Graph Embeddings(Cham : Springer, 2020) Rivas, Ariam; Grangel-González, Irlán; Collarana, Diego; Lehmann, Jens; Vidal, Maria-Esther; Hartmann, Sven; Küng, Josef; Kotsis, Gabriele; Tjoa, A Min; Khalil, IsmailIndustry 4.0 (I4.0) standards and standardization frameworks have been proposed with the goal of empowering interoperability in smart factories. These standards enable the description and interaction of the main components, systems, and processes inside of a smart factory. Due to the growing number of frameworks and standards, there is an increasing need for approaches that automatically analyze the landscape of I4.0 standards. Standardization frameworks classify standards according to their functions into layers and dimensions. However, similar standards can be classified differently across the frameworks, producing, thus, interoperability conflicts among them. Semantic-based approaches that rely on ontologies and knowledge graphs, have been proposed to represent standards, known relations among them, as well as their classification according to existing frameworks. Albeit informative, the structured modeling of the I4.0 landscape only provides the foundations for detecting interoperability issues. Thus, graph-based analytical methods able to exploit knowledge encoded by these approaches, are required to uncover alignments among standards. We study the relatedness among standards and frameworks based on community analysis to discover knowledge that helps to cope with interoperability conflicts between standards. We use knowledge graph embeddings to automatically create these communities exploiting the meaning of the existing relationships. In particular, we focus on the identification of similar standards, i.e., communities of standards, and analyze their properties to detect unknown relations. We empirically evaluate our approach on a knowledge graph of I4.0 standards using the Trans∗ family of embedding models for knowledge graph entities. Our results are promising and suggest that relations among standards can be detected accurately.
- ItemA Data-Driven Approach for Analyzing Healthcare Services Extracted from Clinical Records(Piscataway, NJ : IEEE, 2020) Scurti, Manuel; Menasalvas-Ruiz, Ernestina; Vidal, Maria-Esther; Torrente, Maria; Vogiatzis, Dimitrios; Paliouras, George; Provencio, Mariano; Rodríguez-González, Alejandro; Seco de Herrera, Alba García; Rodríguez González, Alejandro; Santosh, K.C.; Temesgen, Zelalem; Soda, PaoloCancer remains one of the major public health challenges worldwide. After cardiovascular diseases, cancer is one of the first causes of death and morbidity in Europe, with more than 4 million new cases and 1.9 million deaths per year. The suboptimal management of cancer patients during treatment and subsequent follows up are major obstacles in achieving better outcomes of the patients and especially regarding cost and quality of life In this paper, we present an initial data-driven approach to analyze the resources and services that are used more frequently by lung-cancer patients with the aim of identifying where the care process can be improved by paying a special attention on services before diagnosis to being able to identify possible lung-cancer patients before they are diagnosed and by reducing the length of stay in the hospital. Our approach has been built by analyzing the clinical notes of those oncological patients to extract this information and their relationships with other variables of the patient. Although the approach shown in this manuscript is very preliminary, it shows that quite interesting outcomes can be derived from further analysis. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
- ItemOntology Design for Pharmaceutical Research Outcomes(Cham : Springer, 2020) Say, Zeynep; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören; Hall, Mark; Merčun, Tanja; Risse, Thomas; Duchateau, FabienThe network of scholarly publishing involves generating and exchanging ideas, certifying research, publishing in order to disseminate findings, and preserving outputs. Despite enormous efforts in providing support for each of those steps in scholarly communication, identifying knowledge fragments is still a big challenge. This is due to the heterogeneous nature of the scholarly data and the current paradigm of distribution by publishing (mostly document-based) over journal articles, numerous repositories, and libraries. Therefore, transforming this paradigm to knowledge-based representation is expected to reform the knowledge sharing in the scholarly world. Although many movements have been initiated in recent years, non-technical scientific communities suffer from transforming document-based publishing to knowledge-based publishing. In this paper, we present a model (PharmSci) for scholarly publishing in the pharmaceutical research domain with the goal of facilitating knowledge discovery through effective ontology-based data integration. PharmSci provides machine-interpretable information to the knowledge discovery process. The principles and guidelines of the ontological engineering have been followed. Reasoning-based techniques are also presented in the design of the ontology to improve the quality of targeted tasks for data integration. The developed ontology is evaluated with a validation process and also a quality verification method.
- ItemCreation of a Knowledge Space by Semantically Linking Data Repository and Knowledge Management System - a Use Case from Production Engineering(Laxenburg : IFAC, 2022) Sheveleva, Tatyana; Wawer, Max Leo; Oladazimi, Pooya; Koepler, Oliver; Nürnberger, Florian; Lachmayer, Roland; Auer, Sören; Mozgova, IrynaThe seamless documentation of research data flows from generation, processing, analysis, publication, and reuse is of utmost importance when dealing with large amounts of data. Semantic linking of process documentation and gathered data creates a knowledge space enabling the discovery of relations between steps of process chains. This paper shows the design of two systems for data deposit and for process documentation using semantic annotations and linking on a use case of a process chain step of the Tailored Forming Technology.
- ItemInteraction Network Analysis Using Semantic Similarity Based on Translation Embeddings(Berlin ; Heidelberg : Springer, 2019) Manzoor Bajwa, Awais; Collarana, Diego; Vidal, Maria-Esther; Acosta, Maribel; Cudré-Mauroux, Philippe; Maleshkova, Maria; Pellegrini, Tassilo; Sack, Harald; Sure-Vetter, YorkBiomedical knowledge graphs such as STITCH, SIDER, and Drugbank provide the basis for the discovery of associations between biomedical entities, e.g., interactions between drugs and targets. Link prediction is a paramount task and represents a building block for supporting knowledge discovery. Although several approaches have been proposed for effectively predicting links, the role of semantics has not been studied in depth. In this work, we tackle the problem of discovering interactions between drugs and targets, and propose SimTransE, a machine learning-based approach that solves this problem effectively. SimTransE relies on translating embeddings to model drug-target interactions and values of similarity across them. Grounded on the vectorial representation of drug-target interactions, SimTransE is able to discover novel drug-target interactions. We empirically study SimTransE using state-of-the-art benchmarks and approaches. Experimental results suggest that SimTransE is competitive with the state of the art, representing, thus, an effective alternative for knowledge discovery in the biomedical domain.
- ItemSemantic Representation of Physics Research Data(Setúbal, Portugal : Science and Technology Publications, Lda, 2020) Say, Aysegul; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören; Aveiro, David; Dietz, Jan; Filipe, JoaquimImprovements in web technologies and artificial intelligence enable novel, more data-driven research practices for scientists. However, scientific knowledge generated from data-intensive research practices is disseminated with unstructured formats, thus hindering the scholarly communication in various respects. The traditional document-based representation of scholarly information hampers the reusability of research contributions. To address this concern, we developed the Physics Ontology (PhySci) to represent physics-related scholarly data in a machine-interpretable format. PhySci facilitates knowledge exploration, comparison, and organization of such data by representing it as knowledge graphs. It establishes a unique conceptualization to increase the visibility and accessibility to the digital content of physics publications. We present the iterative design principles by outlining a methodology for its development and applying three different evaluation approaches: data-driven and criteria-based evaluation, as well as ontology testing.
- ItemOptimierte Erfassung und Veröffentlichung von Artikelmetadaten für eine nachhaltige Metadatenallmende im OA-Ökosystem zur Unterstützung von Auffindbarkeit und Evaluation(Zenodo, 2023) Nüst, Daniel; Hauschke, Christian; Coordts, Anette; Yücel, GaziDie Metadatenallmende ist ein wichtiger Baustein für den Wandel hin zu einem diversen Open-Access-Ökosystem. Nur mit offenen und hochwertigen Metadaten, die mindestens die gleiche oder sogar höhere Qualität und Abdeckung als die Metadaten-Silos etablierter kommerzieller Verlage erreichen, können unabhängige Zeitschriften auf Augenhöhe Themen wie Auffindbarkeit und faire Evaluation von Forschenden angehen. Wir stellen einen Arbeitsablauf und Werkzeuge vor, die eine Professionalisierung der Metadatenprozesse von unabhängigen, scholar-led OA-Journals unterstützen. Diese Journals können damit ihre Sichtbarkeit im Wissenschaftssystem und ihre Bedeutung als Publikationsort erhöhen. Dazu wird die global verbreitete Free and Open Source Software (FOSS) Open Journal Systems (OJS) um Funktionen erweitert, die a) Eingabe, Kuratierung und Anreicherung von artikelbezogenen Metadaten durch Autor:innen und Editor:innen und b) den Export dieser Metadaten an offene Datenquellen wie z. B. Wikidata ermöglichen. Diese Erweiterung wird durch OJS-Plugins und auch Anpassung der OJS-Kernsoftware in den folgenden Anwendungsfällen umgesetzt: Erstens ermöglicht die Integration von validierten persistenten Identifikatoren (PIDs) und geographischen Metadaten als Teil der Publikationsmetadaten eine bessere Auffindbarkeit und von Artikeln aus Open-Access-Zeitschriften. PIDs und Geometadaten stellen Verbindungen zu anderen Publikationen, akademischen Events, physischen Proben und wissenschaftlichen Instrumenten her und verknüpfen so verwandte Artikel über Zeitschriften und Wissenschaftsdisziplinen hinweg. Auf Basis dieser Metadaten werden zum Beispiel eine Suchplattform realisiert, die wissenschaftliche Artikel über Disziplingrenzen und Publikationsplatformen hinweg als offene Daten interaktiv auf einer Karte darstellt, und semantisch bedeutsame Links in Artikeln und ihren Landing Pages eingefügt. Zweitens werden Zitationsmetadaten während des Einreichungs- und Begutachtungsprozesses strukturiert erfasst und frei in standardisierten Formaten veröffentlicht. Durch die innovative und benutzerfreundliche Metadatenerfassung im Zuge des wissenschaftlichen Veröffentlichungsprozesses können Open-Access-Artikel in transparenten zitationsbasierten Evaluationsmetriken verwendet werden. Diese Neuerungen unterstützen eine offene Publikationskultur und -praxis. In diesem Beitrag berichten wir vom aktuellen Arbeitsstand zu den Anwendungsfällen und beschreiben eine Vision für eine neuartige verteilte Erfassung und Veröffentlichung offener Metadaten.
- ItemBaroque AI(Zenodo, 2023) Worthington, Simon; Blümel, InaPublication prototype: A computational publishing and AI assisted writing course unit with students of the Open Knowledge class – at Hochschule Hannover with the Open Science Lab, TIB. The prototype publication exercise involves creating a fictional ‘exhibition catalogue’ drawing on Wikidata based cataloguing of seventeenth century painting deposited by the Bavarian State Painting Collections. The prototype demostrates how computational publishing can be used to bring together different distributed linked open data (LOD) sources. Additionally AI tools are used for assisted essay writing. Then both are encapsulated in a multi-format computational publication — allowing for asynchronous collaborative working. Distributed LOD sources include: Wikidata/base, Nextcloud, Thoth, Semantic Kompakkt, and TIB AV Portal. AI tools used for essay writing are — OpenAI and Perplexity. Eleven students completed the class unit which was carried out over March to April 2023. An open access OER guide to running the class, a template publication for use in the class are online on GitHub and designed for OER reuse. Full class information and resources are on Wikiversity. The open source software used is brought together in the ADA Pipeline.
- ItemCausal Relationship over Knowledge Graphs(2022) Huang, Hao; Al Hasan, Mohammad; Xiong, LiCausality has been discussed for centuries, and the theory of causal inference over tabular data has been broadly studied and utilized in multiple disciplines. However, only a few works attempt to infer the causality while exploiting the meaning of the data represented in a data structure like knowledge graph. These works offer a glance at the possibilities of causal inference over knowledge graphs, but do not yet consider the metadata, e.g., cardinalities, class subsumption and overlap, and integrity constraints. We propose CareKG, a new formalism to express causal relationships among concepts, i.e., classes and relations, and enable causal queries over knowledge graphs using semantics of metadata. We empirically evaluate the expressiveness of CareKG in a synthetic knowledge graph concerning cardinalities, class subsumption and overlap, integrity constraints. Our initial results indicate that CareKG can represent and measure causal relations with some semantics which are uncovered by state-of-the-art approaches.
- ItemEasy Semantification of Bioassays(Heidelberg : Springer, 2022) Anteghini, Marco; D’Souza, Jennifer; dos Santos, Vitor A. P. Martins; Auer, SörenBiological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose a solution for automatically semantifying biological assays. Our solution contrasts the problem of automated semantification as labeling versus clustering where the two methods are on opposite ends of the method complexity spectrum. Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art labeling approach. This novel contribution is based on two factors: 1) a learning objective closely modeled after the data outperforms an alternative approach with sophisticated semantic modeling; 2) automatically semantifying biological assays achieves a high performance F1 of nearly 83%, which to our knowledge is the first reported standardized evaluation of the task offering a strong benchmark model.