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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.
- 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.
- ItemFormalizing Gremlin pattern matching traversals in an integrated graph Algebra(Aachen, Germany : RWTH Aachen, 2019) Thakkar, Harsh; Auer, Sören; Vidal, Maria-Esther; Samavi, Reza; Consens, Mariano P.; Khatchadourian, Shahan; Nguyen, Vinh; Sheth, Amit; Giménez-García, José M.; Thakkar, HarshGraph 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.
- ItemA Knowledge Graph for Industry 4.0(Cham : Springer, 2020) Bader, Sebastian R.; Grangel-Gonzalez, Irlan; Nanjappa, Priyanka; Vidal, Maria-Esther; Maleshkova, Maria; Harth, Andreas; Kirrane, Sabrina; Ngonga Ngomo, Axel-Cyrille; Paulheim, Heiko; Rula, Anisa; Gentile, Anna Lisa; Haase, Peter; Cochez, MichaelOne of the most crucial tasks for today’s knowledge workers is to get and retain a thorough overview on the latest state of the art. Especially in dynamic and evolving domains, the amount of relevant sources is constantly increasing, updating and overruling previous methods and approaches. For instance, the digital transformation of manufacturing systems, called Industry 4.0, currently faces an overwhelming amount of standardization efforts and reference initiatives, resulting in a sophisticated information environment. We propose a structured dataset in the form of a semantically annotated knowledge graph for Industry 4.0 related standards, norms and reference frameworks. The graph provides a Linked Data-conform collection of annotated, classified reference guidelines supporting newcomers and experts alike in understanding how to implement Industry 4.0 systems. We illustrate the suitability of the graph for various use cases, its already existing applications, present the maintenance process and evaluate its quality.
- ItemWhy reinvent the wheel: Let's build question answering systems together(New York City : Association for Computing Machinery, 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, SörenModern 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.
- ItemFalcon 2.0: An Entity and Relation Linking Tool over Wikidata(New York City, NY : Association for Computing Machinery, 2020) Sakor, Ahmad; Singh, Kuldeep; Patel, Anery; Vidal, Maria-EstherThe Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from a natural language text, and possibly linking them to proper matches in Knowledge Graphs (KGs). Considering Wikidata as the background KG, there are still limited tools to link knowledge within the text to Wikidata. In this paper, we present Falcon 2.0, the first joint entity and relation linking tool over Wikidata. It receives a short natural language text in the English language and outputs a ranked list of entities and relations annotated with the proper candidates in Wikidata. The candidates are represented by their Internationalized Resource Identifier (IRI) in Wikidata. Falcon 2.0 resorts to the English language model for the recognition task (e.g., N-Gram tiling and N-Gram splitting), and then an optimization approach for the linking task. We have empirically studied the performance of Falcon 2.0 on Wikidata and concluded that it outperforms all the existing baselines. Falcon 2.0 is open source and can be reused by the community; all the required instructions of Falcon 2.0 are well-documented at our GitHub repository (https://github.com/SDM-TIB/falcon2.0). We also demonstrate an online API, which can be run without any technical expertise. Falcon 2.0 and its background knowledge bases are available as resources at https://labs.tib.eu/falcon/falcon2/.
- ItemOptimizing Federated Queries Based on the Physical Design of a Data Lake(Aachen : RWTH, 2020) Rohde, Philipp D.; Vidal, Maria-EstherThe optimization of query execution plans is known to be crucial for reducing the query execution time. In particular, query optimization has been studied thoroughly for relational databases over the past decades. Recently, the Resource Description Framework (RDF) became popular for publishing data on the Web. As a consequence, federations composed of different data models like RDF and relational databases evolved. One type of these federations are Semantic Data Lakes where every data source is kept in its original data model and semantically annotated with ontologies or controlled vocabularies. However, state-of-the-art query engines for federated query processing over Semantic Data Lakes often rely on optimization techniques tailored for RDF. In this paper, we present query optimization techniques guided by heuristics that take the physical design of a Data Lake into account. The heuristics are implemented on top of Ontario, a SPARQL query engine for Semantic Data Lakes. Using sourcespecific heuristics, the query engine is able to generate more efficient query execution plans by exploiting the knowledge about indexes and normalization in relational databases. We show that heuristics which take the physical design of the Data Lake into account are able to speed up query processing.
- ItemDigital Transformation of Education Credential Processes and Life Cycles – A Structured Overview on Main Challenges and Research Questions([Wilmington, DE, USA] : IARIA, [2020], 2020) Keck, Ingo R.; Vidal, Maria-Esther; Heller, Lambert; Mikroyannidis, Alexander; Chang, Maiga; White, StephenIn this article, we look at the challenges that arise in the use and management of education credentials, and from the switch from analogue, paper-based education credentials to digital education credentials. We propose a general methodology to capture qualitative descriptions and measurable quantitative results that allow to estimate the effectiveness of a digital credential management system in solving these challenges. This methodology is applied to the EU H2020 project QualiChain use case, where five pilots have been selected to study a broad field of digital credential workflows and credential management. Copyright (c) IARIA, 2020
- ItemPreface of MEPDaW 2020: Managing the evolution and preservation of the data web(Aachen, Germany : RWTH Aachen, 2020) Orlandi, Fabrizio; Graux, Damien; Vidal, Maria-Esther; Fernández, Javier D.; Debattista, Jeremy; Orlandi, Fabrizio; Graux, Damien; Vidal, Maria-Esther; Fernández, Javier D.; Debattista, JeremyThe MEPDaW workshop series targets one of the emerging and fundamental problems of the Web, specifically the management and preservation of evolving knowledge graphs. During the past six years, the workshop series has been gathering a community of researchers and practitioners around these challenges. To date, the series has successfully published more than 30 articles allowing more than 50 individual authors to present and share their ideas. This 6th edition, virtually co-located with the International Semantic Web Conference (ISWC 2020), gathered the community around nine research publications and one invited keynote presentation. The event took place online on the 1st of November, 2020.