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2023 Semantic Web Challenge on Tabular data to knowledge Graph Matching, SemTab 2023

作者： Parmar, Vishvapalsinhji Algergawy, Alsayed Chair of Data and Knowledge Engineering University of Passau Passau Germany

This paper introduces DREIFLUSS, an innovative, minimalist approach designed to tackle the Column Type Annotation (CTA) and Column Property Annotation (CPA) tasks in the SemTab challenge. DREIFLUSS efficiently employs semantic information from well-established knowledge graphs, DBpedia, and ***, to improve the annotation process. Experimental evidence illustrates the superior performance of logistic regression models trained via DREIFLUSS, resulting in precise column-type annotations and insightful relationship predictions. The findings substantiate the significance of proper sampling technique while training a model, thereby boosting the accuracy and efficiency of table matching. This research illuminates a promising pathway to enhance table matching techniques, underlining the practical ramifications of DREIFLUSS for data integration and knowledge discovery endeavors. © 2023 CEUR-WS. All rights reserved.

关键词： data integration

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Patent Classification Using BERT-for-Patents on USPTO 22

Patent Classification Using BERT-for-Patents on USPTO

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Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing

作者： Renukswamy Chikkamath Vishvapalsinhji Ramsinh Parmar Yasser Otiefy Markus Endres University of Passau Germany Chair of Data and Knowledge Engineering University of Passau Germany

ISBN: (纸本)9781450399067

Domain-specific complex nature of patent text, unique drafting styles of patent applicants, and mammoth volume of patent data makes classification a challenging task. To become a helping hand, in the recent time, Google has released pre-trained BERT model trained over 100 million patent documents. However, to the best of our knowledge, there has not been any testament about prediction capabilities and performance of the BERT-for-Patents model over any patent tasks on standard benchmarks. Our work addresses this problem, investigates BERT-for-patents in multi-label patent classification at both CPC and IPC sub-class level. Evidence from experiments enables us to claim that, this work outperformed SOTA by an absolute 2% on micro-F1 with a newly proposed USPTO 2.8M dataset. In order to introduce robustness to the classification process, our collaborative Machine Learning models including NB and SVM uplifted the micro-F1 measures to 70%. This work stands as a corroboration to promote development of patent-specific language models and also claims, robustness in patent analysis tasks can be achieved by not forgetting plain old Machine Learning models. The contributions of this work including code, models, and a novel dataset of the size 2.8M with patent claims are released to the public1, in order to nurture the patent community in developing AI solutions.

关键词： uspto dataset patent classification artificial intelligence patent analysis bert-for-patents

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Social event network analysis: Structure, preferences, and reality

Social event network analysis: Structure, preferences, and r...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Martin Atzmueller Tom Hanika Gerd Stumme Richard Schaller Bernd Ludwig Chair of Knowledge and Data Engineering University of Kassel Germany AG Digital Humanities University of Erlangen-Nuremberg Germany I:IMSK University of Regensburg Germany

ISBN: (纸本)9781509028474

This paper focuses on the analysis of socio-spatial data, i. e., user-performance relations at a distributed event. We consider the data as a bimodal network (i. e., model it as a bipartite graph), and investigate its structural characteristics towards a social network. We focus on plans of the participants (expressed by preferences) and their fulfilment, and propose measures for matching preference and reality. We specifically analyse behavioural patterns w.r.t. distinct user and performance groups. We utilise real-world data collected at the Lange Nacht der Musik (Long Night of Music) 2013 in Munich.

关键词： Social network services Bipartite graph Cultural differences Urban areas Analytical models Planning Global Positioning System

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Neuroinformatics, Neural Networks and Neurocomputers for Brain-inspired Computational Intelligence 17

Neuroinformatics, Neural Networks and Neurocomputers for Bra...

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17th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Kasabov, Nikola K Auckland University of Technology Founding Director Kedri and Knowledge Engineering Auckland New Zealand Ulster University Derry The George Moore Chair Data Analytics United Kingdom Visiting Professor Iict Bulgarian Academy of Sciences Sofia Bg Honorary Professor Teesside University Uk New Zealand The University of Auckland Nz Peking University Shenzhen Bulgaria

ISBN: (纸本)9798350321104

The talk discusses briefly current challenges in artificial intelligence (AI), including: efficient learning of data (interactive, adaptive, life-long;transfer);interpretability and explainability;personalised predictive modelling and profiling;multiple modality of data (e.g. genetic, clinical, behaviour, cognitive, static, temporal, longitudinal);computational complexity;energy consumption;human-machine interaction. © 2023 IEEE.

关键词： Energy utilization

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Modeling Decisions for Artificial Intelligence 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Vicenç Torra Yasuo Narukawa Aïda Valls Josep Domingo-Ferrer

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Caching and Reproducibility: Making data Science experiments faster and FAIRer

arXiv

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arXiv 2022年

作者： Schubotz, Moritz Satpute, Ankit Greiner-Petter, André Aizawa, Akiko Gipp, Bela Chair for Data and Knowledge Engineering University of Wuppertal Wuppertal Germany FIZ Karlsruhe Leibniz Institute for Information Infrastructure Berlin Germany Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo Japan Chair for Scientific Information Analytics University of Göttingen Göttingen Germany

Small to medium-scale data science experiments often rely on research software developed ad-hoc by individual scientists or small teams. Often there is no time to make the research software fast, reusable, and open access. The consequence is twofold. First, subsequent researchers must spend significant work hours building upon the proposed hypotheses or experimental framework. In the worst case, others cannot reproduce the experiment and reuse the findings for subsequent research. Second, suppose the ad-hoc research software fails during often long-running computational expensive experiments. In that case, the overall effort to iteratively improve the software and rerun the experiments creates significant time pressure on the researchers. We suggest making caching an integral part of the research software development process, even before the first line of code is written. This article outlines caching recommendations for developing research software in data science projects. Our recommendations provide a perspective to circumvent common problems such as propriety dependence, speed, etc. At the same time, caching contributes to the reproducibility of experiments in the open science workflow. Concerning the four guiding principles, i.e., Findability, Accessibility, Interoperability, and Reusability (FAIR), we foresee that including the proposed recommendation in a research software development will make the data related to that software FAIRer for both machines and humans. We exhibit the usefulness of some of the proposed recommendations on our recently completed research software project in mathematical information retrieval. © 2022, CC BY.

关键词： Reusability

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On the usability of probably approximately correct implication bases

arXiv

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arXiv 2017年

作者： Borchmann, Daniel Hanika, Tom Obiedkov, Sergei Chair of Automata Theory Technische Universität Dresden Germany Knowledge & Data Engineering Group University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany National Research University Higher School of Economics Moscow Russia

We revisit the notion of probably approximately correct implication bases from the literature and present a first formulation in the language of formal concept analysis, with the goal to investigate whether such bases represent a suitable substitute for exact implication bases in practical use-cases. To this end, we quantitatively examine the behavior of probably approximately correct implication bases on artificial and real-world data sets and compare their precision and recall with respect to their corresponding exact implication bases. Using a small example, we also provide qualitative insight that implications from probably approximately correct bases can still represent meaningful knowledge from a given data *** Codes 03G10 68T27 Copyright © 2017, The Authors. All rights reserved.

关键词： Formal concept analysis

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Linking data Sovereignty and data Economy: Arising Areas of Tension 17

Linking Data Sovereignty and Data Economy: Arising Areas of ...

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17th International Conference on Wirtschaftsinformatik, WI 2022

作者： Lauf, Florian Scheider, Simon Bartsch, Jan Herrmann, Philipp Radic, Marija Rebbert, Marcel Nemat, André T. Schlueter-Langdon, Christoph Konrad, Ralf Sunyaev, Ali Meister, Sven Fraunhofer Institute for Software and Systems Engineering ISST Healthcare Dortmund Germany TU Dortmund Chair of Industrial Information Management Dortmund Germany Department of Economics and Management Karlsruhe Germany Fraunhofer Center for International Management and Knowledge Economy IMW Corporate Development in International Competition Division Leipzig Germany Witten/Herdecke University Institute for Digital Transformation in Healthcare GmbH Witten Germany T-Systems International GmbH Digital Solutions - Data Intelligence Hub Frankfurt Germany Claremont Graduate University Drucker School of Management Claremont United States KASTEL Security Research Labs Karlsruhe Germany Witten/Herdecke University Faculty of Health School of Medicine Witten Germany

In the emerging information economy, data evolves as an essential asset and personal data in particular is used for data-driven business models. However, companies frequently leverage personal data without considering individuals’ data sovereignty. Therefore, we strive to strengthen individuals’ position in data ecosystems by combining concepts of data sovereignty and data economy. Our research design comprises an approach to design thinking iteratively generating, validating, and refining such concepts. As a result, we identified ten areas of tension that arise when linking data sovereignty and data economy. Subsequently, we propose initial solutions to resolve these tensions and thus contribute to knowledge about the development of fair data ecosystems benefiting both individuals’ sovereignty and companies’ access to data. © 2022 17th International Conference on Wirtschaftsinformatik, WI 2022. All rights reserved.

关键词： Ecosystems

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Neuroinformatics, Neural Networks and Neurocomputers for Brain-inspired Computational Intelligence

Neuroinformatics, Neural Networks and Neurocomputers for Bra...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Nikola K Kasabov Fellow INNS College of Fellows Fellow RSNZ Doctor Honoris Causa Obuda University Budapest Founding Director KEDRI and Knowledge Engineering Auckland University of Technology Auckland New Zealand George Moore Chair Data Analytics Ulster University Derry the UK Visiting Professor IICT Bulgarian Academy of Sciences Sofia BG Honorary Professor Teesside University UK The University of Auckland NZ Peking University Shenzhen

The talk discusses briefly current challenges in artificial intelligence (AI), including: efficient learning of data (interactive, adaptive, life-long; transfer); interpretability and explainability; personalised predictive modelling and profiling; multiple modality of data (e.g. genetic, clinical, behaviour, cognitive, static, temporal, longitudinal); computational complexity; energy consumption; human-machine interaction.

关键词：

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第Sup1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Kings County Hospital Center Brooklyn NY 11203 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics

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