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Collaborative Research Center SFB 876 - Providing Information by Resource-Constrained Data Analysis

The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.

October  6,  2022  16:15

Managing Large Knowledge Graphs: Techniques for Completion and Federated Querying

Abstract - Knowledge Graphs (KGs) allow for modeling inter-connected facts or statements annotated with semantics, in a semi-structured way. Typical applications of KGs include knowledge discovery, semantic search, recommendation systems, question answering, expert systems, and other AI tasks. In KGs, concepts and entities correspond to labeled nodes, while directed, labeled edges model their connections, creating a graph. Following the Linked Open Data initiatives, thousands of KGs have been published on the web represented with the Resource Description Framework (RDF) and queryable with the SPARQL language through different web services or interfaces. In this talk, we will address two relevant problems when managing large KGs. First, we will address the problem of KG completion, which is concerned with completing missing statements in the KG. We will focus on the task of entity type prediction and present an approach using Restricted Boltzmann Machines to learn the latent distribution of labeled edges for the entities in the KG. The solution implements a neural network architecture to predict entity types based on the learned representation. Experimental evidence shows that resulting representations of entities are much more separable with respect to their associations with classes in the KG, compared to existing methods. In the second part of this talk, we will address the problem of federated querying, which requires access to multiple, decentralized and autonomous KG sources. Due to advancements in technologies for publishing KGs on the web, sources can implement different interfaces which differ in their querying expressivity. I will present an interface-aware framework that exploits the capabilities of the member of the federations to speed up the query execution. The results over the FedBench benchmark with large KGs show a substantial improvement in performance by devising our interface-aware approach that exploits the capabilities of heterogeneous interfaces in federations. Finally, this talk will summarize further contributions of our work related to the problem of managing large KGs and conclude with an outlook to future work.

Short bio - Maribel Acosta is an Assistant Professor at the Ruhr-University Bochum, Germany, where she is the Head of the Database and Information Systems Group and a member of the Institute for Neural Computation (INI). Her research interests include query processing over decentralized knowledge graphs and knowledge graph quality with a special focus on completeness. More recently, she has applied Machine Learning approaches to these research topics. Maribel conducted her bachelor and master studies in Computer Science at the Universidad Simon Bolivar, Venezuela. In 2017, she finished her Ph.D. at the Karlsruhe Institute of Technology, Germany, where she was also a Postdoc and Lecturer until 2020. She is an active member of the (Semantic) Web and AI communities, and has acted as Research Track Co-chair (ESWC, SEMANTiCS) and reviewer of top conferences (WWW, AAAI, ICML, NEURIPS, ISWC, ESWC).


6th International Summer School 2022 on Machine Learning under Resource Constraints

From September 12-16, 2022, the Collaborative Research Center 876 (CRC 876) at TU Dortmund University hosted its 6th International Summer School 2022 on Resource-aware Machine Learning. In 14 different lectures, the hybrid event allowed the approximately 70 participants present on site and more than 200 registered remote participants to enhance their skills in data analysis (machine learning, data mining, statistics), embedded systems, and applications of the demonstrated analysis techniques. The lectures were given by international experts in these research fields and covered topics such as Deep Learning on FPGAs, efficient Federated Learning, Machine Learning without power consumption, or generalization in Deep Learning.

The on-site participants of the Summer School were CRC 876 members and international guests from eleven different countries. In the Student's Corner of the Summer School - an extended coffee break with poster presentations - they presented their research to each other, exchanged ideas and networked with each other. The Summer School’s hackathon put the participants' practical knowledge of machine learning to the test. In light of the current COVID-19 pandemic, participants were tasked with identifying virus-like nanoparticles using a plasmon-based microscopy sensor in a real-world data analysis scenario. The sensor and the analysis of its data are part of the research work of CRC 876. The goal of the analysis task was to detect samples with virus-like particles and to determine the viral load on an embedded system under resource constraints.

Details and information about the Summer School can be found at: https://sfb876.tu-dortmund.de/summer-school-2022/

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