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Tutorial at the KnowGraphs Winter School 2022

Maribel Acosta presented a tutorial on "Querying Federations of Knowledge Graphs" at the KnowGraphs Winter School 2022.

The Marie Curie ITN-ETN KnowGraphs is a training network that focuses on research about Knowledge Graphs.  The Winter School 2022 was held online from 31.01 to 02.02.2022, and comprised 9 tutorials and 3 data challenges. 

The tutorial by Maribel Acosta included an overview on the foundations of federated query processing, adaptive techniques for decentralized knowledge graphs, and a framework for query heterogeneous federations of knowledge graphs. The latter is part of a recent publication accepted at The Web Conference 2022, the prime conference for research on the web. 

Tutorial Overview

Federations of Knowledge Graphs (KGs) are composed of multiple, decentralized, autonomous sources that expose KGs on the web. To query such KGs, Federated Query Engines (FEQs) implement query processing techniques for reducing the execution time of queries while maximizing the answer completeness.

The first part of this talk will introduce the general architecture of FQEs with an overview of (i) source selection and query decomposition, (ii) query planning, and (iii) query execution techniques. We will learn that due to the decentralized and autonomous aspects of federated KGs, query planning and execution techniques can fail if the runtime conditions are not taken into account. To address this, we will present Adaptive Query Processing (AQP) tailored to federated KGs. We will focus on two types of adaptivity that produce results incrementally and address query performance issues due to network delays or suboptimal plans. In addition, this talk will present a metric for benchmarking querying approaches that produce answers incrementally. 

Federations of Knowledge Graphs

In the second part of this talk, we will concentrate on heterogeneous KG federations. Heterogeneity in query processing can come in different forms, e.g., data models, languages, hardware, interfaces, etc. In this talk, we will focus on heterogeneous federations composed of interfaces with different expressivity. The Linked Data Fragments (LDFs) Framework defines the expressivity of the interfaces in terms of the class of SPARQL expressions they can evaluate and the metadata they provide. This talk will then present the challenges that FQEs face when querying KG federations with heterogeneous LDF interfaces. To address these challenges, in our most recent work, we propose an interface-aware framework that exploits the capabilities of the member of the federations to speed the query execution. This talk will conclude with an outlook on other research problems in the context of federations of KGs.

Framework for Heterogeneous Federations

Relevant Links 

Link to the Slides: zenodo.org/record/5972367

Pre-print: arxiv.org/abs/2102.03269 

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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