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2025 | OriginalPaper | Buchkapitel

Advancing Robotic Perception with Perceived-Entity Linking

verfasst von : Mark Adamik, Romana Pernisch, Ilaria Tiddi, Stefan Schlobach

Erschienen in: The Semantic Web – ISWC 2024

Verlag: Springer Nature Switzerland

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Abstract

The capabilities of current robotic applications are significantly constrained by their limited ability to perceive and understand their surroundings. The Semantic Web aims to offer general, machine-readable knowledge about the world and could be a potential solution to address the information needs of robotic agents. We introduce the Perceived-Entity Linking (PEL) problem as the task of recognizing entities and linking the sensory data of an autonomous agent to a unique identifier in a target knowledge graph. We provide a formal definition of PEL, and propose a PEL baseline based on the YOLO object detection algorithm and a conventional entity linking method as an initial attempt to solve the task. The baseline is evaluated by linking the concepts contained in MS COCO and VisualGenome datasets to WikiData, DBpedia and YAGO as target knowledge graphs. This study makes a first step in allowing robotic agents to leverage the extensive knowledge contained in general-purpose knowledge graphs.

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Vorheriges Kapitel Understanding SPARQL Queries: Are We Already There? Multilingual Natural Language Generation Based on SPARQL Queries and Large Language Models

Nächstes Kapitel PreAdapter: Pre-training Language Models on Knowledge Graphs

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Titel: Advancing Robotic Perception with Perceived-Entity Linking
verfasst von: Mark Adamik
Romana Pernisch
Ilaria Tiddi
Stefan Schlobach
Verlag: Springer Nature Switzerland
Buch: The Semantic Web – ISWC 2024
Print ISBN: 978-3-031-77849-0

Electronic ISBN: 978-3-031-77850-6

Copyright-Jahr: 2025
DOI: https://doi.org/10.1007/978-3-031-77850-6_11

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