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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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Advancing Robotic Perception with Perceived-Entity Linking

  • 会议论文

  • 发表日期：

    2025年

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  • Mark Adamik,
  • Romana Pernisch,
  • Ilaria Tiddi,
  • Stefan Schlobach

摘要

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.