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Creation and Maintenance of Query Expansion Rules
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| Stefania Castellani, Aaron Kaplan, Frédéric Roulland, Jutta Willamowski, Antonietta Grasso |
| ICEIS 2009, 11th International Conference on Enterprise Information Systems, Milan, Italy, 6-10 May, 2009 |
In an information retrieval system, a thesaurus can be used for query expansion, i.e. adding words to queries in order to improve recall. We propose a semi-automatic and interactive approach for the creation and maintenance of domain-specific thesauri for query expansion. Domain-specific thesauri are especially required in highly technical domains where the use of general thesauri for query expansion introduces more noise than useful results. Our semi-automatic approach to thesaurus creation constitutes a good compromise between fully manual approaches, which produce high-quality thesauri but at a prohibitively high cost, and fully automatic approaches, which are cheap but produce thesauri of limited quality. This article describes our approach and the architecture of the system implementing it, named Cannelle. It exploits user query logs and natural language processing to identify valuable synonymy candidates, and allows editors to interactively explore and validate these candidates in the context of a domain-specific searchable knowledge base. We evaluated the system in the domain of online troubleshooting, where the proposed method clearly yielded an improvement in the quality of the search results obtained.
This paper received the best paper award in the area of human-computer interaction at the ICEIS conference 2009
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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