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UID:pretalx-euroscipy-2026-KFBJXK@pretalx.com
DTSTART;TZID=CET:20260720T152000
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DESCRIPTION:Mapping freeform research affiliations to persistent identifier
 s such as [ROR (Research Organization Registry)](https://ror.org/)  is har
 der than it looks. Institution names appear in many forms such as abbrevia
 tions\, alternate spellings\, local languages\, or legacy names\, thus mak
 ing a reliable mapping difficult to achieve at scale.\n\nIn this talk\, we
  present a semantic retrieval pipeline that reframes institution identific
 ation as a search problem rather than a string-matching task. Our system c
 ombines named entity recognition to extract institution entities\, dense e
 mbeddings to represent their semantic meaning\, and vector search to retri
 eve the most likely ROR matches. This approach allows us to handle noisy\,
  incomplete\, and multilingual inputs while remaining resilient to variati
 on in how institutions are referenced.\n\nBy treating institution matching
  as semantic retrieval\, we improve recall and robustness without relying 
 on heuristics or on a continuous expanding rule-based approach. The system
  scales naturally as new institutions are added and as naming conventions 
 evolve\, making it well suited for the dynamic research environment.\n\nWe
  will share implementation details\, evaluation results\, and practical le
 ssons learned from deploying this pipeline in a real-world production sett
 ing.
DTSTAMP:20260603T201738Z
LOCATION:Room 1.19 (Ground Floor\, Shannon)
SUMMARY:Finding the Right ROR: Semantic Search for Research Institutions - 
 Diogo Rodrigues\, Frank Sauerburger
URL:https://pretalx.com/euroscipy-2026/talk/KFBJXK/
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