Evaluation of a Semantic Search Approach based on AMR for Information Retrieval in Image Exploitation

As part of the unprecedented wealth of data available nowadays, semi-formal reports in the domain of remote sensing can convey information important for decision making in structured and unstructured text parts. For such reports, often kept in large data management systems, targeted information retrieval remains difficult, e.g., the extraction of texts parts relevant to a question posed via natural language. The work presented in this paper therefore aims at finding the relevant documents in data management systems and extracting their relevant content parts based on natural language questions. For this purpose, an approach for semantic information retrieval based on Abstract Meaning Representation (AMR) is adapted, extended and evaluated for the considered domain of remote sensing and image exploitation. In detail, two different metrics used in AMR, Smatch and SemBleu, are compared for their suitability in an AMR-based search. The first results presented in this paper are promising. In addition, more detailed experiments regarding the performance of the metrics under differently formulated yet semantically equivalent questions reveal interesting insights into their ability for semantic comparison.