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    SANKOFA – Semantic Annotation of Knowledge Featuring Akoma Ntoso

    by Monica Palmirani 02/08/2019 04:30 PM GMT

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          The SANKOFA project will produce a web application (and an API) capable to mark up UN resolutions using the XML vocabulary Akoma Ntoso, to qualify the paragraphs according to the roles they play inside the structure (e.g., preambular or operative) and to classify the semantic aspects of all provisions (e.g. references). We also plan to qualify several peculiar textual parts (e.g. the specification of persons, organizations, dates, quantities, actions, and events). We plan to employ a hybrid solution using NLP techniques to detect structure, references, presentational parts, annexes, inline elements (e.g. symbols, committees, sessions, etc). We use parsers, vocabularies, Name-Entity-Relationship models, regular expressions, patterns, frames, and deep learning techniques to detect the knowledge that can be extracted from the text, and to represent it through semantic annotations.
          In addition to the qualifications of the textual structures and the semantics of relevant fragments, we plan to associate the correct conceptual classes from relevant ontologies (e.g., UNDO, SDGIO, UNBIS) to the corresponding fragments, and to create assertions and relationships (e.g. the decision that was taken, the timing of the decision, which actors were involved, which results are expected). The FRBR model and the ALLOT Top Level Classes are the two fundamental pillars in our approach. Assertion and relationships will be serialized in RDF according to Linked Open Data (LOD) principles. 

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          Monica Palmirani, Fabio Vitali, Silvio Peroni, Aldo Gangemi, Andrea Nuzzolese - UNIBO

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            Judge review 05/24/2019 Completed
            on 05/28/2019
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