## 2 Materials and Methods
### Approach for the Fuzzy Classification of Documents into Topics  
With regard to the fuzzy classification of the document corpus into topics--which, in the present paper, is proposed but not yet implemented--the topics were identified during the use case analysis based on the predefined keywords of the seven UNEP/MAP-related themes listed in the UNESCO thesaurus [31], an RDF SKOS concept scheme without definitions, as reported in Table 1. Then, we identified \"definitions\" of each topic keyword, having the form of textual abstracts, in renowned and authoritative sources, as reported in Table 1, i.e., open domain websites. The pre-existing thesauri were enriched by adding those definitions in the web of data. The results are available both as linked data [32] and through a SPARQL endpoint [33]. After choosing the best performing combination of LLMs, similarity function, and chunk definitions, evaluated as explained in the next section, we are able to apply it to classify the whole collection into the distinct topics by considering the topics' definitions as queries. This way, for each topic, we will obtain a distinct ranked list of documents, and thus, in principle, a document will be assigned to multiple topics to a different extent, where the extent is the document's relevance score with respect to a topic. The more similar two ranked lists of two distinct topics are--that is, they have the same order and relevance score for the documents--the more the two topics are considered \"similar\" with regard to their descriptions in the collection. The fuzzy intersection of a pair of ranked lists (named \(A\) and \(B\)), which will be returned by querying the model with two topics, is the ranked list (\(AB\)) of documents at the cross-road of both the two topics. It is such that for each document \(d_{i}\) in the two ranked lists \(A\) and \(B\), the rank of document \(d_{i}\) is computed as the minimum of the relevance scores of the document in the two lists \(A\) and \(B\):  
\[RSV_{AB}(d_{i})=min(RSV_{A}(d_{i}),RSV_{B}(d_{i})\]  
This representation of the contents of documents resembles a knowledge graph in which the nodes correspond to the different topics, the relevant concepts of the KH, and their corresponding ranked lists are the manifestations of the concepts in the documents of the collection. On the other hand, the edges of the knowledge graph represent the similarity relationships between pairs of connected concepts at the nodes, i.e., concepts at the cross-road of pairs of topics, the manifestation of which is the ranked list of documents obtained by computing the fuzzy intersection of the ranked lists of the connected nodes. \begin{table}
\begin{tabular}{l l} \hline \hline
**Topic Keyword** & **Definition Source** \\ \hline Climate change & United Nations (UN) \\ \hline Marine biodiversity & UN \\ \hline Sustainability and blue economy & UN \\ \hline Pollution & National Geographic \\ \hline Marine spatial planning & EU Commission \\ \hline Fishery and aquaculture & FAO \\ \hline Governance & UN Dev. Progr. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Topics and their source of definitions. The bottom part of Figure 1 reports the operations necessary to generate the knowledge graph. By conceiving such a kind of \"knowledge graph\", we aim to mirror the fusion of the manifestations of the relevant concepts dealt with in the collection. Figure 2 sketches the visual representation of the UNEP-MAP knowledge graph. Creating such a knowledge graph can be costly in terms of time elapsed, since it involves evaluating the seven queries that represent the topics in the entire collection and then computing the fuzzy intersections of all pairs of ranked lists. Nevertheless, we do not need quick execution since this is performed once and for all in a preliminary off-line phase in order to prepare the KH for subsequent browsing by final users. This is the equivalent of an \"indexing\" process of a collection of documents to aid in their retrieval by topics.