Building a morpho-semantic knowledge graph for Arabic information retrieval

In this paper, we propose to build a morpho-semantic knowledge graph from Arabic vocalized corpora. Our work focuses on classical Arabic as it has not been deeply investigated in related works. We use a tool suite which allows analyzing and disambiguating Arabic texts, taking into account short diacritics to reduce ambiguities. At the morphological level, we combine Ghwanmeh stemmer and MADAMIRA which are adapted to extract a multi-level lexicon from Arabic vocalized corpora. At the semantic level, we infer semantic dependencies between tokens by exploiting contextual knowledge extracted by a concordancer. Both morphological and semantic links are represented through compressed graphs, which are accessed through lazy methods. These graphs are mined using a measure inspired from BM25 to compute one-to-many similarity. Indeed, we propose to evaluate the morpho-semantic Knowledge Graph in the context of Arabic Information Retrieval (IR). Several scenarios of document indexing and query expansion are assessed. That is, we vary indexing units for Arabic IR based on different levels of morphological knowledge, a challenging issue which is not yet resolved in previous research. We also experiment several combinations of morpho-semantic query expansion. This permits to validate our resource and to study its impact on IR based on state-of-the art evaluation metrics.