The legislative process starts with a deep analysis of the existing regulations at European and national levels to avoid conflicts and fostering the into force norms. Also the Constitutional Court decisions play a fundamental role in this analysis for checking the compliance with the constitutional framework and for including the inputs coming from this relevant court in the law-making process. Finally, it is also significant to compare the forthcoming proposal with the already presented bills regarding the same topic. This comparison is crucial to avoid overlapping and to coordinate the democratic dialogue with the different parties. In this light, this paper presents an unsupervised approach for calculating similarity between heterogeneous documents annotated in Akoma Ntoso XML, with the aim to support the information retrieval of similar documents using thematic taxonomy used in legal domain. The prototype has been developed for answering to a call for manifestation of interests launched by the Chamber of Deputy of Italy in order to adopt hybrid AI in the legislation process. It uses a completely unsupervised approach based on Sentence Transformers, meaning that neither annotated data or any fine-tuning process is required.