## 2 Related Work  
The semantic retrieval of physical models is the main objective of this work. Although many studies have developed reuse techniques focusing on system artefact representation, integrating more advanced methods based on artificial intelligence techniques remains a challenge [12]. The continuous evolution of information representation techniques for searching aims to enhance the accuracy and relevance of retrieval systems in order to provide users with more effective experiences. Notable approaches include deep learning models for text representations, which are capable of capturing high-level information; however, these demand extensive labelled data [13]. Additionally, the use of graphs and structured knowledge allows semantic relationships to be captured [14], although this usually requires external data sources [15]. In the context of text representation, recent studies have seen the use of word embeddings as a technique to encode texts as dense low-dimensional vectors [16]. These vectors represent the semantic and syntactic relationships between words, and are learned through unsupervised training on a text corpus such has Word2 Vec [17]. Word embeddings can be used to improve the accuracy of tasks such as text classification [18], sentiment analysis [19], and machine translation [20]. In order to retrieve information, this approach has been presented in [21] to calculate the similarity between a query table and a set of tabular datasets, although the implemented algorithm does not consider the similarity with the input text pattern and does not apply preprocessing to the input text, as there is only one term in each cell of the table. The application of Large Language Models (LLMs) such as OpenAI GPT-x, Google Bard, and Meta Llama to natural language processing tasks is a complete game-changer in the text processing arena; these models use transformers [22] as an underlying architecture to build relationships [23] between tokens derived from raw texts. Although these models can probabilistically understand and produce text, and have been used to perform tasks such as classification, content summarisation, entity recognition, question answering, translation, etc., they lack the ability to perform reasoning without the support of external tools; even more importantly for a retrieval system, they cannot trace the output to an input. In the context of model reuse, ref [24] introduced an approach for representing and retrieving Computer-Aided Design (CAD) models. This method involves the implementation of a mapping function designed to correlate the features of various CAD models. Additionally, in the domain of model representation, prior work is exemplified in [25; 26]. In these studies, the authors proposed a solution for representing components within models using a concept referred to as Physical Model Mappable Element (PMME). A PMME serves as a means to capture information in a manner that transcends specific programming languages, thereby enhancing cross-compatibility and language-independence. In [26], the concept of a metamodel paved the way for demonstrating the feasibility of transforming models to achieve the objective of reuse. The main limitation of these works is that it is necessary to define an ontology that contains the domain terminology and semantics in order to be able to reuse models. This requires significant effort, and there is no guarantee of all the terms that may be used in a query being included in the domain knowledge. Recently, interoperability-based approaches have been widely studied and developed to define interoperable engineering environments in which data, metadata, and information can be accessed through a standard service interface.