# A Hybrid Approach to Literature-Based Discovery: Combining Traditional Methods with LLMs
## 1 Introduction  
The increasing volume of the scientific literature presents a challenge for knowledge discovery. The manual synthesis of information cannot keep up with the rapid growth of publications across diverse fields, leading to missed connections. Literature-based discovery (LBD) is a data mining approach capable of connecting disparate literature studies [1] and uncovering new connections within specialized fields, addressing situations where the volume of publications cannot be processed by a human (e.g., [2, 3]). The original LBD technique, the ABC model [4]--due to the connection between \(A\) and \(C\) it proposes based on known relations between \(A\) and \(B\) and \(B\) and \(C\)--hinges on the accuracy and quality of the relations extracted from texts, as well as the ability to filter the resulting candidate hidden knowledge pairs (CHKPs) to remove uninformative candidates [5]. This work introduces a hybrid data analysis approach, which explores the use of large language models (LLMs) to address both of these points. Early LBD works utilised simple text mining techniques, such as word co-occurrence in titles, to define relations [6]. This method was effective for investigating suspected (closed) connections where the literature could be reduced to titles containing \(A\) or \(C\) and only the linking term \(B\) was sought. However, even when the relations were refined, the approach had a tendency to generate an overwhelming number of CHKPs when used in open mode, where all connections from every term are followed for two steps [7]. The need to reduce the quantity of CHKPs generated by open LBD has fuelled research into enhancements to existing methods for effective data mining. Prior approaches haveincluded the following: (a) the removal of terms based on frequency (e.g., [8; 9]), (b) the mapping of terms to specialized lexicons such as the Unified Medical Language System (UMLS) [10] to prevent connections via ambiguous terms [11], (c) the removal of terms based on semantic type (e.g., [12; 13]), (d) the restriction of the type of discovery (e.g., cancer biology [14] or protein interaction [15]), (e) the restriction of relations to be connected [16], and (f) the re-ranking of generated CHKPs (e.g., [17; 18; 19; 20; 21]). However, the exponential growth of academic publications [22] requires a constant refinement of these approaches. Recent advances in deep learning have improved the performance of many natural language processing (NLP) tasks in the biomedical field [23], including LBD (for an overview, see [24]). While pre-trained language models have been used for LBD [25], many successful applications of data mining, particularly in areas such as drug repurposing, have predominantly relied on knowledge graph-based deep learning, with the knowledge provided by existing tools, such as SemMedDB [26] (e.g., [3]), the Global Network of Biomedical Relationships [27] (e.g., [28]), or named entity extraction (e.g., [29]). The use of large, generative, language models (LLMs) has, however, been relatively low for the purpose of LBD since they have a propensity for adhering to information they were trained on rather than proposing new connections [30]. Through the vast quantity of training data, LLMs have access to an enormous amount of knowledge. While this appears to be a limitation when proposing new connections, it suggests their suitability for use within CHKP filtering. Since a large quantity of proposed CHKPs represent background, well-known knowledge (which, due to being widely familiar, does not appear explicitly in publications [25]), we propose that LLMs could be used to filter these CHKPs. By instructing them to act as judges--a modification of the LLM-as-a-judge paradigm--they can assess each CHKP against their training data to determine whether it represents generally known, background knowledge. This theoretically distinct approach goes beyond conventional filtering methods that rely on frequency or semantic constraints.