A Logic-based Framework for Characterizing Nexus of Similarity within Knowledge Bases

Abstract

Complex challenges frequently necessitate the establishment of significant connections among diverse entities. For instance, selecting the optimal CV, finding a suitable apartment or deciphering the causal factors behind a specific disorder are just a few examples, and each of them requires deep understanding of what characterizes a set of entities. In addition to the aforementioned examples, having to recognize similarities between entities is a recurring phenomenon in a multitude of real-world scenarios. Researchers from different fields have presented various methodologies over the last century to evaluate these similarities, commonly called similarity. In recent times, momentum has increased with the advent of “Google Sets”, leading to the fervent development of strategies to amplify a given set of entities while preserving their original shared interconnected properties. As a consequence, current methodologies can encompass, in a way or another, relevant interconnected properties shared by entities, a concept we term the nexus of similarity. Machines have demonstrated considerable prowess in handling similarity evaluations, often returning numerical scores as a result, and set expansions, thus giving the end user the opportunity to observe entities similar to those he was looking for. However, there is a notable gap in formally characterizing the nexus of similarity in a way that is intelligible by machines and interpretable by human intellect, especially considering that the attempts that have gained the most traction thus far are often bound to cases very specific, such as those made with respect to RDF graphs. To address these gaps, our endeavor contributes significantly to the existing literature. We aim to construct a novel framework grounded in logical constructs, designed to systematically and autonomously delineate the nexus of similarity. Our framework extends not only to pairs of entities but also to sets of tuples of entities, which we term anonymous relations, within a knowledge base. Furthermore, our analysis encompasses an in-depth examination of the computational complexity inherent in the proposed framework. Such an investigation affords a thorough insight into its feasibility and a subsequent evaluation of scalability. Both are critical components for the framework’s practical application. In summary, our study pioneers a novel, knowledge-driven approach capable of characterizing nexus of similarity and that can be used as a means to perform entity set expansion in a manner clear and intelligible to humans. Two of the principal components integral to our framework will be the semantic resources, specifically selective knowledge bases, that in their essence are knowledge bases equipped with a particular supplementary algorithm, and the explanation languages, of which we will take into consideration one in particular, which in our opinion has all the ideal characteristics to be considered a language suitable to reveal the nexus of similarity as best as possible. During the work, we will also justify our design choices. With the help of these means we aim to fill the perceptible gap in the characterization of nexus of similarity. At the same time, we will show how some of the current approaches to entity set expansion do not notice that by their very nature this kind of expansions should take the form of a taxonomy rather than a chain. To resolve this other gap, we will introduce the concept of expansion graph.