Graph theory is a well-studied discipline as are the fields of natural language processing and information retrieval. Traditionally, these areas of study have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, as recent research work has shown, these disciplines in fact are intimately connected, with much variety in the way that natural language processing and information retrieval applications find efficient solutions within graph-theoretical frameworks.

In a cohesive text, language units – whether they are words, phrases, or entire sentences – are connected through various relationships, which contribute to the overall meaning and maintain the cohesive structure and discourse unity of the text. Since the early stages of artificial intelligence, associative or semantic networks have been proposed as representations that enable the storage of such language units and their interconnecting relationships, which allow for a variety of inference and reasoning processes that simulate functionalities of the human mind (Sowa 1983). The symbolic structures that emerge from these representations correspond naturally to graphs – in which text constituents are represented as vertices and their interconnecting relationships form the edges in the graph.

Many text-processing applications can be modeled by means of a graph. These data structures have the capability to encode naturally the meaning and structure of a cohesive text and to follow closely the associative or semantic memory representations.