Revisiting concepts of topicality and novelty - A new simple graph model that rewards and penalizes based on semantic links

Research in Information Retrieval (IR) experienced a paradigm shift from first having too few documents to search from to now having way too many of them. When users have trouble finding relevant documents, they tend to become frustrated and give up searching. Scholars have attempted to reduce instances of search frustration via query expansion, information filtering, and incorporating user feedback. However, these approaches are not effective as users still experience a period of frustration before getting more relevant results. The aim of this conceptual paper is to explore possible improvements to the field by revisiting two fundamental concepts: topicality and novelty. First, we elaborate various issues with existing IR models in capturing these two concepts. Second, we illustrate a potential improvement to these issues: namely, a new simple graph space model with new topicality and novelty measures that can better capture these features of a document based on rewards and penalties for corresponding matching and missing semantic links. Lastly, we demonstrate a walk-through example using the new graph-based IR model.