The rise of the machines: Artificial intelligence meets scholarly content

Abstract

Key points New forms of human/machine dialogue are emerging as robots understand vast amounts of content rather than simply indexing content as strings of characters. Recognizing strings of characters as entities (e.g. = names = authors) allows for meaningful associations between entities and reasoning over these relationships. Web‐scale adoption of the Semantic Web approach has been slow because it is too complex to implement and does not scale. User intent, discovered through conversational models of human–computer interaction, allows for a deeper understanding of exactly what researchers are looking for. Personal agents hold the promise of finding information that we will find useful before we have started to look for it. Publishers can use Academic Knowledge APIs to interpret academic user queries and find rich information from the Microsoft Academic Graph.