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.