Classification of personal knowledge graphs based on their methods of construction.
Abstract
Information that can encapsulate a person's daily life and its different aspects provides insightful knowledge. This knowledge can prove to be more useful than general knowledge for improving personalized tasks. When it comes to storing such knowledge, personal knowledge graphs (PKGs) come in as handy saviors. PKGs are knowledge graphs which store details that are pertinent to a user but not, in general, useful to the rest of humanity. Conversational agents can access these PKGs to answer queries related to the user's day-to-day life, whereas recommender systems can harness the knowledge stored in PKGs to make personalized suggestions. Despite the immense applicability of PKGs, there has not been significant research in this area. We present an extensive review of PKGs. We categorize them according to the domains in which they are most relevant; in particular, we highlight the use of PKGs in medicine, finance, and education and research. We also categorize the different ways of constructing a PKG based on the source of data required for such constructions. Furthermore, we discuss the limitations of PKGs and suggest directions for future work.
This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Artificial Intelligence