IEEE Transactions on Knowledge and Data Engineering
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A Knowledge Base (KB) stores, organizes, and shares information pertinent to entities (i.e. KB nodes) such as people, organizations, and events. A large KB system, such as Wikipedia, relies on human curators to create and maintain the content in the systems. However, it has become challenging for human curators to sift through the rapidly growing amount of information and filter out the information irrelevant to a KB node. The area of Knowledge Base Enhancement (KBE) aims to explore and identify automatic methods to assist human curators to accelerate the process. KBE can be viewed as a special case of Information Filtering (IF). However, the lack of high-quality labelled data introduces a major challenge to train a satisfying model for the task. Transfer learning provides solutions to the problem and has explored applications in the area of text mining, whereas direct application to KBE or IF remains absent. Transfer learning is a research area in machine learning, emphasizing the reuse of previously acquired knowledge to another applicable task. The method is particularly useful in the situations where labeled instances are absent or difficult to obtain. To accelerate the growth of a KB, a transfer learning approach enables leveraging the heuristics and models learned from one KB node to another. For example, reusing the learned filtering models from Willie Nelson, a famous country singer, to Eddie Rabbitt, another country singer. Transfer learning requires three components: the target task (e.g. the problem to be solved), the source task(s) (e.g. auxiliary data, previously studied problem), and criteria to select appropriate source tasks. The objectives of my dissertation are twofold. First, it explores methods to identify informative source nodes from which to transfer. Second, it constructs a knowledge transfer network to represent the transfer learning relationship between KB nodes. This proposed research applies a transfer learning method -- Segmented Transfer (ST) -- and a knowledge representation -- Knowledge Transfer Network (KTN) -- to approach the area of KBE. The primary research questions include: What are the transferable objects in information filtering algorithms? What are the KB nodes of high transferability? What are the factors that determine the transfer learning relationship? Does it manifest on a knowledge transfer network representation? This interdisciplinary research crosses the study area of information filtering, machine learning, knowledge representation, and network analysis. This proposal motivates the problem of KBE, discusses the research methodology and proposed experiments, and reviews related works in information filtering and transfer learning. This line of research hopes to extend the application of transfer learning to KBE and to explore a new dimension of IF. The proposed ST and KTN intends to bring interdisciplinary approaches in the emerging field of KBE.