Co-embedding Multi-type Data for Information Fusion and Visual Analytics.

This paper proposes a novel interactive system for exploratory document search in multi-type data sets that employs a data fusion approach. The system, designed to visualize different object types collectively and clearly display their semantic proximity, utilizes a co-embedding technique for knowledge fusion of multi-type data and projects the various types of objects onto a common lower-dimensional space. This produces a more informed representation and visualization that shows both in-type and across-type semantic proximity between objects. The system enables users’ exploration of multi-type document data by providing embedding-based scatter plots to visualize semantic relations within and across object types. In addition, based on user relevance feedback of displayed objects, the system offers recommendations of relevant objects. We have demonstrated the effectiveness of the proposed system and the underlying embedding method through comparison experiments with the proposed fusion-based approach.