  The large volume of scientific publications is likely to have hidden
knowledge that can be used for suggesting new research topics. We propose an
automatic method that is helpful for generating research hypotheses in the
field of physics using the massive number of physics journal publications. We
convert the text data of titles and abstract sections in publications to a
bipartite graph, extracting words of physical matter composed of chemical
elements and extracting related keywords in the paper. The proposed method
predicts the formation of new links between matter and keyword nodes based on
collaborative filtering and matter popularity. The formation of links
represents research hypotheses, as it suggests the new possible relationships
between physical matter and keywords for physical properties or phenomena. The
suggested method has better performance than existing methods for link
prediction in the entire bipartite graph and the subgraph that contains only a
specific keyword, such as `antiferromagnetism' or `superconductivity.'
