How could PageRank be used to rank paragraphs in relevance to keywords?
If I understand, you have a set of paragraphs, and each paragraph is tagged with keywords. This defines a mapping from paragraph to keyword and vice-versa (an index and a reverse index). You can create a graph of paragraphs and keywords, but all of the edges are undirected (or they all have the same direction). Paragraphs only connect with keywords, and keywords only connect with paragraphs.

To establish relationships between paragraphs, you could define a transitive equivalence relation such as "all paragraphs that connect to the same keyword are now connected to each other". This would allow you to rank the paragraphs that contain a given keyword by their number of connections to other paragraphs, but it may not be an appropriate measure of relevance.

For PageRank to be most helpful, I think you need directional relationships (e.g. if "see page 123" was mentioned in a paragraph). To better represent the complexity of your data, creating additional relationships between keywords (e.g. semantic meaning, lexical relationships) and creating additional relationships between paragraphs (e.g. document classification) may help. Natural Language Processing with Python discusses these topics.