While navigation or browsing is sufficient for small hypertext systems, more powerful information retrieval techniques become very important in large scale hypertext databases. Content queries can be used to retrieve the contents of nodes while structural queries can be used to retrieve subgraphs of the hypertext network that match a given pattern. Many researchers have investigated the possibilities of separating index information from contents thus forming an index space (or concept network) on top of a content space (or document network). These would not only facilitate IR but also accommodate dynamic linking and independent maintenance of the two networks.
Query languages are being extended to perform structural queries. These extensions include the notions of quantifiers, recursive operators, aggregation, and improved semantics. Research has also been carried out in the use of belief networks or Bayesian inference networks for hypertext-based IR. Some researchers have explored aggregating hypertext networks into semantic or hierarchical clusters. Very little work has been done in the area of merging Artificial Intelligence with hypertext. A combination of inference-based IR and knowledge-based hypertext could greatly facilitate browsing and searching. More research is required in the integration of querying techniques and browsing mechanisms. Experiments are required to measure the effectiveness of these IR techniques.