Keyword-based search is what we all do when we enter words in databases and search sites such as Google and Bing. Suggestions is related “stuff” the computer presents us with when we search YouTube, Amazon, iTunes and other such sites. Link-based browsing differs from both. Unlike keyword-based search, there is no underlying assumption that the user has a clear idea of what he wants to find when he begins his search. Unlike suggestions, the user is much more in control of what relevant information will be suggested. And unlike both, in link-based browsing, the emphasis is on specific concepts and their possible relationships rather than on “containers” (web pages, videos, songs, books) that refer to those concepts.
Link-based browsing is suited to situations where one does not have a clear view of what they are looking for, yet they do hope to find possibly related information they are not aware of;
in other words, to situations that often arise in a scientific discovery or brainstorming context where a good understanding of an area of research, when combined with the uncovering of non-obvious connections has the potential to create insights that lead to new discoveries.
One tool that uses linked-based browsing to support discovery in the life sciences is Vizit. Search in Vizit works by combining a specific concept of interest, say the “MTOR” gene, with a concept class of interest, say “Pathways”. So a search of MTOR and Pathways returns all the Biological Pathways in which MTOR has some role. Providing additional filters allows the user to further specify that role and ultimately to access the underlying scientific bibliography where the specific information is mentioned. By removing assumptions and allowing the user to easily direct the search based on unfiltered system feedback, linked-based systems such as Vizit offer a better way of capturing and understanding the user’s context and supporting them in developing novel insights and scientific hypotheses.