A Review of “Semantic Web and Google’s Knowledge Graph: The Changing Landscape of Search” by Mark Bialkowski

As computing power increases, the amount of data collected, and the relationships that can be drawn from that data, have continued to evolve. In Ken Fujiuchi’s Semantic Web and Google’s Knowledge Graph: The Changing Landscape of Search workshop at WNYLRC we discussed how that continued evolution is not only changing the way we search but how it is changing the way information about us is searched for and used.

Based on how metadata is arranged and how much it can now be sifted and sorted through by computer programs, there are now search engines, applications, personal assistants (like Siri and Cortana), and other data aggregators that are becoming increasing aware of the relationships between the data in one place, or record, and the the data that exists in other places. It almost goes without saying that this use of linked data has, in many cases, enabled faster and more relevant searching. The advent of the semantic web has basically enabled the automatic generation of see also references on almost any subject, including yourself.

However, what stands in the way of some of the technology’s full potentiality being expressed has been the lack of standards in regards to how data is put online. Ken Fujiuchi continued our discussion on the semantic web by highlighting how RDF (resource description and framework), the RDF Query Language, BIBFRAME, and RDA may offer opportunities to make the arrangement of metadata, and the metadata itself, more useful. Adding an element of standardization will make it easier for programs to retrieve information in a more relevant, timely, and, most importantly, a relationally meaningful way, unlocking all sorts of capabilities.

A quick demonstration of Google’s Knowledge Graph and Wikidata by Ken Fujiuchi quickly illustrated the point of how data that is actually created by us and data about us, aggregated from multiple places by metacrawlers, were all interacting with each other in such way as to reveal relationships. In the near future, if not already partially in the present, Siri or Cortana will not only know who your contacts are but will easily be able to determine what your favorite genre to read is, who your favorite authors are, what your favorite journals are, what your favorite color is, all without you, personally, entering much data at all. Perhaps more importantly, a future Siri or Cortana product may even be able to tell you why you, seemingly, like the things you do.