Search today is less about keywords and more about entities as is made clear (again) in this examination by Barbara Starr of Google patents.
Structured Data & The SERPs: What Google’s Patents Tell Us About Ranking In Universal Search (Search Engine Land, May 29)
Google packs into search results knowledge panels, answers, images. Much of this derives from use of structured data and identification of entities involved. One patent quoted notes that, “In some implementations, search results are retrieved from a data structure. In some implementations, the data structure also contains data regarding relationships between topics, links, contextual information, and other information related to the search results that the system may use to determine the ranking metrics.”
Starr describes with examples four entity-specific metrics. “The patent provides strong evidence that semantic web technology is being used as background context for the definitions of the metrics and the environment in which they are framed.”
Lastly, we learn that thare are “different algorithms for different screen areas”; ie., different displays for different devices.
Google explains in this patent a prototype for surfacing content from structured databases.
How Google May Index Deep Web Entities, by Bill Slawski, SEO by the Sea (April 5)
Bill Slawski presents this takeaway
If you’ve been looking for a connection between the SEO of web-page Crawling, and the use of Data from sources like Knowledge-bases, this paper describes such a connection – using data from a knowledge-base such as freebase to query the content of a deepweb database, such as an ecommerce site where content doesn’t surface to be crawled unless it is queried first.
Microsoft identifies entities and expands on them to improve Bing search results – or so it seems from this patent – “Query Expansion, Filtering and Ranking for Improved Semantic Search Results Utilizing Knowledge Graphs”.
How Bing May Expand Queries Based Upon Finding Entities Within Them, Bill Slawski, SEO by the Sea (April 3)
“The patent is telling us that it might provide improved search results by expanding queries using information about entities involved.”
Search engines today – especially Google and Bing – seek to identify entities and their relationships. This posting distinguishes between implicit and explicit entities. Explicit is known from structured markup; implicit is inferred from the text on the page.
Demystifying The Knowledge Graph, Barbara Starr, Search Engine Land (Sep 2)
Posting has advice for SEO people for optimizing their pages for recognition by the Knowledge Graph.
Semantic search technology is being employed by search engines to produce better answers. Semantic means meaning – to respond to the meaning of the query rather than the exact words. This deployment has been gradual, and mostly (it seems) is directed to helping us shop. This article by Barbara Starr
Is Google Hijacking Semantic Markup/Structured Data? (Search Engine Land, Jan 17) together with her earlier piece, How Search & Social Engines Are Using Semantic Search– provides background and explanation.
Some of the pieces for us to understand are:
- “Semantic Search, as it is used in current parlance is essentially the notion of using or exploiting metadata to improve search on documents. In the case of search engines, it more explicitly refers to embedding metadata in HTML5 (using semantic markup, the formats or HTML5 syntax currently supported by the search engines: RDFa Lite and microdata).”
- Support by Bing, Google, and Yahoo for Schema.org as as a “set of schemas for structured data markup on web pages”.
- With schema.org came more use of rich snippets to enhance display, especially useful to merchants to provide detailed product information.
- Google’s Knowledge Graph that pulls in information from Freebase, Wikipedia, CIA World Book in answer to queries that seem suitable for facts and aggregations of information or reference – mostly people and places.