Tag Archives: search engines

Stop. Google is not a semantic search engine

Is Google a semantic search engine?

A semantic search engine understands each query and presents relevant documents based on this understanding. Google however is not a semantic search engine but it does display elements of a semantic search engine:

  1. “Searches related to” is officially known as semantic similarity and it is designed to guide a searcher towards their search goal
  2. Google AdWords offers a keyword generation which is also closely related to semantic similarity
  3. Computing engineers also analysed Google’s search engine in 2011 and found that several results, or snippets, provide incorrect or non-useful semantic information — including queries who ranked in the top results. Google is therefore not a semantic search engine
Popular semantic search is a good bit away

Semantic technology is not ‘new’. Copyright granted by DullHunk.

Examples of semantic search engines

Sensebot and Sindice are examples of semantic search engines. Here understanding and relationships of keywords are key to producing results. That is not to say that other search engines do not understand queries but they rely on other technologies to do so rather than take a mathematical approach and rank queries based on meaning as supposed to, for example, saving and placing weight on keywords that appear in the title and header (term location in information retrieval). Search engines have just started to get smarter, thus a little semantic, with search, for instance, Google’s Knowledge Graph. But on the whole Google and other search engines are not semantic search engines; they just display elements of semantic search rather than be a complete and whole semantic search engine.

It is impossible to ask [photos of a flower with 5 petals and a yellow vase in the background] so Google is not a semantic search engine because it does not, unlike humans, understand a string of keywords to display relevant results.

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Reference

  • Xu, Z. Luo, X. Yu, J. and Xu, W. (2011) Measuring semantic similarity between words by removing noise and redundancy in web snippets. Concurrency and computation: Practice and experience. 23(2011) pp. 2496–2510
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