Difference between revisions of "Tools:Google use of semantic search features"
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| + | ==Notes on semantic search== | ||
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| + | Useful blog article: [https://ahrefs.com/blog/understanding-semantic-search-introduction-beginners/ Paul Grabowski, 'Understanding Semantic Search - Introduction for Beginners', Feb 18th 2015] | ||
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| + | The article lists other resources on semantic search, including: | ||
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| + | * [https://searchenginewatch.com/sew/how-to/2285277/how-the-semantic-web-changes-everything-for-search Simon Penson, 'How the Semantic Web Changes Everything for Search', July 29th 2013] | ||
| + | * [https://youtu.be/5lCSDOuqv1A Andrew Hogue, The Structured Search Engone, January 19, 2011, video] | ||
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| + | The Structured Search Engine | ||
| + | - Discusses Google acquisition of "Freebase" (now closed down as an independent entity)<ref>[https://en.wikipedia.org/wiki/Freebase Wikipedia article: Freebase]</ref> | ||
| + | - To create a new Freebase entity extract tabular and attribute data in a web page | ||
| + | - Using Open-Domain Fact Extraction; rank extracted attributes with confidence values | ||
| + | - Query parsing, e.g. "when was martin luther king jr born" | ||
| + | - Parser identifies entities (thing being asked about, e.g. "martin luther king jr") and attributes (e.g. "born"); synonyms for specific entities; question forms (what "value" does the wusetion form found tend to deliver (here it is "date") | ||
| + | - Understanding content: Sentiment Analysis (positive vs. negative; happy vs sad) using Natural Language Processing. Example of summarising restaurant reviews. Use seed words and N-gram graph to create a Lexicon. Scores for specific words as to how positive or negative they are in a given context. | ||
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==People also ask== | ==People also ask== | ||
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They also include results from Google images and selected news featuring that search term. | They also include results from Google images and selected news featuring that search term. | ||
| − | For example. | + | For example, Google search for the term "Basketball" will yield a box containing dropdown questions related to basketball, which are frequently asked by searchers interested in basketball. |
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| + | ==Searches related to specific search term or terms== | ||
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| + | [[File:Cheapest Apple Search 042016.PNG|400px|thumb|left|Google search: Searches related to specific search term or terms]] | ||
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| + | Google searches using a single or multiple search terms will generate a table of "Searches related to..." at the bottom of the first page of search results. | ||
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| + | For example, Google search for the term "cheapest apple" will yield a box with eight further suggested searches using those terms | ||
==Specific Google services== | ==Specific Google services== | ||
[[File:Google Adv Patent Search 02072016.PNG|400px|thumb|left|[http://www.google.com/advanced_patent_search Google Advanced Patent Search]]] | [[File:Google Adv Patent Search 02072016.PNG|400px|thumb|left|[http://www.google.com/advanced_patent_search Google Advanced Patent Search]]] | ||
Latest revision as of 16:15, July 4, 2016
Contents
Notes on semantic search
Useful blog article: Paul Grabowski, 'Understanding Semantic Search - Introduction for Beginners', Feb 18th 2015
The article lists other resources on semantic search, including:
- Simon Penson, 'How the Semantic Web Changes Everything for Search', July 29th 2013
- Andrew Hogue, The Structured Search Engone, January 19, 2011, video
The Structured Search Engine
- Discusses Google acquisition of "Freebase" (now closed down as an independent entity)[1]
- To create a new Freebase entity extract tabular and attribute data in a web page
- Using Open-Domain Fact Extraction; rank extracted attributes with confidence values
- Query parsing, e.g. "when was martin luther king jr born"
- Parser identifies entities (thing being asked about, e.g. "martin luther king jr") and attributes (e.g. "born"); synonyms for specific entities; question forms (what "value" does the wusetion form found tend to deliver (here it is "date")
- Understanding content: Sentiment Analysis (positive vs. negative; happy vs sad) using Natural Language Processing. Example of summarising restaurant reviews. Use seed words and N-gram graph to create a Lexicon. Scores for specific words as to how positive or negative they are in a given context.
People also ask
Google searches using common search terms generate first page boxes containing dropdown menus of frequent searches using that common search term.
They also include results from Google images and selected news featuring that search term.
For example, Google search for the term "Basketball" will yield a box containing dropdown questions related to basketball, which are frequently asked by searchers interested in basketball.
Google searches using a single or multiple search terms will generate a table of "Searches related to..." at the bottom of the first page of search results.
For example, Google search for the term "cheapest apple" will yield a box with eight further suggested searches using those terms
Specific Google services
