AlchemyAPI employs sophisticated text analysis techniques to concept tag documents in a manner similar to how humans would identify concepts. The concept tagging API is capable of making high-level abstractions by understanding how concepts relate, and can identify concepts that aren't necessarily directly referenced in the text.
For example, if an article mentions CERN and the Higgs boson, it will tag Large Hadron Collider as a concept even if the term is not mentioned explicitly in the page. By using concept tagging you can perform higher level analysis of your content than just basic keyword identification.
Example concept tagging on a scientific article
The more things change... Yes, I'm inclined to agree, especially with regards to the historical relationship between stock prices and bond yields. The two have generally traded together, rising during periods of economic growth and falling during periods of contraction. Consider the period from 1998 through 2010, during which the U.S. economy experienced two expansions as well as two recessions: Then central banks came to the rescue. Fed Chairman Ben Bernanke led from Washington with the help of the bank's current $3.6T balance sheet. He's accompanied by Mario Draghi at the European Central Bank and an equally forthright Shinzo Abe in Japan. Their coordinated monetary expansion has provided all the sugar needed for an equities moonshot, while they vowed to hold global borrowing costs at record lows.
|Central bank||0.70608||DBpedia | OpenCyc|
|Federal Reserve System||0.694171||Website | DBpedia | Yago | OpenCyc|
A relevance score is calculated for each concept based on statistical analysis, and the results are returned sorted by relevancy. Use the relevance score to determine the keyword's relative importance.
The associated linked data is returned for each concept to make it easy to pull in additional semantic information to further enhance your content. Learn more about AlchemyAPI's linked data support.
AlchemyAPI supports concept tagging for content written in English, with additional language support coming soon.
Concept tagging data can be returned in either JSON, XML or RDF to fit the needs of your application.