Example of Inference Tagging
This section will walk you through another sample Inference Tagging process to even better illustrate the actual procedure as well as path of reasoning from input to results.
Since we have already determined our search space, corpus, minimum corpus score as well as the actual input text. In our sample call this text is "Renewable energy reduces carbon emissions.", we only need to few more parameters for the extract request; these are: maximum number of concepts, filtering of nested concepts, using of corpus score, shadow concepts and disambiguation as well as showing the matching information.
The API returns in response to this call the following: information whether the extraction was successful or not, then the list of extracted concepts and in our case also all the identified shadow concepts.
All concepts including shadow concepts are listed with their URI, label, score and text values with their respective score.
Our next call is an expand request; here you see again our search space (the same as for extract call) and then list of concepts which are however now only represented by their URI and score, followed by actual expansion query which in our case uses the narrower relation to create semantic footprint.
In the expand request we also specify the maximum number of concepts. We use here the concepts from our extract response.
The response to the expansion query contains information whether the query was successful and if so lists the expanded concepts showing for each of them the label, URI and their score.
Note
Please note that https://esg.poolparty.biz/esg-core/ is not publicly available.
For more information on shadow concepts, filtering nested concepts and disambiguation go to the following sections of our documentation: