Sentence examples for seed topics from inspiring English sources

The group uses Semantic MEDLINE with the Combo algorithm to assess the citations, choosing the most relevant seed topics.

Bladder cancer served as the seed topic in each case.

All novel semantic predications including these predicates and the seed topic were extracted as salient output.

Available seed topic choices are automatically determined by mapping UMLS Metathesaurus concepts to the SemRep data.

In a particular search process for a seed topic entity, we can obtain many relation predicates related to the seed topic and semantic types related to a relation predicate.

Using the example above, in cetuximab-TREATS-Endometrial carcinoma, the seed topic "Endometrial carcinoma" has the semantic type "neoplastic process".

The P.A. submits the search "Hip Fractures[mesh] AND recurrent" and then chooses "Hip fractures" as the UMLS Metathesaurus seed topic.

With Carcinoma of bladder as the seed topic, KLD method extracts the relation predicates in the data set.

Semantic predications in both distributions are limited to those containing a chosen UMLS Metathesaurus seed topic before KLD analysis.

For example, if the user chose the UMLS Metathesaurus topic "Coronary Arteriosclerosis," summarization would collect all predications that included this seed topic as a subject or object argument.

However, these methods can only extract the direct relation between biomedical entities and, therefore, cannot obtain deep comprehensive information related to the seed topic.