# Determines if Knowledge base contains the specified term
#
# @param term [String] term
# @return [Hash] with :presence field
defterm_presence(term)
presetKBM:termPresence,term
end
# Returns information measure for the given term. Information measure denotes, how often given term is used as link caption among all its occurences
#
# @param term [String] term
# @result [Hash] with :infomeasure field
defterm_info_measure(term)
presetKBM:termInfoMeasure,term
end
# Return concepts resource from the Knowledge base corresponding to the found meanings of the given term
#
# @param term [String] term
# @result [Hash] with :elements field
defterm_meanings(term)
presetKBM:termMeanings,term
end
# If concept isn't provided, returns concepts with their commonness, corresponding to the found meanings of the given term. Commonness denotes, how often the given term is associated with the given concept.
# With concept(format is {id}:{kbname}) returns commonness of given concept for the given term.
#
# @param term [String] term
# @param concept [String] concept as {id}:{kbname}
# @result [Hash] with :elements field
defterm_commonness(term,concept='')
concept="id=#{concept}"unlessconcept.empty?
presetKBM:termCommonness,[term,concept]
end
# Return neighbour concepts for the given concepts(list or single concept, each concept is {id}:{kbname}).
# If at least one traverse parameter(check REST Documentation for values) is specified, all other parameters should also be specified
#
# @param concepts [String, Array] either concept as {id}:{kbname} or array of such concepts
# @param traverse_params [Hash] optional, should contain :linkType, :nodeType, :minDepth, :maxDepth keys with values
# Computes sum of similarities from each concepts(list or single concept, each concept is {id}:{kbname}) from the first list to all concepts(list or single concept, each concept is {id}:{kbname}) from the second one.
#
# @param first_concepts [Array] array of concepts as {id}:{kbname}
# @param second_concepts [Array] array of concepts as {id}:{kbname}
# @param linkWeight [String] pecifies method for computation of link weight in case of multiple link types - check REST Documentation for values
# Compute similarity from each concept from the first list to all concepts(list or single concept, each concept is {id}:{kbname}) from the second list as a whole.
# Links of second list concepts(each concept is {id}:{kbname}) are collected together, thus forming a "virtual" article, similarity to which is computed.
#
# @param concepts [Array] array of concepts as {id}:{kbname}
# @param virtual_aricle [Array] array of concepts as {id}:{kbname}
# @param linkWeight [String] pecifies method for computation of link weight in case of multiple link types - check REST Documentation for values
# Compute similarity between two sets of concepts(list or single concept, each concept is {id}:{kbname}) as between "virtual" articles from these sets.
# The links of each virtual article are composed of links of the collection of concepts.
#
# @param first_virtual_aricle [Array] array of concepts as {id}:{kbname}
# @param second_virtual_article [Array] array of concepts as {id}:{kbname}
# @param linkWeight [String] pecifies method for computation of link weight in case of multiple link types - check REST Documentation for values
# Search for similar concepts over filtered set of the first and the second neighbours of the given ones(list or single concept, each concept is {id}:{kbname}).
#
# @param concepts [Array] array of concepts as {id}:{kbname}
# @param linkWeight [String] pecifies method for computation of link weight in case of multiple link types - check REST Documentation for values
# @param params [Hash]
# => :linkWeight [String] pecifies method for computation of link weight in case of multiple link types - check REST Documentation for values
# => :offset [Integer] provides a possibility to skip several concepts from the start of the result
# => :limit [Integer] provides a possibility to limit size of result
# => :among [SimilarCandidatesFilter] specifies how to filter neighbour concepts when searching for most similar
@en_text='Apple today updated iMac to bring numerous high-performance enhancements to the =s leading all-in-one desktop. iMac now features fourth-generation Intel Core processors, new graphics, and next-generation Wi-Fi. In addition, it now supports PCIe-based flash storage, making its Fusion Drive and all-flash storage options up to 50 percent faster than the previous generation'
@en_text='Apple today updated iMac to bring numerous high-performance enhancements to the leading all-in-one desktop. iMac now features fourth-generation Intel Core processors, new graphics, and next-generation Wi-Fi. In addition, it now supports PCIe-based flash storage, making its Fusion Drive and all-flash storage options up to 50 percent faster than the previous generation'
@ru_text='Первые в этом году переговоры министра иностранных дел России Сергея Лаврова и госсекретаря США Джона Керри, длившиеся 1,5 часа, завершились в Мюнхене.'
@en_tweet='mentioning veterens care which Mccain has voted AGAINST - SUPER GOOOOD point Obama+1 #tweetdebate'
@ru_tweet='В мастерской готовят пушку и автомобили 1940-х годов, для участия в Параде Победы в Ново-Переделкино.'
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@@ -16,6 +16,8 @@ class TestTexterraAPI < Minitest::Test
