$ sudo python setup.py install

$ conceptnet-make.py

$ conceptnet-make.py -s /home/user/conceptnet_project

# :param offset: skip the specified amount of first results
# :type offset: integer
# :param limit: change the number of results from the default of 50
# :type limit: integer
# :param filter: If 'core', only get edges from the ConceptNet 5 Core (not from ShareAlike resources), if 'core-assetions', search for edges by default, and there can be many edges representing the same assertion.
# :type filter: Either 'core' or 'core-assertions'

# get general facts about 'see movie'
lookup = LookUp(limit=1)
response = lookup.search_concept('see movie')

# parse the response
r = Result(response)
edges = r.parse_all_edges(clean_self_ref = True) # this is a list of Edge objects
[edge.print_edge() for edge in edges]
# in order to print all attributes of edges
[edge.print_all_attrs() for edge in edges]

lookup = LookUp()
response1 = lookup.search_source('/s/wordnet/3.0')
r1 = Result(response1)
# print results in key = value format 
r1.print_raw_results()

response2 = lookup.search_source('/s/rule/sum_edges')
r2 = Result(response2)
# print results in key = value format 
r2.print_raw_results()

# :param {id, uri, rel, start, end, context, dataset, license}: giving a ConceptNet URI for any of these parameters will return edges whose corresponding fields start with the given path
# :type {id, uri, rel, start, end, context, dataset, license}: uri
# :param nodes: returns edges whose rel, start, or end start with the given URI
# :type nodes: uri
# :param {startLemmas, endLemmas, relLemmas}: returns edges containing the given lemmatized word anywhere in their start, end, or rel respectively
# :type {startLemmas, endLemmas, relLemmas}: word
# :param text: matches any of startLemmas, endLemmas, or relLemmas 
# :type text: word
# :param surfaceText: matches edges with the given word in their surface text. The word is not lemmatized, but it is a case-insensitive match
# :type surfaceText: word
# :param minWeight: filters for edges whose weight is at least weight
# :type minWeight: float
# :param limit: change the number of results from the default of 50
# :type limit: integer
# :param offset: skip the specified amount of first results
# :type offset: integer 
# :param features: Takes in a feature string (an assertion with one open slot), and returns edges having exactly that string as one of their features
# :type features: string of uri
# :param filter: If 'core', only get edges from the ConceptNet 5 Core (not from ShareAlike resources), if 'core-assetions', search for edges by default, and there can be many edges representing the same assertion.
# :type filter: Either 'core' or 'core-assertions'

# Search for the edges whose relation is 'be often compared to'
s = Search(rel='/c/en/be_often_compare_to')
data = s.search()
r = Result(data)
# print results in key = value format 
r.print_raw_results()

# Search for any edge whose any of 'startLemmas', 'endLemmas' or 'relLemmas' matches 
# 'mariah carey' and whose 'surfaceText' matches 'dion'. Here the arg'something' is 
# not supported, so it will be ignored constructing query URL.
s = Search(text='mariah carey', surfaceText='dion', something='anything')
data = s.search()
r = Result(data)
# print results in key = value format 
r.print_raw_results()

# :param limit: change the number of results from the default of 50
# :type limit: integer
# :param filter: return only results that start with the given URI. For example, filter=/c/en returns results in English.
# :type filter: a uri, e.g. '/c/en/cat' (Different than lookup API!)

# get how similar cats and dogs 
a = Association(filter='/c/en/dog', limit=1)
data = a.get_similar_concepts('cat')
r = Result(data)
# print results in key = value format 
r.print_raw_results()

a = Association()
data = a.get_similar_concepts_by_term_list(['toast', 'cereal', 'juice', 'egg'])
r = Result(data)
# print results in key = value format 
r.print_raw_results()