def node_matching(relations,user_query,dict1):
stemmer=SnowballStemmer('english')
user_query_list=map(str,map(stemmer.stem,word_tokenize(user_query)))
user_query_set=set(user_query_list)
ans_found=0
r=[]
#print user_query_list
#print relations
for j in relations:
#get the subject and object for j
i='concept:'+''.join(j.split(" "))+'/scores.tsv'
source_list=[]
target_list=[]
score_list=[]
#print j
with open(os.path.join('nell_pca_svo_pra',i),'r') as fp:
for line in fp:
if(chk_line(line)):
n1,n2,score=map(float,line.split('\t')[:3])
if score>1:
#print n1,n2
source_list.append(n1)
target_list.append(n2)
score_list.append(score)
#convert node to human readable forms
source_list=read_graph_nodes(dict1,source_list)
target_list=read_graph_nodes(dict1,target_list)
#if any of source list and target list is in user_query_set and 60 % of other user query text matches with the relation j, then print that relation
#this is where we use similarity technique
for i in range(len(source_list)):
#print source_list[i].split(":")[2],target_list[i].split(":")[2]
if stemmer.stem(source_list[i].split(":")[2]) in user_query_list and stemmer.stem(target_list[i].split(":")[2]) in user_query_list:
#print source_list[i],target_list[i]
#get both nodes info, get relation from graph, check 60% matching with user query
relation_info=map(str,map(stemmer.stem,word_tokenize(j)))
relation_info.extend(map(str,map(stemmer.stem,(source_list[i].split(":")[1:]))))
relation_info.extend(map(str,map(stemmer.stem,(target_list[i].split(":")[1:]))))
relation_info_set=set(relation_info)
#print relation_info_set,user_query_set
#print len(relation_info_set.intersection(user_query_set)),len(relation_info_set)/2
if len(relation_info_set.intersection(user_query_set))>len(relation_info_set)*0.4:
r.append(j)
ans_found=1
return r
dict1[int(n_id)]=name
user_query=raw_input("Enter the query: ")
ans_found=0
#print relations
for j in relations:
if j in user_query: #this is where we use paraphrasing
#print the answer. node convert. etc
#read relation nodes obtained by PRA
i='concept:'+''.join(j.split(" "))+'/scores.tsv'
source_list=[]
target_list=[]
score_list=[]
with open(os.path.join('nell_pca_svo_pra',i),'r') as fp:
for line in fp:
if(chk_line(line)):
n1,n2,score=map(float,line.split('\t')[:3])
if score>1:
source_list.append(n1)
target_list.append(n2)
score_list.append(score)
#convert node to human readable forms
source_list=read_graph_nodes(dict1,source_list)
target_list=read_graph_nodes(dict1,target_list)
#match nodes with user query
for ii in xrange(len(source_list)):
print source_list[ii].split(":")[2]+' '+j+' '+target_list[ii].split(":")[2]+' '+str(score_list[ii])
ans_found=1
if ans_found==0:
print 'Not found anything relevant'
