imports the R# package in script, and then create a empty graph database:
import graphQL
kb = MsgFile::open()then you can insert the knowledge data into the database engine:
# example of insert the knowledge data in table format liked:
# key value
# ---------------------------------------------------
# <mode> <string> <string>
# [1, ] "id" "C01405"
# [2, ] "name" "Aspirin"
# [3, ] "name" "Acetylsalicylic acid"
# [4, ] "name" "2-Acetoxybenzenecarboxylic acid"
# [5, ] "name" "Acetylsalicylate"
# [6, ] "formula" "C9H8O4"
# [ reached 'max' / getOption("max.print") -- omitted 7 rows ]
def process_knowledge(kb, data):
data = as.list(data, byrow = True)
data = groupBy(data, x -> x[["key"]])
data = lapply(data, x -> sapply(x, i -> i[["value"]]), x -> x[["key"]])
Query::insert(kb, data[["id"]], data)at last you can query the knowledge databse with query function, or assert that two term is identical or not?
# query knowledge data by term
result = Query::query(kb, "Aspirin")
# [1] "Aspirin is the name of C01405 with confidence 1.00"
# [2] "Aspirin is the name of HMDB0001879 with confidence 1.00"
# [3] "Aspirin is the name of ChEBI:15365 with confidence 1.00"
# assert that two term are identical or not?
Query::similarity(kb, "Aspirin", "Acetylsalicylic acid")
# [1] 0.740741
Query::similarity(kb, "Aspirin", "water")
# [1] 0
Query::similarity(kb, "dihydrogen oxide", "water")
# [1] 0.35