Read BioKG

Download BioKG

The following Python script downloads the BioKG knowledge graph from the Open Graph Benchmark (OGB).

import numpy as np
import copy
import json
from ogb.linkproppred import LinkPropPredDataset

dataset = LinkPropPredDataset(name = "ogbl-biokg")

split_edge = dataset.get_edge_split()
train_edge, valid_edge, test_edge = split_edge["train"], split_edge["valid"], split_edge["test"]
graph = dataset[0] # graph: library-agnostic graph object

The edge index in the graph object is converted into JSON format to be read into R.

print(graph.keys())
edge_index = graph["edge_index_dict"].copy()

To make the conversion, the dictionary keys must be renamed (i.e., cannot be tuples).

old_keys = list(edge_index.keys())
for old_name in old_keys:
    new_name = "--".join(old_name)
    edge_index[new_name] = edge_index[old_name]
    del edge_index[old_name]

A special numpy encoder is defined, borrowed from this StackOverflow post.

class NumpyEncoder(json.JSONEncoder):
    """ Special json encoder for numpy types """
    def default(self, obj):
        if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,
                            np.int16, np.int32, np.int64, np.uint8,
                            np.uint16, np.uint32, np.uint64)):
            return int(obj)
        elif isinstance(obj, (np.float_, np.float16, np.float32,
                              np.float64)):
            return float(obj)
        elif isinstance(obj, (np.ndarray,)):
            return obj.tolist()
        return json.JSONEncoder.default(self, obj)

Finally, we convert the edge index to JSON and write to a file.

edge_index_json = json.dumps(edge_index, cls = NumpyEncoder)

with open('inst//extdata//edge_index.json', 'a') as f:
    f.write(edge_index_json + '\n') 

Parse KG in R

Read the JSON dataset saved previously.

# load libraries
library(data.table)
library(purrr)
library(magrittr)
library(rjson)

# load metapaths library
library(metapaths)

# read data
biokg = fromJSON(file = "inst/extdata/edge_index.json")

Create a function to convert the edge list to a data.table. Note that the node IDs are specific to each type, so we must add a type-specific prefix.

convert_biokg = function(sub_kg, sub_label) {
  
  # split label
  split_label = strsplit(sub_label, "--")[[1]]
  
  # create data table
  kg_dt = data.table(Origin = paste(split_label[1], sub_kg[[1]], sep = "_"),
                     Destination =  paste(split_label[3], sub_kg[[2]], sep = "_"),
                     OriginType = split_label[1], DestinationType = split_label[3],
                     EdgeType = split_label[2])
  
}

Now, map the conversion function over the biokg list.

biokg_edge_list = imap_dfr(biokg, convert_biokg)
biokg_node_list = get_node_list(biokg_edge_list)
head(biokg_edge_list)

Check that the counts of each node type conform with graph["num_nodes_dict"] from the Python script.

disease	drug	function	protein	sideeffect
10687	10533	45085	17499	9969

table(biokg_node_list$NodeType)

Randomly sample the knowledge graph to generate a small test set.

biokg_graph = igraph::graph.data.frame(biokg_edge_list,
                                       vertices = biokg_node_list,
                                       directed = T)

Save node list and edge list to file.

biokg_graph = igraph::graph.data.frame(biokg_edge_list,
                                       vertices = biokg_node_list,
                                       directed = T)