mommi84

# After having extended the SSD size via AWS console:

ubuntu@ip-172-31-46-157:~$ lsblk
NAME    MAJ:MIN RM  SIZE RO TYPE MOUNTPOINT
loop1     7:1    0   18M  1 loop /snap/amazon-ssm-agent/1455
loop2     7:2    0 88.7M  1 loop /snap/core/7396
loop3     7:3    0   89M  1 loop /snap/core/7713
xvda    202:0    0  100G  0 disk 
└─xvda1 202:1    0   50G  0 part /

mommi84

#
# Endpoint: https://databus.dbpedia.org/repo/sparql
# Credits: @JJ-Author, @mommi84
#
PREFIX dataid: <http://dataid.dbpedia.org/ns/core#>
PREFIX dc:     <http://purl.org/dc/terms/>
PREFIX dcat:   <http://www.w3.org/ns/dcat#>
PREFIX dcv:    <http://dataid.dbpedia.org/ns/cv#>

SELECT DISTINCT ?url WHERE {

mommi84

import urllib2, urllib, httplib, json
import sys

MAX_RESULTS = 10000

graph = ""
endpoint = ""

# Execute a SPARQL query.
def sparql_query(query):

mommi84

#!/usr/bin/env python
import sys
import bibtexparser

reload(sys)
sys.setdefaultencoding("utf-8")

def fd(s):
    if s[-1] != '.':
        s += '.'

mommi84

SELECT ?s (count(?o) AS ?c) {
  {
    <Y> ?p ?o .
    ?s ?p ?o ; a <X>
  } UNION {
    ?o ?p <Y> , ?s .
    ?s a <X>
  }
} GROUP BY ?s ORDER BY DESC(?c) LIMIT 11

mommi84

#!/usr/bin/env python
"""

ProDiMem - Processor, disk, memory monitor for computations.

Usage:
    python prodimem.py [PID] [SECONDS]

Author:
    Tommaso Soru <tom@tommaso-soru.it>

mommi84

#!/usr/bin/env python
"""

Conversion from integer numbers to English words.
Author: Tommaso Soru <tom@tommaso-soru.it>

Example:
$ python numbers_words.py 3213213000312
threetrillionstwohundredsthirteenbillionstwohundredsthirteenmillionsthreehundredstwelve

mommi84

#!/usr/bin/env python
import sys
import urllib2, urllib, httplib, json

reload(sys)
sys.setdefaultencoding("utf-8")

ENDPOINT = "http://stats.lod2.eu/sparql"
GRAPH = ""
BUFFER = 10000

mommi84

PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#> 

SELECT DISTINCT ?s ?t2 WHERE {
  ?s a ?t2
  MINUS {
    ?s a ?t1, ?t2 .
    ?t1 rdfs:subClassOf ?t2 .
  }
}

mommi84

Awesome Knowledge Graph Embedding Approaches

This list contains repositories of libraries and approaches for knowledge graph embeddings, which are vector representations of entities and relations in a multi-relational directed labelled graph. Licensed under CC0.

Libraries

• 🆕 AmpliGraph (4 algorithms) @ https://github.com/Accenture/AmpliGraph
• Embedding framework (5 algorithms) @ https://github.com/BookmanHan/Embedding