A PoC Corporation Relationship Knowledge Graph System on top of Nebula Graph.

Related tags

Deep Learningknowledge-graphgraphdatabase
Overview

Corp-Rel is a PoC of Corpartion Relationship Knowledge Graph System. It's built on top of the Open Source Graph Database: Nebula Graph with a dataset from nebula-shareholding-example.

corp-rel-capture.mov

Quick Start

First, please setup a Nebula Graph Cluster with data loaded from nebula-shareholding-example.

Then, clone this project:

git clone https://github.com/wey-gu/nebula-corp-rel-search.git
cd nebula-corp-rel-search

Start the backend:

python3 -m pip install -r requirements.txt
cd corp-rel-backend
export NG_ENDPOINTS="192.168.123.456:9669" # This should be your Nebula Graph Cluster GraphD Endpoint
python3 app.py

Start the frontend in another terminal:

npm install -g @vue/cli
cd nebula-corp-rel-search/corp-rel-frontend
vue serve src/main.js

Start a reverse Proxy to enable Corp-Rel Backend being served with same origin of Frontend:

For example below is a Nginx config to make :8081/ go to http://localhost:8080 and :8081/api go to http://192.168.123.456:5000/api.

http {
    include       mime.types;
    default_type  application/octet-stream;

    keepalive_timeout  65;

    server {
        listen       8081;
        server_name  localhost;
        # frontend
        location / {
            proxy_pass http://localhost:8080;
        }
        # backend
        location /api {
            proxy_pass http://192.168.123.456:5000/api;
        }
    }
#...

After above reverse proxy being configured, let's verify it via cURL:

curl --header "Content-Type: application/json" \
     --request POST \
     --data '{"entity": "c_132"}' \
     http://localhost:8081/api | jq

If it's properly responded, hen we could go to http://localhost:8081 from the web browser :).

Design Log

data from Backend Side

Backend should query node's relationship path as follow:

MATCH p=(v)-[e:hold_share|:is_branch_of|:reletive_with|:role_as*1..3]-(v2) \
WHERE id(v) IN ["c_132"] RETURN p LIMIT 100

An example of the query will be like this:

([email protected]) [shareholding]> MATCH p=(v)-[e:hold_share|:is_branch_of|:reletive_with|:role_as*1..3]-(v2) \
                           -> WHERE id(v) IN ["c_132"] RETURN p LIMIT 100
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| p                                                                                                                                                                                                                                        |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| <("c_132" :corp{name: "Chambers LLC"})<-[:[email protected] {share: 0.0}]-("c_245" :corp{name: "Thompson-King"})>                                                                                                                             |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| <("c_132" :corp{name: "Chambers LLC"})<-[:[email protected] {share: 3.0}]-("p_1039" :person{name: "Christian Miller"})>                                                                                                                       |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| <("c_132" :corp{name: "Chambers LLC"})<-[:[email protected] {share: 3.0}]-("p_1399" :person{name: "Sharon Gonzalez"})>                                                                                                                        |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| <("c_132" :corp{name: "Chambers LLC"})<-[:[email protected] {share: 9.0}]-("p_1767" :person{name: "Dr. David Vance"})>                                                                                                                        |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| <("c_132" :corp{name: "Chambers LLC"})<-[:[email protected] {share: 11.0}]-("p_1997" :person{name: "Glenn Reed"})>                                                                                                                            |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| <("c_132" :corp{name: "Chambers LLC"})<-[:[email protected] {share: 14.0}]-("p_2341" :person{name: "Jessica Baker"})>                                                                                                                         |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
...

Leveraging nebula2-python, we could have result in below data structure:

$ python3 -m pip install nebula2-python==2.5.0
$ ipython
In [1]: from nebula2.gclient.net import ConnectionPool
In [2]: from nebula2.Config import Config
In [3]: config = Config()
   ...: config.max_connection_pool_size = 10
   ...: # init connection pool
   ...: connection_pool = ConnectionPool()
   ...: # if the given servers are ok, return true, else return false
   ...: ok = connection_pool.init([('192.168.8.137', 9669)], config)
   ...: session = connection_pool.get_session('root', 'nebula')
[2021-10-13 13:44:24,242]:Get connection to ('192.168.8.137', 9669)

In [4]: resp = session.execute("use shareholding")
In [5]: query = '''
   ...: MATCH p=(v)-[e:hold_share|:is_branch_of|:reletive_with|:role_as*1..3]-(v2) \
   ...: WHERE id(v) IN ["c_132"] RETURN p LIMIT 100
   ...: '''
In [6]: resp = session.execute(query) # Note: after nebula graph 2.6.0, we could use execute_json as well

In [7]: resp.col_size()
Out[7]: 1

In [9]: resp.row_size()
Out[10]: 100

As we know the result is actually a nebula-python path type, they could be extracted as follow with .nodes() and .relationships():

In [11]: p=resp.row_values(22)[0].as_path()

In [12]: p.nodes()
Out[12]:
[("c_132" :corp{name: "Chambers LLC"}),
 ("p_4000" :person{name: "Colton Bailey"})]

In [13]: p.relationships()
Out[13]: [("p_4000")-[:role_as@0{role: "Editorial assistant"}]->("c_132")]

For relationships/edges, we could call its .edge_name(), .properties(), .start_vertex_id(), .end_vertex_id():

In [14]: rel=p.relationships()[0]

In [15]: rel
Out[15]: ("p_4000")-[:role_as@0{role: "Editorial assistant"}]->("c_132")

In [16]: rel.edge_name()
Out[16]: 'role_as'

In [17]: rel.properties()
Out[17]: {'role': "Editorial assistant"}

In [18]: rel.start_vertex_id()
Out[18]: "p_4000"

In [19]: rel.end_vertex_id()
Out[19]: "c_132"

And for nodes/vertices, we could call its .tags(), properties, get_id():

In [20]: node=p.nodes()[0]

In [21]: node.tags()
Out[21]: ['corp']

In [22]: node.properties('corp')
Out[22]: {'name': "Chambers LLC"}

In [23]: node.get_id()
Out[23]: "c_132"

Data visualization

For the frontend, we could create a view by leveraging vue-network-d3:

npm install vue-network-d3 --save
touch src/App.vue
touch src/main.js

In src/App.vue, we create a Network instance and fill in the nodeList, and linkList fetched from backend, in below example, we put fake data as:

nodes: [
        {"id": "c_132", "name": "Chambers LLC", "tag": "corp"},
        {"id": "p_4000", "name": "Colton Bailey", "tag": "person"}],
relationships: [
        {"source": "p_4000", "target": "c_132", "properties": { "role": "Editorial assistant" }, "edge": "role_as"}]

And the full example of src/App.vue will be:

<template>
  <div id="app">
    <network
      :nodeList="nodes"
      :linkList="relationships"
      :nodeSize="nodeSize"
      :linkWidth="linkWidth"
      :linkDistance="linkDistance"
      :linkTextFrontSize="linkTextFrontSize"
      :nodeTypeKey="nodeTypeKey"
      :linkTypeKey="linkTypeKey"
      :nodeTextKey="nodeTextKey"
      :linkTextKey="linkTextKey"
      :showNodeText="showNodeText"
      :showLinkText="showLinkText"
      >
    </network>
  </div>
</template>

<script>
import Network from "vue-network-d3";

export default {
  name: "app",
  components: {
    Network
  },
  data() {
    return {
      nodes: [
        {"id": "c_132", "name": "Chambers LLC", "tag": "corp"},
        {"id": "p_4000", "name": "Colton Bailey", "tag": "person"}
      ],
      relationships: [
        {"source": "p_4000", "target": "c_132", "properties": { "role": "Editorial assistant" }, "edge": "role_as"}
      ],
      nodeSize: 18,
      linkDistance: 120,
      linkWidth: 6,
      linkTextFrontSize: 20,
      nodeTypeKey: "tag",
      linkTypeKey: "edge",
      nodeTextKey: "name",
      linkTextKey: "properties",
      showNodeText: true,
      showLinkText: true
    };
  },
};
</script>

<style>
body {
  margin: 0;
}
</style>

Together with src/main.js:

import Vue from 'vue'
import App from './App.vue'

Vue.config.productionTip = false

new Vue({
  render: h => h(App),
}).$mount('#app')

Then we could run: vue serve src/main.js to have this renderred:

vue-network-d3-demo

The data construction in Back End:

Thus we shoud know that if the backend provides list of nodes and relationships in JSON as the following, things are perfectly connected!

Nodes:

[{"id": "c_132", "name": "Chambers LLC", "tag": "corp"},
 {"id": "p_4000", "name": "Colton Bailey", "tag": "person"}]

Relationships:

[{"source": "p_4000", "target": "c_132", "properties": { "role": "Editorial assistant" }, "edge": "role_as"},
 {"source": "p_1039", "target": "c_132", "properties": { "share": "3.0" }, "edge": "hold_share"}]

We could construct it as:

def make_graph_response(resp) -> dict:
    nodes, relationships = list(), list()
    for row_index in range(resp.row_size()):
        path = resp.row_values(row_index)[0].as_path()
        _nodes = [
            {
                "id": node.get_id(), "tag": node.tags()[0],
                "name": node.properties(node.tags()[0]).get("name", "")
                }
                for node in path.nodes()
        ]
        nodes.extend(_nodes)
        _relationships = [
            {
                "source": rel.start_vertex_id(),
                "target": rel.end_vertex_id(),
                "properties": rel.properties(),
                "edge": rel.edge_name()
                }
                for rel in path.relationships()
        ]
        relationships.extend(_relationships)
    return {"nodes": nodes, "relationships": relationships}

The Flask App

Then Let's create a Flask App to consume the HTTP API request and return the data designed as above.

from flask import Flask, jsonify, request



app = Flask(__name__)


@app.route("/")
def root():
    return "Hey There?"


@app.route("/api", methods=["POST"])
def api():
    request_data = request.get_json()
    entity = request_data.get("entity", "")
    if entity:
        resp = query_shareholding(entity)
        data = make_graph_response(resp)
    else:
        data = dict() # tbd
    return jsonify(data)


def parse_nebula_graphd_endpoint():
    ng_endpoints_str = os.environ.get(
        'NG_ENDPOINTS', '127.0.0.1:9669,').split(",")
    ng_endpoints = []
    for endpoint in ng_endpoints_str:
        if endpoint:
            parts = endpoint.split(":")  # we dont consider IPv6 now
            ng_endpoints.append((parts[0], int(parts[1])))
    return ng_endpoints

def query_shareholding(entity):
    query_string = (
        f"USE shareholding; "
        f"MATCH p=(v)-[e:hold_share|:is_branch_of|:reletive_with|:role_as*1..3]-(v2) "
        f"WHERE id(v) IN ['{ entity }'] RETURN p LIMIT 100"
    )
    session = connection_pool.get_session('root', 'nebula')
    resp = session.execute(query_string)
    return resp

And by starting this Flask App instance:

export NG_ENDPOINTS="192.168.8.137:9669"
python3 app.py

 * Serving Flask app 'app' (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: off
[2021-10-13 18:30:17,574]: * Running on all addresses.
   WARNING: This is a development server. Do not use it in a production deployment.
[2021-10-13 18:30:17,574]: * Running on http://192.168.10.14:5000/ (Press CTRL+C to quit)

we could then query the API with cURL like this:

curl --header "Content-Type: application/json" \
     --request POST \
     --data '{"entity": "c_132"}' \
     http://192.168.10.14:5000/api | jq

{
  "nodes": [
    {
      "id": "c_132",
      "name": "\"Chambers LLC\"",
      "tag": "corp"
    },
    {
      "id": "c_245",
      "name": "\"Thompson-King\"",
      "tag": "corp"
    },
    {
      "id": "c_132",
      "name": "\"Chambers LLC\"",
      "tag": "corp"
    },
...
    }
  ],
  "relationships": [
    {
      "edge": "hold_share",
      "properties": "{'share': 0.0}",
      "source": "c_245",
      "target": "c_132"
    {
      "edge": "hold_share",
      "properties": "{'share': 9.0}",
      "source": "p_1767",
      "target": "c_132"
    },
    {
      "edge": "hold_share",
      "properties": "{'share': 11.0}",
      "source": "p_1997",
      "target": "c_132"
    },
...
    },
    {
      "edge": "reletive_with",
      "properties": "{'degree': 51}",
      "source": "p_7283",
      "target": "p_4723"
    }
  ]
}

Upstreams Projects

Owner
Wey Gu
Developer Advocate @vesoft-inc
Wey Gu
GitHub Repository
LBBA-boosted WSOD

LBBA-boosted WSOD Summary Our code is based on ruotianluo/pytorch-faster-rcnn and WSCDN Sincerely thanks for your resources. Newer version of our code

Martin Dong 20 Sep 19, 2022
Compact Bidirectional Transformer for Image Captioning

Compact Bidirectional Transformer for Image Captioning Requirements Python 3.8 Pytorch 1.6 lmdb h5py tensorboardX Prepare Data Please use git clone --

YE Zhou 19 Dec 12, 2022
3D Generative Adversarial Network

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling This repository contains pre-trained models and sampling

Chengkai Zhang 791 Dec 20, 2022
Crowd-Kit is a powerful Python library that implements commonly-used aggregation methods for crowdsourced annotation and offers the relevant metrics and datasets

Crowd-Kit: Computational Quality Control for Crowdsourcing Documentation Crowd-Kit is a powerful Python library that implements commonly-used aggregat

Toloka 125 Dec 30, 2022
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

Microsoft 8.4k Jan 01, 2023
Code implementation of Data Efficient Stagewise Knowledge Distillation paper.

Data Efficient Stagewise Knowledge Distillation Table of Contents Data Efficient Stagewise Knowledge Distillation Table of Contents Requirements Image

IvLabs 112 Dec 02, 2022
Mercury: easily convert Python notebook to web app and share with others

Mercury Share your Python notebooks with others Easily convert your Python notebooks into interactive web apps by adding parameters in YAML. Simply ad

MLJAR 2.2k Dec 27, 2022
A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking

PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking PoseRBPF Paper Self-supervision Paper Pose Estimation Video Robot Manipulati

NVIDIA Research Projects 107 Dec 25, 2022
This project contains an implemented version of Face Detection using OpenCV and Mediapipe. This is a code snippet and can be used in projects.

Live-Face-Detection Project Description: In this project, we will be using the live video feed from the camera to detect Faces. It will also detect so

Hassan Shahzad 3 Oct 02, 2021
Curated list of awesome GAN applications and demo

gans-awesome-applications Curated list of awesome GAN applications and demonstrations. Note: General GAN papers targeting simple image generation such

Minchul Shin 4.5k Jan 07, 2023
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

CNTK Chat Windows build status Linux build status The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes

Microsoft 17.3k Dec 29, 2022
Pairwise learning neural link prediction for ogb link prediction

Pairwise Learning for Neural Link Prediction for OGB (PLNLP-OGB) This repository provides evaluation codes of PLNLP for OGB link property prediction t

Zhitao WANG 31 Oct 10, 2022
Non-Vacuous Generalisation Bounds for Shallow Neural Networks

This package requires jax, tensorflow, and numpy. Either tensorflow or scikit-learn can be used for loading data. To run in a nix-shell with required

Felix Biggs 0 Feb 04, 2022
A model that attempts to learn and benefit from data collected on card counting.

A model that attempts to learn and benefit from data collected on card counting. A decision tree like model is built to win more often than loose and increase the bet of the player appropriately to c

1 Dec 17, 2021
Locally Constrained Self-Attentive Sequential Recommendation

LOCKER This is the pytorch implementation of this paper: Locally Constrained Self-Attentive Sequential Recommendation. Zhankui He, Handong Zhao, Zhe L

Zhankui (Aaron) He 8 Jul 30, 2022
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

Learning to Adapt Structured Output Space for Semantic Segmentation Pytorch implementation of our method for adapting semantic segmentation from the s

Yi-Hsuan Tsai 782 Dec 30, 2022
Semi-supevised Semantic Segmentation with High- and Low-level Consistency

Semi-supevised Semantic Segmentation with High- and Low-level Consistency This Pytorch repository contains the code for our work Semi-supervised Seman

123 Dec 30, 2022
Code for KDD'20 "Generative Pre-Training of Graph Neural Networks"

GPT-GNN: Generative Pre-Training of Graph Neural Networks GPT-GNN is a pre-training framework to initialize GNNs by generative pre-training. It can be

Ziniu Hu 346 Dec 19, 2022
ICLR 2021 i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning

Introduction PyTorch code for the ICLR 2021 paper [i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning]. @inproceedings{lee2021i

Kibok Lee 68 Nov 27, 2022
Change Detection in SAR Images Based on Multiscale Capsule Network

SAR_CD_MS_CapsNet Code for the paper "Change Detection in SAR Images Based on Multiscale Capsule Network" , IEEE Geoscience and Remote Sensing Letters

Feng Gao 21 Nov 29, 2022