Repository for my SIGIR 2017 paper
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revs
scripts
static
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LICENSE.md
README.md
config.json
requirements.txt
server.py

README.md

REVS

Introduction

REVS(Relatedness Extraction & Visualization System) is a web-based system to extract and visualize the relatedness of a pair of queried entities in a knowledge graph.

Live Demonstration

We provide a live demonstration website here.

However, due to a potential heavy load on the query processing and some security risks, we do not guarantee that this website is available all the time. When it is unavailable, you may read the following sections and install REVS in your own system.

Installation

Check Prerequisites

  • Python 3 & Virtualenv

    We will use virtualenv to create a virtual python 3 environment. Make sure you have installed it in your system.

  • Postgres 9.4+

    Create a new relational database for storing and querying the knowledge graph.

  • Mongodb 3.4+

    Create a new NoSQL database for caching queries.

  • ElasticSearch 5.4+

    This enables the entity name auto-completion. You do not need to do any initializations right now.

Edit Configurations

Edit the config.json file to change the following settings according to your system environment.

  1. Postgres connection string

    "postgres": {
        "connection_string": "host='YOUR_POSTGRES_HOST' port='YOUR_POSTGRES_PORT' dbname='DB_NAME' user='DB_USER' password='DB_PASS'"
    },
  2. MongoDB connection URL and database name

    "mongo": {
        "url": "mongodb://USER_NAME:PASSWORD@YOUR_MONGO_HOST:YOUR_MONGO_PORT/?authSource=USER_AUTH_SOURCE_DB",
        "db": "DB_NAME"
    },

    USER_NAME:PASSWORD@ and /?authSource=USER_AUTH_SOURCE_DB are required only if you have enabled the authentication of your MongoDB. You do not have to change the other settings in the mongo block.

  3. ElasticSearch connection URL

    "elastic_search": {
        "url": "YOUR_ELASTIC_HOST:YOUR_ELASTIC_PORT"
    },

Import Datasets

  1. Download DBpedia (e.g. DBpedia-2016-04) or any other knowledge graphs with NT/TTL format. We only need the following 3 types of data files.

    1. Ontology (e.g. DBpedia Ontology (nt)). Rename the file into ontology.ttl.
    2. Facts (e.g. DBpedia Mapping-based Objects (ttl), decompess it first). Rename the file into facts.ttl.
    3. Instance Types (e.g. DBpedia Instance Types (ttl), decompess it first). Rename the file into instance_types.ttl.

    Assume you put these files in a folder named DATA_FOLDER.

  2. Convert TTL files into TSV files. We have provided a simple script to do this.

    cd DATA_FOLDER
    python SOURCE_FOLDER/scripts/ttl2tsv.py ontology.ttl facts.ttl instance_types.ttl

    Here, SOURCE_FOLDER is the root directory of this project repository.

  3. Remove the redundant predicate column in instance_types.tsv because all the predicates should be the same and we do not want to store them in the database.

    cd DATA_FOLDER
    cut -f1,3 instance_types.tsv > tmp.tsv && mv tmp.tsv instance_types.tsv
  4. Import data into Postgres.

    cd DATA_FOLDER
    psql -h YOUR_POSTGRES_HOST -p YOUR_POSTGRES_PORT -U DB_USER -d DB_NAME -a -f SOURCE_FOLDER/scripts/import_db.sql

    Use your real parameters for accessing the Postgres.

Setup Environment

  1. Create Python virtual enviroment

    virtualenv -p python3 VENV_HOME
    source VENV_HOME/bin/activate

    You may put this VENV_HOME at any place you like.

  2. Upgrade pip to the latest version

    pip install -U pip
  3. Install Python dependency packages

    cd SOURCE_FOLDER
    pip install -r requirements.txt

Initialize Other Components

  1. Build entity index in ElasticSearch

    source VENV_HOME/bin/activate # if you have not activated the virtual environment
    cd SOURCE_FOLDER
    export PYTHONPATH="$PWD"
    python ./scripts/index_entities.py

Start REVS server

source VENV_HOME/bin/activate # if you have not activated the virtual environment
cd SOURCE_FOLDER
python server.py

Then, open http://localhost:8055 in your browser to access it.

Resources

Please check the wiki for any other resources, e.g. the questions we used in our user-based evaluation.

Citation

Please cite our paper as below if you are using the code or the resources in this repository.

Miao, Y., Qin, J. & Wang, W., 2017. Graph Summarization for Entity Relatedness Visualization. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR ’17. New York, NY, USA: ACM, pp. 1161–1164.

LICENSE

The MIT License (MIT)

Copyright (c) 2017 Yukai Miao

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.