screaper

Don't fear the scReaper

Who buys what from Whom?

Iteration One

Niche market focus: Bearings

Who?

We identify the list of companies active in this field.

Buys what?

We will identify the list of products associated with each seller.

From whom?

We will apply relation extraction, as well as design the platform to ask people where they sell, to make similar recommendations.

Further installation steps

Pre-requisite:

python -m deeppavlov install ner_ontonotes_bert_mult

Resources:

• Record all contacts and companies that could be useful on HubSpot
• Record all TODOs on this Google Sheet

Ideas

• Treat this as an ontology learning problem

• Create a database of all documents, then you can apply tf-idf for document similarity

• Create a database of all edges between entities, entitiy-classifications and entity-link. You can then apply supervised learning (e.g. graph neural networks) to infer structure on such websites. You would need to a manually crafted website for this.

• Each node can have PoS, NER information, and any other NLP information

• Only if a node occurs multiple times, and is classified as a specific NER, use it as a NER.

• Only use text of certain length to classify as an entity.

• Save all extracted entities into a database, and then do big data operations on this (do a script-first implementation)

• On this edge-set apply matrix factorization techniques to induce hidden relations

Incredibly good talk

• https://www.youtube.com/watch?v=vf0t2R0FZ5M

• index all websites around key entities (organizations, brands, products)

• -> how to determine a key entity

• entities, attributes, relationships, trends, events, bare analytics, predictions over time, connect them

• 1.3T documents, 4-5 biggest elasticsearch cluster

• recommender engines, semantic searchers, contextual searchers, inference engines

• closed domains around companies and related entities

• knowledge graph embedding

• base-url as entitiy (?) (bcs organization).

• then do entity-label-matching (title of html)

• rule-based feature engineering

• 2.5k features per website

• predictor on what sites to prioritize by the terms included in the data

• NLP tasks:

  • language detection

  • text similarity

  • text classification

  • POS

  • Noun Chunking

  • Dependency Parsing

  • Named Entity Recognition

  • Named Entity Disambiguation

  • Key Phrases

  • Concept Extraction ->

  • Sentiment Analysis

  • Relation Extraction

  • Summarization

  • NER: CRF models, another one BERT models

• populate knowledge graph with links from web

• DBPedia Schema

• AGDISTIS NED

• apache lucene triplet store

• ontology of 100s of tasks

  • based on semantic rules on NLP tasks

• topic classifier

• record linking

• schema mapping, normalization, blocking, pairwise similarity classification, agglorometive clustering, cluster UUID assignment

• truth discovery with majority voting

• human validation of feedback loop

• check out later: https://www.youtube.com/watch?v=UGPT64wo7lU

-> start scraping the website already? (on bob)

• 700M documents per day ; 120 articles/s

Quick Start

Install all dependencies

• pip install -r kubernetes/scraper_language_model/requirements.txt
• sudo apt-get install python3 python-dev python3-dev \ build-essential libssl-dev libffi-dev \ libxml2-dev libxslt1-dev zlib1g-dev \ python-pip
• pip install --upgrade pip
• pip install deeppavlov
• python -m deeppavlov install ner_ontonotes_bert_mult
• python -c "from deeppavlov import configs, build_model; build_model(configs.ner.ner_ontonotes_bert_mult, download=True)"

Install PostgreSQL and create database

• sudo apt update
• sudo apt install postgresql postgresql-contrib
• sudo -i -u postgres
• CREATE DATABASE screaper;

Instantiate the tables

• python -m screaper.resources.entities

Graph Neural Networks

• https://towardsdatascience.com/overview-of-deep-learning-on-graph-embeddings-4305c10ad4a4

-> Perhaps do not include any scripts that are not company pages, or just be more careful which pages to scrape, and which ones to just skip

Run with higher priority

nice -n 17 python -m screaper.core.main
nice -n 17 python -m screaper.engine.core.