Knowledge Graph from Natural Language: Incorporating order from textual chaos

Intro

What If I told you that instead of the age-old saying that "a picture is worth a thousand words", it could be that "a word is worth a thousand pictures"?

Language evolved as an abstraction of distilled information observed and collected from the environment for sophisticated and efficient interpersonal communication and is responsible for humanity's ability to collaborate by storing and sharing experiences. Words represent evocative abstractions over information encoded in our memory and are a composition of many primitive information types.

That is why language processing is a much more challenging domain and witnessed a delayed 'imagenet' moment.

One of the cornerstone applications of natural language processing is to leverage the language's inherent structural properties to build a knowledge graph of the world.

Knowledge Graphs

Knowledge graph is a form of a rich knowledge base which represents information as an interconnected web of entities and their interactions with each other. This naturally manifests as a graph data structure, where nodes represent entities and the relationship between them are the edges.

Automatically constructing and leveraging it in an intelligent system is an AI-hard problem, and an amalgamation of a wide variety of fields like natural language processing, information extraction and retrieval, graph algorithms, deep learning, etc.

It represents a paradigm shift for artificial intelligence systems by going beyond deep learning driven pattern recognition and towards more sophisticated forms of intelligence rooted in reasoning to solve much more complicated tasks.

To elucidate the differences between reasoning and pattern recognition: consider the problem of computer vision: the vision stack processes an image to detect shapes and patterns in order to identify objects - this is pattern recognition, whereas reasoning is much more complex - to associate detected objects with each other in order to meaningfully describe a scene. For this to be accomplished, a system needs to have a rich understanding of the entities within the scene and their relationships with each other.

To understand a scene where a person is drinking a can of cola, a system needs to understand concepts like people, that they drink certain liquids via their mouths, liquids can be placed into metallic containers which can be held within a palm to be consumed, and the generational phenomenon that is cola, among others. A sophisticated vision system can then use this rich understanding to fetch details about cola in-order to alert the user of his calorie intake, or to update preferences for a customer. A Knowledge Graph's 'awareness' of the world phenomenons can thus be used to augment a vision system to facilitate such higher order semantic reasoning.

In production systems though, reasoning may be cast into a pattern recognition problem by limiting the scope of the system for feasibility, but this may be insufficient as the complexity of the system scales or we try to solve general intelligence.

Challenges in building a Knowledge Graph

There are two primary challenges towards integrating knowledge graphs in systems: acquisition of knowledge and construction of the graph and effectively leveraging it with robust algorithms to solve reasoning tasks. Creation of the knowledge graph can vary widely depending on the breadth and complexity of the domain - from just manual curation to automatically constructing it by leveraging unstructured/semi-structured sources of knowledge, like books and Wikipedia.

Many natural language processing tasks are precursors towards building knowledge graphs from unstructured text, like syntactic parsing, information extraction, entity linking, named entity recognition, relationship extraction, semantic parsing, semantic role labeling, entity disambiguation, etc. Open information extraction is an active area of research on extracting semantic triplets of object ('John'), predicate ('eats'), subject ('burger') from plain text, which are used to build the knowledge graph automatically.

A very interesting approach to this problem is the extraction of frame semantics. Frame semantics relates linguistic semantics to encyclopedic knowledge and the basic idea is that the meaning of a word is linked to all essential knowledge that relates to it, for eg. to understand the word "sell", it's necessary to also know about commercial transactions, which involve a seller, buyer, goods, payment, and the relations between these, which can be represented in a knowledge graph.

This workshop will focus on building such a knowledge graph from unstructured text.

Learn good research practices like organizing code and modularizing output for productive data wrangling to improve algorithm performance.

Knowledge Graph at Embibe

We will showcase how Embibe's proprietary Knowledge Graph manifests and how it's leveraged across a multitude of projects in our Data Science Lab.

 
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Outline/Structure of the Workshop

  1. A brief note on Language and it's evolution
  2. Knowledge Graphs: What and Why?
  3. Building Knowledge Graph from unstructured text:
    • Data preparation: collection and pre-processing (information extraction)
    • Text ingestion on elastic search
    • Data Wrangling to improve quality of preprocessing, notes on good research practices
    • Extracting entities and relationships from text (NER, relationship extraction)
  4. Ingesting Knowledge Graph into Neo4j
  5. Visualizing the Knowledge Graph
  6. Further Improvements
  7. How to use a Knowledge Graph in a smart system?
  8. Stretch goal:
    • Using the knowledge graph to answer text queries
  9. Q/A session

Learning Outcome

- Understand what are Knowledge Graphs and their usage in production

- Learn to build Knowledge Graph from unstructured text and deploy it to a production graph database (neo4j)

- Learn good research practices and understand the data science workflow of improving system performance, including data wrangling

- (Hopefully) Appreciate the beauty and complexity of language from an evolutionary perspective

Target Audience

Data Scientists, NLP Engineers, Deep Learning Engineers, Machine Learning Engineers, Research Scholars

Prerequisites for Attendees

  1. Familiarity with Python, algorithms, machine learning and deep learning
  2. *Jupyter/Google colab environment with following setup:
    • Env: python3
    • Packages used: nltk, spacy, stanford NLP, keras, tensorflow, gensim
  3. Locally setup elastic search and Neo4j (if using jupyter locally)

* Link to Google colab notebook will be provided which can also be followed without any prior setup (elastic search and Neo4j pre-setup on Google Cloud).

schedule Submitted 4 days ago

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