Learning Entity embedding’s form Knowledge Graph
- Over a period of time, a lot of Knowledge bases have evolved. A knowledge base is a structured way of storing information, typically in the following form Subject, Predicate, Object
- Such Knowledge bases are an important resource for question answering and other tasks. But they often suffer from their incompleteness to resemble all the data in the world, and thereby lack of ability to reason over their discrete Entities and their unknown relationships. Here we can introduce an expressive neural tensor network that is suitable for reasoning over known relationships between two entities.
- With such a model in place, we can ask questions, the model will try to predict the missing data links within the trained model and answer the questions, related to finding similar entities, reasoning over them and predicting various relationship types between two entities, not connected in the Knowledge Graph.
- Knowledge Graph infoboxes were added to Google's search engine in May 2012
What is the knowledge graph?
▶Knowledge in graph form!
▶Captures entities, attributes, and relationships
▶More specifically, the “knowledge graph” is a database that collects millions of pieces of data about keywords people frequently search for on the World wide web and the intent behind those keywords, based on the already available content
▶In most cases, KGs is based on Semantic Web standards and have been generated by a mixture of automatic extraction from text or structured data, and manual curation work.
▶Structured Search & Exploration
e.g. Google Knowledge Graph, Amazon Product Graph
▶Graph Mining & Network Analysis
e.g. Facebook Entity Graph
▶Big Data Integration
e.g. IBM Watson
▶Diffbot, GraphIQ, Maana, ParseHub, Reactor Labs, SpazioDati
Outline/Structure of the Case Study
- Knowledge Graph: Overview
- Use Cases for Knowledge Graph
- Knowledge Graph Databases
- Present Activities and perspective
I am using the knowledge graph of Freebase dataset obtained from https://datahub.io/dataset/freebase
I am using the dataset as a triple relationship type i.e. having a Subject, Relationship type/Predicate and an Object.
The Freebase dataset basically consists of 13 Predicates using which it tries to derive hidden connections between any two Subjects or Objects with a probability score, thereby also able to predict a Relationship between two Subjects or Objects which were initially not known to have any relation.
Following are the relations we are having in our dataset:
Use the term “Entity” to denote a subject or an Object. We use the term “relation” to denote a predicate.
In the dataset, we have about 75043 unique Entities to be modeled, so that one’s, which are similar to each other, are closer in the feature space.
People having basic knowledge of Machine Learning algorithm & AI
Prerequisites for Attendees
- Familiarity with algorithms, machine learning, and deep learning
schedule Submitted 2 months ago
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