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

 
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Outline/Structure of the Case Study

  • Knowledge Graph: Overview
  • Use Cases for Knowledge Graph
  • Knowledge Graph Databases
  • Present Activities and perspective

Learning Outcome

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:

  • gender
  • nationality
  • profession
  • place_of_death
  • place_of_birth
  • location
  • institution
  • cause_of_death
  • religion
  • parents
  • children
  • ethnicity
  • spouse

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.

Target Audience

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

Public Feedback

comment Suggest improvements to the Speaker
  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  1 month ago
    reply Reply

    Dear Suvro: The learning objective from the talk is not clear to me. Also, could you please considering include a video of your talk or a recorded introduction to the topic?

    Warm Regards

    Vikas


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    • completeness
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    Gesture Detection: https://www.youtube.com/watch?v=rDSuCnC8Ei0

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    Introduction

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    Predictive maintenance

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    Virtual Assistants

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    Vodafone introduced its new chatbot — TOBi to handle a range of customer service-type questions. The chatbotscales responses to simple customer queries, thereby delivering the speed that customers demand. Nokia’s virtual assistant MIKA suggests solutions for network issues, leading to a 20% to 40% improvement in first-time resolution.

    Robotic process automation (RPA)

    CSPs all have vast numbers of customers and an endless volume of daily transactions, each susceptible to human error. Robotic Process Automation (RPA) is a form of business process automation technology based on AI. RPA can bring greater efficiency to telecommunications functions by allowing telecoms to more easily manage their back office operations and the large volumes of repetitive and rules-based processes. By streamlining execution of once complex, labor-intensive and time-consuming processes such as billing, data entry, workforce management and order fulfillment, RPA frees CSP staff for higher value-add work.

    According to a survey by Deloitte, 40% of Telecom, Media and Tech executives say they have garnered “substantial” benefits from cognitive technologies, with 25% having invested $10 million or more. More than three-quarters expect cognitive computing to “substantially transform” their companies within the next three years.

    Summary

    Artificial intelligence applications in the telecommunications industry is increasingly helping CSPs manage, optimize and maintain not only their infrastructure, but their customer support operations as well. Network optimization, predictive maintenance, virtual assistants and RPA are examples of use cases where AI has impacted the telecom industry, delivering an enhanced CX and added value for the enterprise overall.

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    Advanced

    The problem of network behavior prediction has been an ongoing study by researchers for quite a while now. Network behavior typically exhibits a complex sequential pattern and is often difficult to predict. Nowadays there are several techniques to predict the degradation in Network KPIs like throughput, latency etc., using various machine learning techniques like Deep Neural Networks, where the initial layers have learnt to map the raw features like performance counter measurements, weather, system configuration details etc into a feature space where classification by the final layers can be performed.

    Given the initial number of counters( which constitutes the dimensions) is substantial (more than 2000 in number) the problem requires huge amount of data to train the Deep Neural Networks. Often this needs resources and time and more importantly this requires provisioning of huge amount of data for every trial. Given each node generates huge amount of data ( data on every 2000 counters generated at 15 minutes interval for each of 6 cells in an eNodeB) and the data needs to be transported across several hundred of eNodeBs to one central data center, it requires a very fat data pipe and consequently huge investment to enable a predictive fault prediction apparatus across the network.

    The alernative is to have a compute infrastructure at the node and take the intelligence at the edge. However the challenge is given the huge amount of data generated in a single node having a compute at each node was proving to be expensive. Nowadays this compute requirement at node could be reduced through use of transfer learning. However the other challenge is on sharing the intelligence and developing a system which is collectively intelligent across nodes.

    Network topology, climate features and user patterns vary across regions and service providers and hence an unique model is often necesarry to serve the node. However in order to deal with unseen patterns intelligence from other nodes can be useful which leads us to building an global model which again leads to the challenge of fat data pipeline requirement which makes it commercially less attractive.

    In order to get around this challenge, an combination of federated learning is used in combination with transfer learning.

    This presentation details such deep learning architectures which combines federated learning with transfer learning to enable construction and updation of Global models which imbibes intelligence from nodes but can be constructed by a consensus mechanism whereby weights and changes to weights of local models are shared to global. Also the local models are periodically updated once global model update iteration is complete. Further updation of local models is only done in final layers and initial layers are freezed. This reduces the compute requirement at node also...

    The above principles are being implemented as First of a kind implementation and has prooved to be a success across multiple customers in delivering a compelling ML enabled fault prediction and self-healing mechanism but keeping the investments in infrastructure lower than would have been required in traditional Deep Learning architectures

    This talk will specifically detail the leverage of above principles of federated and transfer learning on LSTMs..