4.4 Subsection of the Refinitiv Knowledge Graph

4.3 Benchmark Dataset Creation

4.2 Knowledge Base Overview

Chapter 4Refinitiv Knowledge Graph

3.5 Optimisation Pipeline

3.4 Framework Implementation

3.3 Data Formats

3.2 Framework Requirements

3.1 Current Link Prediction Pipelines

Chapter 3Link Prediction Framework

2.8 Dynamic Link Prediction

2.7.1 Filtering

2.7 Performance Metrics

2.6 Sampling Strategies

2.5 Latent Feature Models

2.4 Link Prediction

2.3 Knowledge Graph Incompleteness

2.2 Knowledge Base Construction

KGs have recently gained popularity as means of computationally storing large do-mains of knowledge, providing a more intuitive, accurate and flexible representa-tion of the world compared to alternative Knowledge Base (KB) representations

2.1 Knowledge Representation

Chapter 2Background

1.2 Aims and Objectives

4 OPEN PROBLEMS

3.3 Other Scenarios

3.2.2 Text

3.2 Non-structural Scenarios

3.1.3 Knowledge graph

3.1 Structural Scenarios

3 APPLICATIONS

2.3.3 Graph Networks

2.3.2 Non-local Neural Networks

2.3.1 Message Passing Neural Networks

2.3 General Frameworks

2.2.3 Training Methods

2.2.2 Propagation Types

2.2 Variants of Graph Neural Networks

2.1 Graph Neural Networks

2 MODELS

1 INTRODUCTION

VIII. CONCLUSION

F. Automatic Construction and Dynamics

E. Knowledge Aggregation

D. Scalability

C. Interpretability

B. Unified Framework

A. Complex Reasoning

VII. FUTUREDIRECTIONS

C. Recommender Systems

B. Question Answering

A. Natural Language Understanding

VI. KNOWLEDGE-AWAREAPPLICATIONS

D. Temporal Logical Reasoning

C. Temporal Relational Dependency

A. Temporal Information Embedding

B. Entity Dynamics

V. TEMPORALKNOWLEDGEGRAPH

D. Summary

C. Relation Extraction

B. Entity Discovery

A. Knowledge Graph Completion

IV. KNOWLEDGEACQUISITION

E. Summary

D. Embedding with Auxiliary Information

C. Encoding Models

B. Scoring Function

A. Representation Space

III. KNOWLEDGEREPRESENTATIONLEARNING

C. Categorization of Research on Knowledge Graph

B. Definitions and Notations

A. A Brief History of Knowledge Bases

II. OVERVIEW

I. INTRODUCTION

Act Immediately Upon Every Prompting You Get

Intellectual Transformation Through Learning And Journaling

Emotional Transformation Through Reflection

So What Should You Do First Thing In The Morning?

The One Metric That Matters

More generally we can see four cases as shown in the diagram below.

Aligning the NSM and the Top KPI

The NSM and the Top KPI are not enemies

The North Star Metric

The Top Business KPI

At its heart, the core mission of every org is to deliver value, ideally to a big market, and to capture value back.

The Value Loop

Attending conferences

Giving talks

Writing code

Writing papers

“incremental work” (this is the worst adjective possible in academia). Incremental work is a paper that enhances something existing by making it more complex and gets 2% extra on some benchmark.

Research topics

Inspiration from other disciplines

In convergence, you are discarding sources of information that aren’t relevant, eliminating options for how to do things, and producing tangible deliverables that can be shared with others – the scope of your work is converging toward an end point.

In divergence, you are consuming diverse sources of information, investigating new approaches and ways of thinking, and experimenting with different solutions. The number of things you are looking at and tracking is increasing – the scope of your work is diverging from an initial starting point.

The basic idea is that all creative work is completed in one of these two modes, which alternate back and forth: