Authors:
Ying Zhao
1
;
Charles Zhou
2
and
Jennie K. Bellonio
1
Affiliations:
1
Naval Postgraduate School, Monterey, CA and U.S.A.
;
2
Quantum Intelligence, Inc., Cupertino, CA and U.S.A.
Keyword(s):
Lexical Link Analysis, Crowd-Sourcing, Game Theory, Big Data, Unsupervised Learning, Nash Equilibrium, Social Welfare, Pareto Superior, Pareto Efficient
Related
Ontology
Subjects/Areas/Topics:
Applications and Case-studies
;
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
We demonstrated a machine learning and artificial intelligence method, i.e., lexical link analysis (LLA) to discover innovative ideas from big data. LLA is an unsupervised machine learning paradigm that does not require manually labeled training data. New value metrics are defined based on LLA and game theory. In this paper, we show the value metrics generated from LLA in a use case of an internet game and crowd-sourcing. We show the results from LLA are validated and correlated with the ground truth. The LLA value metrics can be used to select high-value information for a wide range of applications.