Spangler Accelerating Discovery

Mining Unstructured Information for Hypothesis Generation
Erscheinungsjahr 2015
ISBN: 978-1-4822-3914-0
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 292 Seiten

Reihe: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Unstructured Mining Approaches to Solve Complex Scientific Problems

As the volume of scientific data and literature increases exponentially, scientists need more powerful tools and methods to process and synthesize information and to formulate new hypotheses that are most likely to be both true and important. Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation describes a novel approach to scientific research that uses unstructured data analysis as a generative tool for new hypotheses.

The author develops a systematic process for leveraging heterogeneous structured and unstructured data sources, data mining, and computational architectures to make the discovery process faster and more effective. This process accelerates human creativity by allowing scientists and inventors to more readily analyze and comprehend the space of possibilities, compare alternatives, and discover entirely new approaches.

Encompassing systematic and practical perspectives, the book provides the necessary motivation and strategies as well as a heterogeneous set of comprehensive, illustrative examples. It reveals the importance of heterogeneous data analytics in aiding scientific discoveries and furthers data science as a discipline.

Autoren/Hrsg.

Spangler, Scott

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie

Inhaltsverzeichnis

Introduction

Why Accelerate Discovery?

Scott Spangler and Ying Chen
THE PROBLEM OF SYNTHESIS

THE PROBLEM OF FORMULATION

WHAT WOULD DARWIN DO?

THE POTENTIAL FOR ACCELERATED DISCOVERY: USING COMPUTERS TO MAP THE KNOWLEDGE SPACE

WHY ACCELERATE DISCOVERY: THE BUSINESS PERSPECTIVE

COMPUTATIONAL TOOLS THAT ENABLE ACCELERATED DISCOVERY

ACCELERATED DISCOVERY FROM A SYSTEM PERSPECTIVE

ACCELERATED DISCOVERY FROM A DATA PERSPECTIVE

ACCELERATED DISCOVERY IN THE ORGANIZATION

CHALLENGE (AND OPPORTUNITY) OF ACCELERATED DISCOVERY

Form and Function

THE PROCESS OF ACCELERATED DISCOVERY

CONCLUSION

Exploring Content to Find Entities

SEARCHING FOR RELEVANT CONTENT

HOW MUCH DATA IS ENOUGH? WHAT IS TOO MUCH?

HOW COMPUTERS READ DOCUMENTS

EXTRACTING FEATURES

FEATURE SPACES: DOCUMENTS AS VECTORS

CLUSTERING

DOMAIN CONCEPT REFINEMENT

MODELING APPROACHES

DICTIONARIES AND NORMALIZATION

COHESION AND DISTINCTNESS

SINGLE AND MULTIMEMBERSHIP TAXONOMIES

SUBCLASSING AREAS OF INTEREST

GENERATING NEW QUERIES TO FIND ADDITIONAL RELEVANT CONTENT

VALIDATION

SUMMARY

Organization

DOMAIN-SPECIFIC ONTOLOGIES AND DICTIONARIES

SIMILARITY TREES

USING SIMILARITY TREES TO INTERACT WITH DOMAIN
EXPERTS

SCATTER-PLOT VISUALIZATIONS

USING SCATTER PLOTS TO FIND OVERLAPS BETWEEN NEARBY ENTITIES OF DIFFERENT TYPES

DISCOVERY THROUGH VISUALIZATION OF TYPE SPACE

Relationships

WHAT DO RELATIONSHIPS LOOK LIKE?

HOW CAN WE DETECT RELATIONSHIPS?

REGULAR EXPRESSION PATTERNS FOR EXTRACTING
RELATIONSHIPS

NATURAL LANGUAGE PARSING

COMPLEX RELATIONSHIPS
EXAMPLE: P53 PHOSPHORYLATION EVENTS

PUTTING IT ALL TOGETHER

EXAMPLE: DRUG/TARGET/DISEASE RELATIONSHIP
NETWORKS

CONCLUSION

Inference

CO-OCCURRENCE TABLES

CO-OCCURRENCE NETWORKS

RELATIONSHIP SUMMARIZATION GRAPHS

HOMOGENEOUS RELATIONSHIP NETWORKS

HETEROGENEOUS RELATIONSHIP NETWORK

Über Autor(innen)

Scott Spangler is a principal data scientist, distinguished engineer, and master inventor in the Watson Innovations Group at the IBM Almaden Research Center. He has been involved with knowledge base and data mining research for the past 25 years. His recent work has applied Watson technology to help accelerate cancer research. He holds 45 patents and is the author of over 30 publications. He received a BS in mathematics from MIT and an MS in computer science from the University of Texas.