=Paper=
{{Paper
|id=Vol-3254/paper399
|storemode=property
|title=Knowledge-Based News Event Analysis Toolkit
|pdfUrl=https://ceur-ws.org/Vol-3254/paper399.pdf
|volume=Vol-3254
|authors=Oktie Hassanzadeh,Parul Awasthy,Ken Barker,Onkar Bhardwaj,Debarun Bhattacharjya,Mark Feblowitz,Aamod Khatiwada,Lee Martie,Steve Fonin Mbouadeu,Jian Ni,Anik Saha,Sola Shirai,Kavitha Srinivas,Lucy Yip
|dblpUrl=https://dblp.org/rec/conf/semweb/HassanzadehA0BB22
}}
==Knowledge-Based News Event Analysis Toolkit==
https://ceur-ws.org/Vol-3254/paper399.pdf
Knowledge-Based News Event Analysis Toolkit
Oktie Hassanzadeh1 , Parul Awasthy1 , Ken Barker1 , Onkar Bhardwaj1 ,
Debarun Bhattacharjya1 , Mark Feblowitz1 , Aamod Khatiwada1,3,† , Lee Martie1 ,
Steve Fonin Mbouadeu1,4,† , Jian Ni1 , Anik Saha1,2,† , Sola Shirai1,2,† , Kavitha Srinivas1
and Lucy Yip1
1
IBM Research, Yorktown Heights, NY, USA
2
Rensselaer Polytechnic Institute, Troy, NY, United States
3
Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
4
St. John’s University, NY, USA
Abstract
We present an overview of our knowledge-based news event analysis toolkit. The toolkit is powered by
a knowledge graph (KG) of event-related concepts and relations curated from Wikidata and enriched
through knowledge extraction from text as well as a variety of link prediction methods. We describe each
of the functions the toolkit provides and an overview of its various components. We present use cases in
enterprise risk management, scenario planning, and media intelligence. We also discuss a number of
lessons learned and directions for future research.
Businesses – large and small – can benefit tremendously from monitoring ongoing global
and local newsworthy events and analyzing how recent events could impact their businesses.
One mechanism of analysis is curating a rich knowledge graph (KG) of past events and their
consequences, such that ongoing events can be mapped to past similar events in the KG, and
one can reason about what caused them and what can happen as a result. As a simple example,
back in January 8 2020, when news stories started reporting on the announcement that the
World Health Organization (WHO) made on a new virus that has caused a pneumonia outbreak
in Wuhan, China, one could immediately identify past similar news events, which include
WHO’s announcement in March 2003 that marked the onset of the 2002–2004 SARS outbreak.
A business involved in tourism or oil & gas industries can then immediately start taking actions
to prepare for the potential impact of a major disease outbreak on their businesses.
In this talk, we present an overview of a toolkit that enables building knowledge-based news
event analysis solutions. Our goal in developing this toolkit is twofold: 1) providing news event
analysis functions based on a rich curated knowledge base from publicly available sources 2)
providing knowledge extraction functions used to curate our knowledge base such that users
The 21st International Semantic Web Conference (ISWC2022), October 23-27, 2022
†
Work done while at IBM Research.
$ hassanzadeh@us.ibm.com (O. Hassanzadeh); awasthyp@us.ibm.com (P. Awasthy); kjbarker@us.ibm.com
(K. Barker); onkarbhardwaj@ibm.com (O. Bhardwaj); debarunb@us.ibm.com (D. Bhattacharjya); mfeb@us.ibm.com
(M. Feblowitz); khatiwada.a@northeastern.edu (A. Khatiwada); Lee.Martie@ibm.com (L. Martie);
steve.mbouadeu19@my.stjohns.edu (S. F. Mbouadeu); nij@us.ibm.com (J. Ni); sahaa@rpi.edu (A. Saha);
shiras2@rpi.edu (S. Shirai); kavitha.srinivas@ibm.com (K. Srinivas); Lucy.Yip@ibm.com (L. Yip)
0000-0001-5307-9857 (O. Hassanzadeh); 0000-0003-0899-8904 (K. Barker); 0000-0001-5720-1207 (A. Khatiwada);
0000-0002-9137-407X (S. F. Mbouadeu); 0000-0001-6913-3598 (S. Shirai)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Neural Language Structured Sources Textual Sources of
Models of Knowledge Causal Knowledge
SEC
Event Identification
Causal Knowledge Extraction
Neural Question Pattern Matching + Neural Relation
Third-Party Neural Concept
Answering Models Neural NLI Models Extraction Models
News Providers Linking
Knowledge Graph of Events & Consequences Event Sequences Analysis
e1 e2
Zero-Shot … Event Sequence Event Sequence
…
Text Classifier Models Extraction
Examples
…
…
Event Analysis & Forecasting APIs
Profile
News Retrieval Event Identification Causal Analysis & Forecasting Causal Knowledge Extraction
Figure 1: Toolkit Components and APIs
can augment the included knowledge base or curate a custom knowledge base for their domain
of interest. Figure 1 presents the current architecture of our toolkit [1]. We outline several
challenges we faced in applying state-of-the-art concept linking, knowledge extraction, and link
prediction techniques to build our toolkit, provide a summary of lessons learned, and present a
number of research challenges that need to be addressed. In particular:
• We outline the use of Wikidata as a primary source of knowledge, report on the challenges
we faced with respect to the current coverage of event-related concepts in Wikidata, and
how the existing knowledge in Wikidata can be enriched through automated knowledge
extraction over Wikipedia articles. Our primary focus has been on weakly supervised
and supervised neural models for causal relation extraction.
• We describe our solution for mapping news headlines to concepts in our KG, and report
on challenges in applying existing concept linking methods to this problem.
• We report on the performance of several rule-based and knowledge graph embeddings
based approaches for link prediction to enrich our KG. We also report on the challenges
we faced in applying existing techniques for reasoning about potential consequences of a
new event as a novel mechanism for event forecasting.
• We also report on our preliminary results on automatically extracting structured event
sequences from textual corpora and applying event sequence models as a mechanism of
learning complex relations between event types in our KG.
References
[1] O. Hassanzadeh, P. Awasthy, K. Barker, O. Bhardwaj, D. Bhattacharjya, M. Feblowitz,
L. Martie, J. Ni, K. Srinivas, L. Yip, Knowledge-based news event analysis & forecasting
toolkit, in: IJCAI (Demonstration Track), 2022.