=Paper=
{{Paper
|id=Vol-1963/paper478
|storemode=property
|title=Semantic Concept Discovery Over Event Data
|pdfUrl=https://ceur-ws.org/Vol-1963/paper478.pdf
|volume=Vol-1963
|authors=Oktie Hassanzadeh,Shari Trewin,Alfio Massimiliano Gliozzo
|dblpUrl=https://dblp.org/rec/conf/semweb/HassanzadehTG17
}}
==Semantic Concept Discovery Over Event Data==
https://ceur-ws.org/Vol-1963/paper478.pdf
Semantic Concept Discovery Over Event Data
Oktie Hassanzadeh, Shari Trewin, and Alfio Gliozzo
IBM Research, USA
Preparing a comprehensive, accurate, and unbiased report on a given topic or
question is a challenging task. The first step is often a daunting discovery task
that requires searching through an overwhelming number of information sources
without introducing bias from the analyst’s current knowledge or limitations
of the information sources. A common requirement for many analysis reports
is a deep understanding of various kinds of historical and ongoing events that
are reported in the media. To enable better analysis based on events, there exist
several event databases containing structured representations of events extracted
from news articles. Examples include GDELT [4], ICEWS [1], and EventReg-
istry [3]. These event databases have been successfully used to perform various
kinds of analysis tasks, e.g., forecasting societal events [6]. However, there has
been little work on the discovery aspect of the analysis, that results in a gap
between the information requirements and the available data, and potentially a
biased view of the available information.
In this presentation, we describe a framework for concept discovery over
event databases using semantic technologies. Unlike existing concept discovery
solutions that perform discovery over text documents and in isolation from the
remaining data analysis tasks [5, 8], our goal is providing a unified solution that
allows deep understanding of the same data that will be used to perform other
analysis tasks (e.g., hypothesis generation [7] or building models for forecasting
[2]). Figure 1 shows the architecture of our system. The system takes in as in-
put a set of event databases and RDF knowledge bases and provides as output
a set of APIs that provide a unified retrieval mechanism over input data and
knowledge bases, and an interface to a number of concept discovery algorithms.
Figures 2 shows different portions of our system’s UI that is built using our
concept discovery framework APIs. The analyst can enter a natural language
question or a set of concepts, and retrieve collections of relevant concepts iden-
tified and ranked using different concept discovery algorithms. A key aspect of
our framework is the use of semantic technologies. In particular:
– A unified view over multiple event databases and a background RDF knowl-
edge base is achieved through semantic link discovery and annotation.
– Natural language or keyword query understanding is performed through
mapping of input terms to the concepts in the background knowledge base.
– Concept discovery and ranking is performed through neural network based
semantic term embeddings.
We will present the results of our detailed evaluation of our proposed concept
discovery techniques. We prepared a ground truth from reports on specific topics
written by human experts, including reports from the Human Rights Watch or-
Knowledge
Sources
DBpedia
Ingestion: Curation: Semantic Term
Wikidata Crawl, Parse, Pre-process, Match, Embeddings
… Clean/Filter, Store Index Creation
Event Databases
GDELT
Events GKG
Event Knowledge Graph &
Embeddings
ICEWS Concept Discovery APIs
Management System
EventRegistry SolrCloud
Fig. 1: System Architecture
Fig. 2: Views from the Question Analysis UI
ganization, and Wikipedia pages on people and events. The ground truth queries
included hand-built test queries on various topics, and an automatically gener-
ated set of queries based on the title of the reports. Given only these query terms,
we measure the ability of different algorithms to find the concepts mentioned in
the original reports. Our study finds that combining our neural network based
semantic term embeddings over structured data with an index-based method
can significantly outperform either method alone.
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