Continuous Semantics to Analyze Real Time Data
Continuous Semantics to Analyze Real-Time Data
Amit Sheth, Christopher Thomas, and Pankaj Mehra • Wright State University
We’ve made significant progress in
applying semantics and Semantic Web
technologies in a range of domains. A
relatively well-understood approach to reaping
semantics’ benefits begins with formal modeling
of a domain’s concepts and relationships,
typically as an ontology. Then, we extract relevant
facts — in the form of related entities —
from the corpus of background knowledge and
use them to populate the ontology. Finally, we
apply the ontology to extract semantic metadata
or to semantically annotate data in unseen or
new corpora.
Using annotations yields semanticsenhanced
experiences for search, browsing,
integration, personalization, advertising, analysis,
discovery, situational awareness, and so
on.1 This typically works well for domains that
involve slowly evolving knowledge concentrated
among deeply specialized domain experts and
that have definable boundaries. A good example
is the US National Center for Biomedical Ontologies,
which has approximately 200 ontologies
used for annotations, improved search, reasoning,
and knowledge discovery. Concurrently,
major search engines are developing and using
large collections of domain-relevant entities as
background knowledge, to support semantic or
facet search.
However, this approach has difficulties dealing
with dynamic domains involved in social,
mobile, and sensor webs. Here, we look at how
continuous semantics can help us model those
domains and analyze the related real-time data.
The Challenge of Modeling Dynamic Domains
