We live in the era of Big Data, and we use
data for everything: from predicting which goods a customer may buy next, to
forecasting the weather, and analyzing traffic in cities, or the spreading of
diseases in the population of a country. Indeed data is an extremely powerful
resource, an enabler for a large portion of our current technology.
However, we should not forget that though data is the foundation underpinning
information processing, the real value is in the information that we are able
to surface by processing the data, in the patterns that we are able to identify
and recognize, and, ultimately, in the knowledge that we extract. So, we can
say that from Big Data we usually distill Small Knowledge, which has Big Value.
The problem of representing knowledge in a machine processable format is a well
known field of Artificial Intelligence, which has been extensively studied
since the 1970s. An approach to knowledge representation based on graph
(mathematical structure represented as sets of nodes, or vertices, which may be
connected by edges) has been discussed for a long time, starting from the
introduction of conceptual graphs [1] to the more recent Linked Data initiative
[2] - a method to publish data and knowledge over the Web by explicitly
representing their relationships, thus enabling computers to directly access
and semantically query such distributed knowledge graphs (example below).
Knowledge graphs are a possible approach to building large knowledge bases,
structured collections of facts about the world that computer systems can use
to reason, and to interact with humans more naturally. These are some of the
key characteristics of cognitive computing. The impact of this new computing
approach are potentially very high, and, combined with other technologies
current under development, it promises to open up whole new ways for humans to
use computers.
Among the various fields of application of cognitive computing, a very
interesting one is the integrated care domain, an emerging worldwide trend
aiming at delivering more effective and coordinated forms of care provision
spanning, among others, the social domain and the health domain. Social care
and health care are knowledge intensive domain, where data and information are
abundant. Delivering insights into the strengths and vulnerabilities of
individuals with respect to the communities they live in and their social
environment represents a key challenge for practitioners in the integrated care
domain.
Link2Outcome [3] [4] is a prototype of a cognitive system that helps care
workers to make more informed decision to improve outcome for patients. Among
other goals, Link2Outcome aims at facilitating access to knowledge across
domains and data sources, and to use such knowledge to summarize the state of
an individuals though a dynamic set of vulnerability indexes. An interesting
example of how Link2Outcome can help doctors is shown in [3]: the system may
import (for example from an open data city portal) linked data describing
pollution levels in a city, and correlate such information with the area where
a patient lives in and with knowledge of how pollution may affect clinical
conditions of the patient. A doctor may see how the vulnerability indexes of
the patien change after the system has ingested the new data, and thus better
understand how the environment may affect this patient.
As an extension to Link2Outcome, the more recent work BlueLENS [5], tackle the
problem of collecting and providing the right information, to the right people,
at the right time across the care continuum. The health of an individual is
increasingly studied from multiple perspective, and analyzing multi-sectoral
determinants of health is becoming a mainstream approach in integrated care. To
enable this approach, data fusion from multiple heterogeneous sources is of key
importance, and cognitive systems are uniquely positioned to help practitioners
in surfacing the right information at the right time.
By establishing a more natural and seamless interaction between doctors and
knowledge systems, cognitive computing is paving the way to revolutionize care,
by bringing better and more timely insights that help experts in taking the
best possible decisions to help patients.
L-R: Martin Stephenson, Vanessa Lopez, Jiewen Wu, Marco Luca Sbodio,
Pierpaolo Tommasi, Spyros Kotoulas, Guruduth Banavar, and Nuno Lopes.
[1] John F. Sowa. 1976. Conceptual graphs for a data base interface. IBM
Journal of Research and Development 20, 4 (July 1976), 336-357. DOI=[http://dx.doi.org/10.1147/rd.204.0336]
[2] Bizer, Christian; Heath, Tom; Berners-Lee, Tim (2009). "Linked
Data—The Story So Far". International Journal on Semantic Web and
Information Systems 5 (3): 1–22. [doi:10.4018/jswis.2009081901]. ISSN
1552-6283.
[4] Spyros Kotoulas, Vanessa Lopez, Marco Luca Sbodio, Martin Stephenson,
Pierpaolo Tommasi, and Pol Mac Aonghusa. 2014. A linked data approach to care
coordination. In Proceedings of the 25th ACM conference on Hypertext and social
media (HT '14). ACM, New York, NY, USA, 77-87. DOI=[http://dx.doi.org/10.1145/2631775.2631807]