Martin Stephenson

The success of a society is often judged by its ability to support the most vulnerable. Supporting the most vulnerable individuals is extremely challenging from an information needs perspective, since it requires data from numerous domains and systems, including Social Care, Healthcare, Public Safety and Juridical systems. Information sharing on this scale gives rise to scientific and technical challenges with regard to data representation, access, integration and retrieval granularity. This is… Show more

The success of a society is often judged by its ability to support the most vulnerable. Supporting the most vulnerable individuals is extremely challenging from an information needs perspective, since it requires data from numerous domains and systems, including Social Care, Healthcare, Public Safety and Juridical systems. Information sharing on this scale gives rise to scientific and technical challenges with regard to data representation, access, integration and retrieval granularity. This is a practice-oriented paper presenting a Linked Data-based approach that is uniquely positioned to access and surface information across domains and data sources using a combination of vulnerability indexes and contextual exploration. We apply this approach on a set of enterprise systems from IBM to develop an information sharing architecture and prototype for Care Coordination with a focus on Social Care and Healthcare. We report on expert feedback and user studies that indicate that our approach indeed reduces the time required to gain some business insight while maintaining the flexibility of a Linked Data-based integration approach Show less

Patient-Centric Care requires comprehensive visibility into the strengths and vulnerabilities of individuals and populations. The systems involved in Patient-Centric Care are numerous and heterogeneous, span medical, behavioral and social domains and must be coordinated across government and NGO stakeholders in Health Care, Social Care and more. We present a system, based on Linked Data technologies, taking first steps in making this cross-domain information accessible and fit-for-use, using… Show more

Patient-Centric Care requires comprehensive visibility into the strengths and vulnerabilities of individuals and populations. The systems involved in Patient-Centric Care are numerous and heterogeneous, span medical, behavioral and social domains and must be coordinated across government and NGO stakeholders in Health Care, Social Care and more. We present a system, based on Linked Data technologies, taking first steps in making this cross-domain information accessible and fit-for-use, using minimal structure and open vocabularies. We evaluate our system through user studies. Show less

More and more urban data is published every day, and consequently, consumers want to take advantage of this body of knowledge. Unfortunately, metadata and schema information around this content is sparse. To effectively fulfill user information needs, systems must be able to capture user intent and context in order to evolve beyond current search and exploration techniques. A Linked Data approach is uniquely positioned to surface information and provide interoperability across a diversity of… Show more

More and more urban data is published every day, and consequently, consumers want to take advantage of this body of knowledge. Unfortunately, metadata and schema information around this content is sparse. To effectively fulfill user information needs, systems must be able to capture user intent and context in order to evolve beyond current search and exploration techniques. A Linked Data approach is uniquely positioned to surface information and provide interoperability across a diversity of information sources, from consumer data residing in the original enterprise systems, to relevant open city data in tabular form. We present a prototype for contextual knowledge mining that enables federated access and querying of entities across hundreds of enterprise and open datasets pertaining to cities. The proposed system is able to (1) lift raw tabular data into a connected and meaningful structure, contextualized within the Web of Data, and (2) support novel search and exploration tasks, by identifying closely related entities across datasets and models. Our user experiments and prototype show how semantics, used to consolidate city information and reuse assets from the Web of Data, improve dataset search and provide users effective means to explore related entities and content to fit their information needs. Show less

We present an approach to access and consolidate complex information spanning multiple specialist domains and make it available to non-experts. We are using a combination of business rules and contextual exploration to reduce interface complexity and improve consumability. We present a use case and a prototype on top of a real-world enterprise solution for coordinating Social care and Health care. We evaluate our system through a user study. Our results indicate that our approach reduces the… Show more

We present an approach to access and consolidate complex information spanning multiple specialist domains and make it available to non-experts. We are using a combination of business rules and contextual exploration to reduce interface complexity and improve consumability. We present a use case and a prototype on top of a real-world enterprise solution for coordinating Social care and Health care. We evaluate our system through a user study. Our results indicate that our approach reduces the time required to obtain business results compared to a baseline graph exploration approach. Show less

We present SPUD, a semantic environment for cataloguing, exploring, integrating, understanding, processing and transforming urban information. A series of challenges are identied: namely, the heterogeneity of the domain and the impracticality of a common model, the volume of information and the number of data sets, the requirement for a low entry threshold to the system, the diversity of the input data, in terms of format, syntax and update frequency (streams vs static data), the complex data… Show more

We present SPUD, a semantic environment for cataloguing, exploring, integrating, understanding, processing and transforming urban information. A series of challenges are identied: namely, the heterogeneity of the domain and the impracticality of a common model, the volume of information and the number of data sets, the requirement for a low entry threshold to the system, the diversity of the input data, in terms of format, syntax and update frequency (streams vs static data), the complex data dependencies and the sensitivity of the information. We propose an approach for incremental and continuous integration of static and streaming data, based on SemanticWeb technologies and apply our technology to a traffic diagnosis scenario. We demonstrate our approach through a system operating on real data in Dublin and we show that semantic technologies can be used to obtain business results in an environment with hundreds of heterogenous datasets coming from distributed data sources and spanning multiple domains. Show less

We present a semantic infrastructure to augment existing
enterprise applications with context coming from external systems. We
showcase our infrastructure using a use-case around Care Coordination,
based on a set of IBM solutions. An up-to-date version of this document,
with additional media, can be found at http://ibm.co/14nUY3i.

Data derived from physical sensing is currenly the most common
form of smart city data. We term the process of deriving usable metrics (via.
HTML Parsing and analysis) from social media sources (e.g. blogs, wikis,
Twitter, Forums etc.) as 'online sensing'. We will argue, through a number of
use cases, that online sensors can be used to determine some of the deficiencies
in physical sensors and sensor networks, e.g., coverage gaps, judging
performance and are useful for… Show more

Data derived from physical sensing is currenly the most common
form of smart city data. We term the process of deriving usable metrics (via.
HTML Parsing and analysis) from social media sources (e.g. blogs, wikis,
Twitter, Forums etc.) as 'online sensing'. We will argue, through a number of
use cases, that online sensors can be used to determine some of the deficiencies
in physical sensors and sensor networks, e.g., coverage gaps, judging
performance and are useful for event tracking scenarios. These use cases
demonstrate the need for, and utility in, combining the outputs from online
sensors and human sources with that of established physical sensors. The
application of collaborative tagging to semantically tag smart city data via the
social media will also be useful in improving event tracking. Show less

QuerioCity aims at creating an ecosystem for managing and accessing the information of a city, with a particular focus on transforming, integrating and querying heterogenous semistructured data in an open environment. We will perform a demonstration of a prototype version of the QuerioCity platform, as used in the upcoming update of the dublinked.ie website.

SPUD, a semantic environment for cataloguing,
exploring, integrating, understanding, processing and transforming ur-
ban information. Such information comes from the Web, government
authorities, social networks and Linked Data sources. We identify a sce-
nario consisting of the following cases: (a) Publication of a dataset, fo-
cusing on privacy protection and semantic annotation of the dataset,
(b) Reporting and Consolidation of multi-faceted information, focusing
on… Show more

SPUD, a semantic environment for cataloguing,
exploring, integrating, understanding, processing and transforming ur-
ban information. Such information comes from the Web, government
authorities, social networks and Linked Data sources. We identify a sce-
nario consisting of the following cases: (a) Publication of a dataset, fo-
cusing on privacy protection and semantic annotation of the dataset,
(b) Reporting and Consolidation of multi-faceted information, focusing
on searching and visualizing heterogenous data from several sources, in-
cluding social media, linked data and government data, (c) Diagnosis,
based on highly expressive reasoning. Through our demonstration, we
show that semantic technologies can be used to obtain business results
in an environment with hundreds of heterogenous real datasets coming
from dierent data sources and spanning multiple domains. Show less