Pol Mac Aonghusa

QuerioCity: a linked data platform for urban information management

In this paper, we present QuerioCity, a platform to catalog, index and query highly heterogenous information coming from complex systems, such as cities. A series of challenges are identified: namely, the heterogeneity of the domain and the lack 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), and the sensitivity of the information. We propose an approach for incremental and continuous integration of static and streaming data, based on Semantic Web technologies. The proposed system is unique in the literature in terms of handling of multiple integrations of available data sets in combination with flexible provenance tracking, privacy protection and continuous integration of streams. We report on lessons learnt from building the first prototype for Dublin.

The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation

BackgroundBehaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support.The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a ‘Knowledge System’ that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question ‘What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?’.MethodsThe HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility.DiscussionThe HBCP aims to revolutionise our ability to synthesise, interpret and deliver evidence on behaviour change interventions that is up-to-date and tailored to user need and context. This will enhance the usefulness, and support the implementation of, that evidence.

SPUD—Semantic Processing of Urban Data

Abstract We present SPUD , a semantic environment for cataloging, exploring, integrating, understanding, processing and transforming urban information. A series of challenges are identified: 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 the incremental and continuous integration of static and streaming data, based on Semantic Web 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 heterogeneous datasets coming from distributed data sources and spanning multiple domains.

Open Innovation Portal: A collaborative platform for open city data sharing

In recent years, many agencies and government authorities have been moving toward opening up their datasets, allowing external parties to create applications that can mash up this data. As the amount and the variety of data is increasing, it is important to create good metadata (descriptions, geographical boundaries, limitations, etc.) in order to allow individuals, who may not be domain experts, to easily search and consume data. In this paper we propose the Open Innovation Portal (OIP), a collaborative platform that allows Cities to annotate, publish and provide access to urban data from multiple sources in an intuitive, consistent and scalable way through open standards. In collaboration with Dublin City authorities and National University of Ireland Maynooth, we implemented a first prototype for Dublin. In the demo, we show how the collaborative metadata creation process works, from the raw data to the publishable information, and how the collaborative platform can be implemented both for a mobile-phone and web application.

Improving cross-domain information sharing in care coordination using semantic web technologies

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.

Don’t Let Google Know I’m Lonely

From buying books to finding the perfect partner, we share our most intimate wants and needs with our favourite online systems. But how far should we accept promises of privacy in the face of personalized profiling? In particular, we ask how we can improve detection of sensitive topic profiling by online systems. We propose a definition of privacy disclosure that we call ε-indistinguishability, from which we construct scalable, practical tools to assess the learning potential from personalized content. We demonstrate our results using openly available resources, detecting a learning rate in excess of 98% for a range of sensitive topics during our experiments.

Plausible Deniability in Web Search—From Detection to Assessment

Web personalization uses what systems know about us to create content targeted at our interests. When unwanted personalization suggests we are interested in sensitive or embarrassing topics, a natural reaction is to deny interest. This is a practical response only if denial of our interest is credible or plausible. Adopting a definition of plausible deniability in the usual sense of “on the balance of probabilities,” we develop a practical and scalable tool called PDE, allowing a user to decide when their ability to plausibly deny interest in sensitive topics is compromised. We show that threats to plausible deniability are readily detectable for all the topics tested in an extensive testing program. Of particular concern is observation of threats to deniability of interest in topics related to health and sexual preferences. We show that this remains the case when attempting to disrupt search engine learning through noise query injection and click obfuscation. We design a defense technique exploiting uninteresting, proxy topics and show that it provides a more effective defense of plausible deniability in our experiments.

On-Demand Integration and Linking of Open Data Information

This paper introduces an extension of DALI, a framework for data integration and visualisation. When integrating new data, DALI automatically tries to recognise the schema and contents of the file, semantically lift them, and annotate them with existing ontologies. The extension presented in this paper allows users to import data from external data portals, namely portals using CKAN or Socrata, based on the results of a search query or by selecting individual datasets. Furthermore, we perform a semantic expansion of the search terms provided by the user in order to identify datasets that might still be relevant while not containing the exact search terms.

A linked data approach to care coordination

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.