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McCrae J.P.,Insight Centre for Data Analytics
Communications in Computer and Information Science | Year: 2016

Lexicons form a crucial part of how we build natural language systems that allow humans to interact with machines and to build web applications that can use web standards such as OWL but express them in natural language, we developed a vocabulary called lemon (Lexicon Model for Ontologies). This tutorial details the model and enables participants to apply it in line with common patterns of usage. © Springer International Publishing Switzerland 2016.


Wilson N.,Insight Centre for Data Analytics
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI | Year: 2016

Maintaining comfortable thermal conditions in an office environment is very important, as it can affect the quality of life of the occupants, their work productivity, and improve energy efficiency. One significant aspect of this task is how to balance the preferences of a number of occupants sharing the same space. We suggest three families of approaches to this problem, both for the case of optimising for a single time period, and for the problem of optimising over multiple different time periods. We analyse in detail the different approaches based on a number of natural properties, proving which of the properties the different families satisfy. © 2015 IEEE.


Mileo A.,Insight Centre for Data Analytics
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

A fast growing torrent of data is being created by companies, social networks, mobile phones, smart homes, public transport vehicles, healthcare devices, and other modern infrastructures. Being able to unlock the potential hidden in this torrent of data would open unprecedented opportunities to improve our daily lives that were not possible before. Advances in the Internet of Things (IoT), Semantic Web and Linked Data research and standardization have already established formats and technologies for representing, sharing and re-using (dynamic) knowledge on the Web. However, transforming data into actionable knowledge requires to cater for (i) automatic mechanisms to discover and integrate heterogeneous data streams on the fly and extract patterns for applications to use, (ii) concepts and algorithms for context and quality-aware integration of semantic data streams, and (iii) the ability to synthesize domain-driven commonsense knowledge (and answers derived from it) with expressive inference that can capture decision analytics in a scalable way. In the first part of this lecture we will characterize the main approaches to stream processing for the Web of Data, showing how data quality and context can guide semantic integration. In the second part of this lecture we will focus on rule-based Web Stream Reasoning and illustrate how scalability and uncertainty issues can be addressed in a rule-based approach. We will discuss new challenges and opportunities in Web Stream Reasoning, briefly considering economical and societal impact in real application scenarios in a smart city context, and we will conclude by providing a brief overview of ongoing research and standardization activities in this area. © Springer International Publishing Switzerland 2015.


Campinas S.,Insight Centre for Data Analytics
CEUR Workshop Proceedings | Year: 2014

The amount of Linked Data has been growing increasingly. However, the efficient use of that knowledge is hindered by the lack of information about the data structure. This is reected by the difficulty of writing SPARQL queries. In order to improve the user experience, we propose an auto-completion library1 for SPARQL that suggests possible RDF terms. In this work, we investigate the feasibility of providing recommendations by only querying the SPARQL endpoint directly.


Gurrin C.,Insight Centre for Data Analytics | Smeaton A.F.,Insight Centre for Data Analytics | Doherty A.R.,University of Oxford
Foundations and Trends in Information Retrieval | Year: 2014

We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging in order to capture life details of life activities, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses to an information retrieval scientist. This review is a suitable reference for those seeking an information retrieval scientist's perspective on lifelogging and the quantified self. © 2014 C. Gurrin, A. F. Smeaton, and A. R. Doherty.

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