A frequent topic discussed in the Linked Data community, especially when trying to outreach its values, is "What can we do with all this data ?". In this paper, we demonstrate (1) how to measure semantic distance on Linked Data in order to identify relatedness between resources, and (2) how such measures can be used to provide a new kind of self-explanatory recommendations, bringing together Linked Data and Artificial Intelligence principles, and demonstrating how intelligent agents could emerge in the realm of Linked Data.

This paper introduces the IRIS mobile robot project. IRIS is a largely student designed and implemented mobile robot platform created to provide a mechanism for classroom explorations of topics in artificial intelligence, cognitive science, and robotics. It has been designed to be used by students from middle school through college.

In this paper, we propose a model of the web of data as a graph of interlinked graphs which goes beyond the standard single-graph RDF semantics, describe two different ways in which a query on this structure can be answered, and characterize semantically each of these ways in terms of restrictions on the relation between the domain of interpretation of each single component graph.

Semantic technologies are available, and gain popularity on the Web as well as on the desktop, but both (desktop and Web) act as large data silos, making personal and online data difficult to interlink. We propose a system that enables easy publishing of personal notes as linked social media content, while at the same time semantically enriching the desktop resources with information retrieved from the Linked Data cloud. The transformation, publication and linking process is integrated with the familiar desktop applications and online blogging platforms, providing a better usability experience.

Territorial privacy is an old concept for privacy of the personal space dating back to the 19th century. Despite its former relevance, territorial privacy has been neglected in recent years, while privacy research and legislation mainly focused on the issue of information privacy. However, with the prospect of smart and ubiquitous environments, territorial privacy deserves new attention. Walls, as boundaries between personal and public spaces, will be insufficient to guard territorial privacy when our environments are permeated with numerous computing and sensing devices, that gather and share real-time information about us. Territorial privacy boundaries spanning both the physical and virtual world are required for the demarcation of personal spaces in smart environments. In this paper, we analyze and discuss the issue of territorial privacy in smart environments. We further propose a real-time user-centric observation model to describe multimodal observation channels of multiple physical and virtual observers. The model facilitates the definition of a territorial privacy boundary by separating desired from undesired observers, regardless of whether they are physically present in the user’s private territory or virtually participating in it. Moreover, we outline future research challenges and identify areas of work that require attention in the context of territorial privacy in smart environments.

Some of the top legal ontologies developed so far include the Functional Ontology for Law [FOLaw] The increasing need for legal information and content (Valente 1995), the Frame-Based Ontology (van Kralingen management caused by the growing amount of 1995), the LRI-Core ontology (Breuker 2004), unstructured (or poorly structured) legal data managed by DOLCE CLO [Core Legal Ontology] (Gangemi et al. legal publishing companies, law firms and public 2003), or the Ontology of Fundamental Concepts (Rubino administrations, or the increasing amount of legal et al. 2006, Sartor 2006) the basis for the LKIF-Core information directly available on the World Wide Web, Ontology (Breuker et al. 2007). Nevertheless, most legal have created an urgent need to construct conceptual ontologies are domain specific ontologies, which represent structures for knowledge representation to share and particular legal domains towards search, indexing and manage intelligently all this information, whilst making reasoning in a specific domain of national or European law human-machine communication and understanding (e.g. the IPRONTO ontology by Delgado et al. 2003, the possible.

We propose a methodology for automatic synthesis of embedded control software that accounts for exogenous disturbances. The resulting system is guaranteed, by construction, to satisfy a given specification expressed in linear temporal logic. The embedded control software consists of three components: a goal generator, a trajectory planner, and a continuous controller. We demonstrate the effectiveness of the proposed technique through an example of an autonomous vehicle navigating an urban environment. This example also illustrates that the system is not only robust with respect to exogenous disturbances but also capable of handling violation of the environment assumptions.

We summarise the experimental design issues related to timing in several human-robot interaction scenarios investigating turn-taking or synchronization between child-sized humanoid robots and human participants. Our aim is not to have the humanoid robots just replicate the human’s behaviours (e.g. waving, peek-a-boo, or drumming), but to engage in interactions in a socially appropriate manner. From these various studies, we have identified several ways in which time has an impact on interaction. We have also identified practical concerns about data collection for time-dependent interactions and ways to address them. The conclusions drawn from this work is likely to be useful in informing the design of systems which engage in synchronized or turn-taking interactions with people.

A key challenge for policymakers in many developing countries is to decide which intervention or collection of interventions works best to improve learning outcomes in their schools. Our aim is to develop a causal model that explains student learning outcomes in terms of observable characteristics as well as conditions and processes difficult to observe directly. We start with a theoretical model based on the results of previous research, direct experience and experts’ knowledge in the field. This model is then refined through application of supervised learning methods to available data sets. Once calibrated with local data in a country, the model estimates the probability that a given intervention would affect learning outcomes.

Development is fundamentally about understanding people, their motivations, behaviors and reactions. We have two primary means of understanding people — observing what they do, and what they say. As the AI4D community has noted, people's increased use of mobile devices has led to a wealth of new data relevant to these topics. We are on the cusp of developing incredibly powerful tools that can help us understand how human beings migrate, transact and acquire wealth. This could have a large impact on how we determine policies and allocate resources. Most of this analysis has tended to focus on what people do — where they go, who they talk to, what they buy, etc. I argue that what people say is an equally rich source of development data, often containing information that cannot be obtained from people's actions, such as their needs, hopes and aspirations. Voice is the most natural form of communication, especially for people who speak a non-mainstream language, and/or have marginal literacy skills.  These are often exactly those populations who are most disenfranchised, and therefore most need their voices to be heard.