Telephone-based information access has the potential to deliver a significant positive impact in the developing world. We discuss some of the most important issues that must be addressed in order to realize this potential, including matters related to resource development, automatic speech recognition, text-to-speech systems, and user-interface design. Although our main focus has been on the eleven official languages of South Africa, we believe that many of these same issues will be relevant for the application of speech technology throughout the developing world.

This paper examines first ideas on the applicability of Linked Data, in particular a subset of the Linked Open Drug Data (LODD), to connect radiology, human anatomy, and drug information for improved medical image annotation and subsequent search. One outcome of our ontology engineering methodology is the alignment between radiology-related OWL ontologies (FMA and RadLex). These can be used to provide new connections in the medicine-related linked data cloud. A use case scenario is provided that demonstrates the benefits of the approach by enabling the radiologist to query and explore related data, e.g., medical images and drugs. The diagnosis is on a special type of cancer (lymphoma).

While recommender systems can greatly enhance the user experience, the entry barriers in terms of data acquisition are very high, making it hard for new service providers to compete with existing recommendation services. This paper proposes to build open recommender systems which can utilise Linked Data to mitigate the new-user, new-item and sparsity problems of collaborative recommender systems. We describe how to aggregate data about object centred sociality from different sources and how to process it for collaborative recommendation. To demonstrate the validity of our approach, we augment the data from a closed collaborative music recommender system with Linked Data, and significantly improve its precision and recall.

Many of the benefits of structured data come about when users can re-use existing vocabularies rather than create new ones, but it is currently difficult for users to find, create, and host new vocabularies. Moreover, the value of any given vocabulary as a foundation for applications depends on the perceived certainty that the vocabulary — both its machine-readable schemas and human-readable specification documents — will remain reliably accessible over time and that its URIs will not be sold, re-purposed, or simply forgotten. This note proposes two approaches for solving these problems: one for multiple Vocabulary Hosting Services and a Vocabulary Preservation System to keep them linked together.

The Web of Data (WoD) is growing at an amazing rate and it will no longer be feasible to deal with it in a global way, by centralising the data or reasoning processes making use of that data. We believe that Computational Intelligence techniques provides the adaptiveness, robustness and scalability that will be required to exploit the full value of ever growing amounts of dynamic Semantic Web data.

We introduce a search engine and information retrieval system for providing access to linked opinion data. Natural language technology of generalization of syntactic parse trees is introduced as a similarity measure between subjects of textual opinions to link them on the fly. Information extraction algorithm for automatic summarization of web pages in the format of Google sponsored links is presented. We outline the usability of the implemented system, integrated opinion delivery environment (IODE).

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.

Data.gov is a website that provides US Government data to the general public to ensure better accountability and transparency. Our recent work on the Data-gov Wiki, which attempts to integrate the datasets published at Data.gov into the Linking Open Data (LOD) cloud (yielding "linked government data"), has produced 5 billion triples – covering a range of topics including: government spending, environmental records, and statistics on the cost and usage of public services. In this paper, we investigate the role of Semantic Web technologies in converting, enhancing and using linked government data. In particular, we show how government data can be (i) inter-linked by sharing the same terms and URIs, (ii) linked to existing data sources ranging from the LOD cloud (e.g. DBpedia) to the conventional web (e.g. the New York Times), and (iii) cross-linked by their knowledge provenance (which captures, among other things, derivation and revision histories).

When searching a knowledge repository such as Wikipedia or the Internet, the user doesn’t always know what they are looking for. Indeed, it is often the case that a user wishes to find information about a concept that was completely unknown to them prior to the search. In this paper we describe C-Link, which provides the user with a method for searching for unknown concepts which lie between two known concepts. C-Link does this by modeling the knowledge repository as a weighted, directed graph where nodes are concepts and arc weights give the degree of “relatedness” between concepts. An experimental study was undertaken with 59 participants to investigate the performance of C-Link compared to standard search approaches. Statistical analysis of the results shows great potential for C-Link as a search tool.

Introducing robots as teammates in medical, space, and military domains raises interesting and challenging human factors issues that do not necessarily arise in multi-robot coordination. For example, we must consider how to design robots that integrate seamlessly with human group dynamics. An essential quality of a good human partner is her ability to robustly anticipate and adapt to other team members and the environment. Robots should preserve this ability and avoid constraining their human partners’ flexibility to act. This requires that the robot partner be capable of reasoning quickly online, and adapting to the humans’ actions in a temporally fluid way. This paper describes recent advances in dynamic plan execution, and argues that these advances provide a potentially powerful framework for explicitly modeling and efficiently reasoning on temporal information for human-robot interaction. We describe an executive named Chaski that enables a robot to coordinate with a human to execute a shared plan under different models of teamwork. We have applied Chaski to demonstrate teamwork using two Barrett Whole Arm Manipulators, and describe our ongoing work to demonstrate temporally fluid human-robot teaming using the Mobile-Dexterous-Social (MDS) robot.