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.

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.

Time is a concept that highly depends on the socio-cultural context. Its perception by humans is primarily based on the cultures, nations and social environment they belong to. Hence, different socio-cultural contexts imply different understandings of time. This leads to communication problems when their members start interacting with each other. In a dynamic and multi-cultural environment like today’s Web, where both billions of people with different socio-cultural contexts and numerous context dependent software applications interact, similar communication and inter-operability problems are expected. Expressing socio-cultural temporal information in an unambiguous, explicit and machine processable way can, however, help reduce such communication conflicts. In this way, heterogeneous temporal Web application systems can share the same concept of time. In this paper we present an ontology of socio-cultural time expressions that attempts to formalize the notion of socio-cultural time. The resulting model can then be used in a Web based temporal applications such as automated appointment scheduling services or calendars to provide more context sensitive service to its users.

Time perception and inferences there from are of critical importance to many autonomous agents. But time is not perceived directly by any sensory organ. We argue that time is constructed by cognitive processes. Here we present a model for time perception that concentrates on succession and duration, and that generates these concepts and others, such as continuity, immediate present duration, and lengths of time. These concepts are grounded through the perceptual process itself. The LIDA cognitive model is used to illustrate these ideas.

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.

Navigation and localization are still one of the most fundamental tasks to be accomplished by mobile autonomous robots. One of the main purposes of the navigation and localization process is to build a precise, usually allocentric spatial static representation (e.g. [S. Thrun and Schulz, 2000] ). Although robots are able to carry more and more powerful sensors, the question is, which informations are needed for localization and navigation. One way to do these tasks with only a minimal amount of resources is via landmarks. Furthermore it is an easy and failsafe way to do so. Localization can be done with only a single 180 degree camera, and a navigation by the change of the landmark ordering is very robust against misinterpretations and errors. This technique uses the fact that, seen from the agent, landmarks are switching locations only in a certain way( [Wagner, Visser, and Herzog, 2004] ). With an additional timer the robustness of this technique can be further increased. But with timing and the use of angles between the landmarks, it is also possible to measure the distances between the landmarks and the agent. Furthermore this technique can be extended to detect moving objects and to compute the speed and direction of them.

This work describes an approach to time-series modeling of social interactions between human and robot, which is motivated by the social psychology concept of social grounding. In this model, the goal of the agents is to establish and use patterns of communication, rather than rely on existing patterns. Our goal is to allow an artifical agent to construct a pattern of shared meaning with a human or other agent through shared experience rather than relying a model provided A priori. We describe a preliminary human robot interaction study which illustrates the proposed approach.