This paper discusses the knowledge integration of clinical information extracted from distributed medical ontology in order to ameliorate a machine learning-based multi-label coding assignment system. The proposed approach is implemented using a decision tree based cascade hierarchical technique on the university hospital data for patients with Coronary Heart Disease (CHD). The preliminary results obtained show a satisfactory finding.

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.

Amongst the best practices that constitute linked data, one of the foremost is to use only HTTP-URIs as identifiers for RDF resources. This is so that the URI will resolve in a Linked Data browser to give information about the named resource. At the same time, Linked Data takes a resource-centric, as opposed to page-centric, approach to resolution. We argue that this approach can, in certain cases, obviate the need for insisting on HTTP-URIs. As a use of our “expanded” notion of Linked Data, we present as an example Life Science Identifiers.

GORT is a system that combines Linked Data from across several Semantic Web data sources to solve guesstimation problems, with user assistance. The system uses customised inference rules over the relationships in the OpenCyc ontology, combined with data from DBPedia, to reason and perform its calculations. The system is extensible with new Linked Data, as it becomes available, and is capable of answering a small range of guesstimation questions.

Over the last several years, the market for speech technology has seen significant developments (Pieraccini and Huerta We learned some lessons which we use as guidelines 2005) and powerful commercial off-the-shelf solutions for in the development of multimodal dialogue systems where speech recognition (ASR) or speech synthesis (TTS). Further users can combine speech and gestures when using multiple application scenarios, more diverse and dynamic information interaction devices. In earlier projects (Wahlster 2003; Reithinger sources, and more complex prototype systems need et al. 2005) we integrated different sub-components to be addressed in the context of QA. Dialogue-based QA allows to multimodal interaction systems. Other lessons served as a user to pose questions in natural speech, followed by guidelines in the development of semantic dialogue systems answers presented in a concise form (Sonntag et al. 2007).

Several solutions exist for semantically describing Web Services (WSs) from the perspective of orchestration but little is known about how semantics benefit WS choreography. The most extreme example of a choreography problem occurs in peer-to-peer systems where shared semantics of data may need to be established via services interactions. We present a solution to this problem by sharing micro-data via interaction models. No pre-unified ontology is required in our approach so peers can make use of existing heterogeneous resources having been described in the RDF data model flexibly and compatibly. The experimental results indicate that our approach semantically enhances WS choreography in a lightweight way which complies with principles of Linked Data and republished Interaction Models (IMs) can further facilitate the progress of the Web of data as well as the formation of peer communities generated through peers' interactions.

In this position paper, we argue that the Linked Open Data (LoD) Cloud, in its current form, is only of limited value for furthering the Semantic Web vision. Being merely a weakly linked triple collection, it will only be of very limited benefit for the AI or Semantic Web communities. We describe the corresponding problems with the LoD Cloud and give directions for research to remedy the situation.

Most social networks have implemented extensive and complex controls in order to battle the host of privacy concerns that initially plagued their online communities. These controls have taken the form of a-priori access control, which allow users to construct barriers preventing unwanted users from viewing their personal information. However, in cases in which the access restriction mechanisms are bypassed or when the access restrictions are met but the data is later misused, this system leaves users unprotected. Our framework, Respect My Privacy, proposes an alternative approach to the protection of privacy. Our strategy is similar to how legal and social rules work in our societies where the vast majority of these rules are not enforced perfectly or automatically, yet most of us follow the majority of the rules because social systems built up over thousands of years encourage us to do so and often make compliance easier than violation. Our project aims to support similar functionality in social networks. Instead of focusing on enforcing privacy policies through restricted access, we focus on helping users conform to existing policies by making them aware of the usage restrictions associated with the data. The framework has two main functions - generating privacy or usage control policies for social networks, and visualizing these policies while exploring social networks. We have implemented this functionality across three platforms: Facebook, OpenSocial and Tabulator, a Semantic Web browser. These applications enable users to specify privacy preferences for their data and then display this privacy-annotated data prominently enabling other users to easily recognize and conform to these preferences.

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.

Research effort, with its emphasis on evaluation and measurable progress, things began to change. Instead SHRDLU (WIN72) is perhaps the canonical example. of systems whose architecture and vocabulary were The rapid growth of efforts to found the next generation of based on linguistic theory (in this case acoustic phonetics), systems on general-purpose knowledge representation languages new approaches based on statistical modelling and Bayesian (I'm thinking of several varieties of semantic nets, probability emerged and quickly spread. "Every time I fire a from plain to partitioned, as well as KRL, KL-ONE and linguist my system's performance improves" (Fred Jellinek, their successors, ending (not yet, of course) with CYC (See head of speech recognition at IBM, c. 1980, latterly repudiated (BRA08) for all these) stumbled to a halt once their failure by Fred but widely attested). As advanced from resolution theorem provers through a number more and more problems are re-conceived as instances of of stages to the current proliferation of a range of Description the noisy channel model, the empiricist paradigm continually Logic'reasoners'; Whereas in the 1970s and 1980s there grew, so did the need to manage the impact of change and was real energy and optimism at the interface between computational conflict: enter'truth maintenance', subsequently renamed and theoretical linguistics, the overwhelming success'reason maintenance'. While still using some of But outflanking these'normal science' advances of AI, the terminology of linguistic theory, computational linguistics the paradigm shifters were coming up fast on the outside: practioners are increasingly detached from theory itself, over the last ten years machine learning has spread from which has suffered a, perhaps connected, loss of energy and small specialist niches such as speech recognition to become sense of progress.