While computers assist humans with tasks such as navigation that involve spatial aspects, agents that can interact in a meaningful way in this context are still in their infancy. One core issue is the mismatch in the representation of spatial information a computer-based system is likely to use, and the one a human is likely to use. Computers are better suited for quantitative schemes such as maps or diagrams that rely on measurable distances between entities. Humans frequently use higher-level, domain-specific conceptual representations such as buildings, rooms, or streets for orientation purposes. Combined with the person-centric world view that we often assume when we refer to spatial information, it is challenging for agents to convert statements using spatial references into assertions that match their own internal representation. In this paper, we discuss an approach that uses natural language processing and information extraction tool kits to identify entities and statements about their spatial relations. These extractions are then processed by a spatial reasoner to convert them from the human conceptual space into the quantitative space used by the computer-based agent.

Most of the developing countries are re-inventing the wheel in their efforts to launch egovernment initiatives — especially in the areas of healthcare, education, economic development, supply chains for food distribution, and emergency services. A Computer Aided Strategic Planner, part of the UN eNabler Toolset, has been developed to quickly and effectively produce detailed strategic plans for a wide range of egovernment services based on best practices and standards. The generated plan is highly customized for the type of service as well as the country/region by using the latest thinking in AI, ontologies, and patterns. The Planner, available through the UN-GAID initiative, can be and has been used very effectively to educate as well as assist the government officials of developing countries to accelerate progress in crucial areas.

The increasing agility of business requires an accelerated adaptation of organizations to continuously changing conditions. Individual and organizational learning are prominent means to achieve this. Hereby learning is always accompanied by the development of knowledge artifacts. For the entire of learning and artifact development the term knowledge maturing has been introduced recently, which focuses on these three manifestations of knowledge: cognifacts, sociofacts, and artifacts. In this paper we will focus on sociofacts as the subject-bound knowledge manifestation of social actions. Sociofacts are rooted in respective cognifacts play an independent role due to their binding to collective actions and subjects. These are particularly difficult to grasp but play a decisive role for the performance of organizations and the collaboration in there.The presented paper approaches the notion of sociofacts, discusses them on a theoretical level and establishes a first formal notation for sociofacts. We use the case of a merger between two companies to describe the advantages of sociofact analysis for such process. Some sociofact related problems during a merger are described and possible solutions are presented. We identify technical approaches for seizing sociofacts from tool-mediated social interaction and discuss open question for future research.

Vast volumes of information from public Web portals are readily accessible from virtually any computer in the world. This can be seen as an enormous repository of information which brings significant business value for companies working in e-commerce activities. However, the main problems encountered when using this information are: (I) the information is published in various, non-machine-processable formats, (II) a lack of services that match and store information from various sources in a homogenous structure, and (III) the accessible datasets are rarely provided with e-commerce concepts in mind. These problems make them difficult to use by e-commerce applications. The main goal of this paper is to propose a methodology and analysis of components required for combining and integrating information into machine-processable dataset from different Web data sources, based on suitable e-commerce ontology. In order to demonstrate proposed methodology, the process of wrapping and matching the data from two public datasets will be discussed as an example.

Inferring the sentiment of social media content, for instance blog postings or online product reviews, is both of great interest to businesses and technically challenging to accomplish. This paper presents two computational methods for estimating social media sentiment which address the challenges associated with Web-based analysis. Each method formulates the task as one of text classification, models the data as a bipartite graph of documents and words, and assumes that only limited prior information is available regarding the sentiment orientation of any of the documents or words of interest. The first algorithm is a semi-supervised sentiment classifier which combines knowledge of the sentiment labels for a few documents and words with information present in unlabeled data, which is abundant online. The second algorithm assumes existence of a set of labeled documents in a domain related to the domain of interest, and leverages these data to estimate sentiment in the target domain. We demonstrate the utility of the proposed methods by showing they outperform several standard methods for the task of inferring the sentiment of online reviews of movies, electronics products, and kitchen appliances. Additionally, we illustrate the potential of the methods for multilingual business informatics through a case study involving estimation of Indonesian public opinion regarding the July 2009 Jakarta hotel bombings.

In this position paper we investigate the opportunities of using functionalities provided by social network sites for the collaborative formalization of semantics in the domain of health. In particular we identified benefits in regard to communication support, economic benefits, and technical opportunities. The implementation of the functionalities are illustrated by describing a use case from an ongoing project with the World Health Organization.

In this paper, we present a novel approach of translating natural languages specification to SBVR business rules. The business rules constraint business structure or control behaviour of a business process. In modern business modelling, one of the important phases is writing business rules. Typically, a business rule analyst has to manually write hundreds of business rules in a natural language (NL) and then manually translate NL specification of all the rules in a particular rule language such as SBVR, or OCL, as required. However, the manual translation of NL rule specification to formal representation as SBVR rule is not only difficult, complex and time consuming but also can result in erroneous business rules. In this paper, we propose an automated approach that automatically translates the NL (such as English) specification of business rules to SBVR (Semantic Business Vocabulary and Rules) rules. The major challenge in NL to SBVR translation was complex semantic analysis of English language. We have used a rule based algorithm for robust semantic analysis of English and generate SBVR rules. Automated generation of SBVR based Business rules can help in improved and efficient constrained business aspects in a typical business modelling.

Sustainable design is as an important movement in design. Biologically inspired design is a major paradigm for sustainable design. In this paper, we analyze a corpus of biologically inspired design projects in terms of sustainability. We then describe a case study of analogical design of a fog harvesting net, and abstract from it the patterns of Hydrophobia and Hydrophilia. We indicate how these two function-mechanism design patterns occur in several design projects in our corpus. This analysis indicates how biologically inspired sustainable design can be analyzed in terms of cross-domain analogical transfer of design patterns.

Figure 1a first illustrates by the oval that a Cradle-to-cradle (C2C) design (McDonough & Braungart, critical problem in traditional design is that a product is designed 2002) recognizes that nothing short of full recycling of materials in isolation. In contrast, the products shown in the with no degradation in material quality is necessary square box of Figure 1b illustrate the concept of a product for long-term planet sustainability. C2C advocates looking family, where multiple products are designed within a system to the natural world as an ideal model of recycling, where of material use and reuse, which flows between product organic materials are continually recycled through processes lines. While there may still be materials that come from of decay and growth. They propose design methodology outside the family and there are materials that are byproducts that separates biological cycles and syntheticmaterial of the family production, a family design would seek cycles, enabling biological material to be reclaimed to minimize these and to exploit them in a still larger context.

In this contribution, it is suggested that argumentation theories may offer the tools to do so. More specifically, the pragmadialectical theory of argumentation (van Eemeren and Grootendorst 1992; 2004) is proposed as a solid instrument for analyzing and evaluating argumentation in consultation, as it not only provides a set of reasonableness criteria for argumentative conduct but also can account for arguers' need to effectively tailor argumentative messages to their recipients. The instrumental value of pragma-dialectics in the field of automated argument selection will be elucidated by means of a case study concerning antibiotics. In doing so, this contribution is closely connected to the paper by Rubinelli, Wierda, Labrie, and O'Keefe (AAAI Spring Symposium 2011) and provides an exploratory investigation of the advantages of a pragma-dialectical approach to the conceptual design of automated health communication systems and autonomous health promotion.