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.

We are developing an ontology of microsocial concepts for use in an instructional system for teaching cross-cultural communication. We report here on that part of the ontology relating to interpersonal relationships. We first explicate the key concepts of commitment, shared plans, and good will. Then in terms of these we present a formal account of the host-guest relationship.

Temporal encoding schemes using RDF and OWL are often plagued by a massive proliferation of useless "container" objects. Reasoning and querying with such representations is extremely complex, expensive, and error-prone. We present a temporal extension of the Hayes and ter Horst entailment rules for RDFS/OWL. The extension is realized by extending RDF triples with further temporal arguments and requires only some lightweight forms of reasoning. The approach has been implemented in the forward chaining engine HFC.

Granularity is the concept of breaking down an event into smaller parts or granules such that each individual granule plays a part in the higher level event. Humans can seamlessly shift their granularity perspectives while reading or understanding a text. To emulate such a mechanism, we describe a theory for inferring this information automatically from raw input text descriptions and some background knowledge to learn the global behavior of event descriptions from local behavior of components. We also elaborate on the importance of discovering granularity structures for solving NLP problems such as — automated question answering and text summarization.

We present an ontology consisting of a theory of spatial dimension and a theory of dimension-independent mereological and topological relations in space. Though both are fairly weak axiomatizations, their interplay suffices to define various mereotopological relations and to make any necessary dimension constraints explicit. We show that models of the INCH Calculus and the Region-Connection Calculus (RCC) can be obtained from extensions of the proposed ontology.

Sustainability requires emphasizing the importance of environmental causes and effects among design knowledge from heterogeneous stakeholders to make a sustainable decision. Recently, such causes and effects have been well developed in ontological representation, which has been challenged to generate and integrate multiple domain knowledge due to its domain specific characteristics. Moreover, it is too challengeable to represent heterogeneous, domain-specific design knowledge in a standardized way. Causal knowledge can meet the necessity of knowledge integration in domains. Therefore, this paper aims to develop a causal knowledge integration system with the authors’ previous mathematical causal knowledge representation.

The context of a software developer is something hard to define and capture, as it represents a complex network of elements across different dimensions that are not limited to the work developed on an IDE. We propose the definition of a software developer context model that takes into account all the dimensions that characterize the work environment of the developer. We are especially focused on what the software developer context encompasses at the project level and how it can be captured. The experimental work done so far show that useful context information can be extracted from project management tools. The extraction, analysis and availability of this context information can be used to enrich the work environment of the developer with additional knowledge to support her/his work.

The ever-increasing amount of data in biomedical research, and in cancer research in particular, needs to be managed to support efficient data access, exchange and integration. Existing software infrastructures, such caGrid, support access to distributed information annotated with a domain ontology. However, caGrid's current querying functionality depends on the structure of individual data resources without exploiting the semantic annotations. In this paper, we present the design and development of an ontology-based querying functionality that consists of: the generation of OWL2 ontologies from the underlying data resources metadata and a query rewriting and translation process based on reasoning, which converts a query at the domain ontology level into queries at the software infrastructure level. We present a detailed analysis of our approach as well as an extensive performance evaluation. While the implementation and evaluation was performed for the caGrid infrastructure, the approach could be applicable to other model and metadata-driven environments for data sharing.

Knowledge is attributed to human whose problem-solving behavior is subjective and complex. In today's knowledge economy, the need to manage knowledge produced by a community of actors cannot be overemphasized. This is due to the fact that actors possess some level of tacit knowledge which is generally difficult to articulate. Problem-solving requires searching and sharing of knowledge among a group of actors in a particular context. Knowledge expressed within the context of a problem resolution must be capitalized for future reuse. In this paper, an approach that permits dynamic capitalization of relevant and reliable actors' knowledge in solving decision problem following Economic Intelligence process is proposed. Knowledge annotation method and temporal attributes are used for handling the complexity in the communication among actors and in contextualizing expressed knowledge. A prototype is built to demonstrate the functionalities of a collaborative Knowledge Management system based on this approach. It is tested with sample cases and the result showed that dynamic capitalization leads to knowledge validation hence increasing reliability of captured knowledge for reuse. The system can be adapted to various domains

The Resource Description Framework (RDF) provides a common data model for the integration of "real-time" social and sensor data streams with the Web and with each other. While there exist numerous protocols and data formats for exchanging dynamic RDF data, or RDF updates, these options should be examined carefully in order to enable a Semantic Web equivalent of the high-throughput, low-latency streams of typical Web 2.0, multimedia, and gaming applications. This paper contains a brief survey of RDF update formats and a high-level discussion of both TCP and UDP-based transport protocols for updates. Its main contribution is the experimental evaluation of a UDP-based architecture which serves as a real-world example of a high-performance RDF streaming application in an Internet-scale distributed environment.