Service-Oriented Computing (SOC) has received much interest due to its potential to tackle many adaptive system architecture issues that were previously hard to overcome by other computing paradigms. However, it has been facing great difficulty in quickly discovering and dynamically combing available Web services to satisfy given request on-demand. Most of the current researches concentrated o n the semantic model for service discovery, composition, and so on. But there are few studies concerned the intrinsic pattern and law of the service interactions and relationships. To achiev e the vision of SOC in heterogeneous and open environment, in our opinion, not only the semantics of individual Web service but also the interactions and relationships among Web services are needed to be considered seriously. In this paper, beginning with combining Semantic Web and social networking technology within SOC paradigm, we study associations between Web services, mine the relationships among services to design and build Service Network (SN), anal y z e the structural and social characteristics and complexity of SN to reveal the user interests, business requests, information and data flow and direction. In short, we would like to reassess and reconsider the SOC paradigm from the network perspective, through finding new knowledge to build new theoretical basis and approach which can be used to guide and promote the service discovery, composition, and so on, in SOC paradigm.

In this paper we present an architecture for provenance policies which can be used to describe and enact behavioural constraints in a system in order to ensure compliance with user and organisational policies. We discuss how this architecture has been used in order to manage the behaviour of the services powering an existing virtual research environment while reasoning about the relationships between users, their social network, their roles in a project, their groups and the provenance of research data.

Service discovery and composition within the ProgrammableWeb directory is a difficult process, since it requires considerable manual effort to locate services, understand their capabilities and compose mashup applications. Furthermore, every site has its databases modeled in a specific way, causing semantically equivalent properties to be defined differently, since data is not easily shared across different domains in the Internet. With the use of Semantic Web technologies, such as description logic ontologies and reasoners to describe Web Services, automated service discovery and composition as well as data linking are made possible. Currently, Programmable Web classifies APIs in a flat categorization where each API is manually classified within a single service category. Search is limited to attributes such as protocol or messaging type and is not related to semantic attributes of the service category. We enhance the service descriptions by using an ontology to describe the domain of each service category. With an ontology description, an API can be automatically classified and queried for according to its attributes. Additionally, APIs can be distributed in ontology-based service discovery systems so that semantic registration and querying of services become possible. One of the limitations in using ontologies for describing a service domain is in creating its generic description. Current work in creating domain ontologies is limited to semi-automated ontology generation tools which create pure hierarchical classifications, given a well-defined corpus or taxonomy, but do not include property descriptions. We propose LexOnt, a semi-automatic ontology creation tool for a high-level service classification ontology. We use the PW directory as the corpus, although it may be used for other corpuses as well. The main contribution of LexOnt is its novel algorithm which generates and ranks frequent terms and significant phrases within a PW category by comparing them to external domain knowledge within Wikipedia, Wordnet and the current state of the ontology. First it matches terms to the Wikipedia page description of the category and ranks them higher, since these indicate domain descriptive words. Synonymous words from Wordnet are then matched and ranked. In a semi-automated process, the user chooses the terms it wants to add to the ontology and indicates the properties to assign these values to and the ontology is automatically generated. In the next iteration, terms within the current state of the ontology are compared to terms in the other categories and automatic property assignments are made for these API instances as well.

Successfully accomplishing everyday manipulation tasks requires robots to have substantial knowledge about the objects they interact with, the environment they operate in as well as about the properties and effects of the actions they perform. Often, this knowledge is implicitly contained in manually written control programs, which makes it hard for the robot to adapt to newly acquired information or to re-use knowledge in a different context. By explicitly representing this knowledge, control decisions can be formulated as inference tasks which can be sent as queries to a knowledge base. This allows the robot to take all information it has at query time into account to generate answers, leading to better flexibility, adaptability to changing situations, robustness, and the ability to re-use knowledge once acquired. In this paper, we report on our work towards a practical and grounded knowledge representation and inference system. The system is specifically designed to meet the challenges created by using knowledge processing techniques on autonomous robots, including specialized inference methods, grounding of symbolic knowledge in the robot's control structures, and the acquisition of the different kinds of knowledge a robot needs.

In this paper we examine the case of a mobile robot that is part of a human-robot urban search and rescue (USAR) team. During USAR scenarios, we would like the robot to have a geometrical-functional understand- ing of space, using which it can infer where to perform planned tasks in a manner that mimics human behav- ior. We assess the situation awareness of rescue work- ers during a simulated USAR scenario and use this as an empirical basis to build our robot’s spatial model. Based upon this spatial model, we present “functional map- ping” as an approach to identify regions in the USAR environment where planned tasks are likely to be opti- mally achievable. The system is deployed and evaluated in a simulated rescue scenario.

This paper describes an attempt to provide more intelligence to industrial robotics and automation systems. We develop an architecture to integrate disparate knowledge representations used in different places in robotics and automation. This knowledge integration framework, a possibly distributed entity, abstracts the components used in design or production as data sources, and provides a uniform access to them via standard interfaces. Representation is based on the ontology formalizing the process, product and resource triangle, where skills are considered the common element of the three. Production knowledge is being collected now and a preliminary version of KIF undergoes verification.

This paper takes new look on language and knowledge modelling for corpus linguistics. Using ideas of Chaitin, a line of argument is made against language/knowledge separation in Natural Language Processing. A simplistic model, that generalises approaches to language and knowledge, is proposed. One of hypothetical consequences of this model is Strong AI.

In a world where communication and information sharing are at the heart of our business, the terminology needs are most pressing. It has become imperative to identify the terms used and defined in a consensual and coherent way while preserving linguistic diversity. To streamline and strengthen the process of acquisition, representation and exploitation of scenarii of train accidents, it is necessary to harmonize and standardize the terminology used by players in the security field. The research aims to significantly improve analytical activities and operations of the various safety studies, by tracking the error in system, hardware, software and human. This paper presents the contribution of ontology to modeling scenarii for rail accidents through a knowledge model based on a generic ontology and domain ontology. After a detailed presentation of the state of the art material, this article presents the first results of the developed model.

We propose a novel, type-elimination-based method for reasoning in the description logic SHIQbs including DL-safe rules. To this end, we first establish a knowledge compilation method converting the terminological part of an ALCIb knowledge base into an ordered binary decision diagram (OBDD) which represents a canonical model. This OBDD can in turn be transformed into disjunctive Datalog and merged with the assertional part of the knowledge base in order to perform combined reasoning. In order to leverage our technique for full SHIQbs, we provide a stepwise reduction from SHIQbs to ALCIb that preserves satisfiability and entailment of positive and negative ground facts. The proposed technique is shown to be worst case optimal w.r.t. combined and data complexity and easily admits extensions with ground conjunctive queries.

Social media analytics has been attracting considerable attention in both research and industry due to the increasing popularity of social media usage. As a subset, social media monitoring describes the process of continuous monitoring of a subject matter in social media. From our point of view, the key requirements for such systems are i) high throughput and real-time processing of incoming data, ii) a user-friendly way to define complex situations of interests that make use of formalized background knowledge and iii) capabilities to perform actions based on gained insights instead of a pure monitoring system. In this paper, we propose a system for (pro) active, real-time social media monitoring. Firstly, we describe the conceptual architecture of our system and necessary pre-processing steps. Secondly, we introduce our concept of semantic requests that is capable to extend event pattern definitions with background knowledge. Finally, we show the usefulness of this system in two different domains: Real-time political opinion tracking and proactive establishment of relationships with consumers in order to perform a new form of real-time marketing. The main advantage of our approach is a simplified, expressive way to formulate event patterns in social media applications.