Dealing with Semantic Heterogeneity Issues on the Web

The Semantic Web is an extension of the traditional Web in which meaning of information is well defined, thus allowing a better interaction between people and computers. To accomplish its goals, mechanisms are required to make explicit the semantics of Web resources, to be automatically processed by software agents (this semantics being described by means of online ontologies). Nevertheless, issues arise caused by the semantic heterogeneity that naturally happens on the Web, namely redundancy and ambiguity. For tackling these issues, we present an approach to discover and represent, in a non-redundant way, the intended meaning of words in Web applications, while taking into account the (often unstructured) context in which they appear. To that end, we have developed novel ontology matching, clustering, and disambiguation techniques. Our work is intended to help bridge the gap between syntax and semantics for the Semantic Web constructio

A finder and representation system for knowledge carriers based on granular computing

In one of his publications Aristotle states ”All human beings by their nature desire to know” [Kraut 1991]. This desire is initiated the day we are born and accompanies us for the rest of our life. While at a young age our parents serve as one of the principle sources for knowledge, this changes over the course of time. Technological advances and particularly the introduction of the Internet, have given us new possibilities to share and access knowledge from almost anywhere at any given time. Being able to access and share large collections of written down knowledge is only one part of the equation. Just as important is the internalization of it, which in many cases can prove to be difficult to accomplish. Hence, being able to request assistance from someone who holds the necessary knowledge is of great importance, as it can positively stimulate the internalization procedure. However, digitalization does not only provide a larger pool of knowledge sources to choose from but also more people that can be potentially activated, in a bid to receive personalized assistance with a given problem statement or question. While this is beneficial, it imposes the issue that it is hard to keep track of who knows what. For this task so-called Expert Finder Systems have been introduced, which are designed to identify and suggest the most suited candidates to provide assistance. Throughout this Ph.D. thesis a novel type of Expert Finder System will be introduced that is capable of capturing the knowledge users within a community hold, from explicit and implicit data sources. This is accomplished with the use of granular computing, natural language processing and a set of metrics that have been introduced to measure and compare the suitability of candidates. Furthermore, are the knowledge requirements of a problem statement or question being assessed, in order to ensure that only the most suited candidates are being recommended to provide assistance

A Business Ontology for supporting cross border cooperation between European Chambers of Commerce

The recent EU enlargement opens up new opportunities, but poses new issues to be addressed. In particular, in order to enable and support cooperation between firms from different countries, it is necessary to address interoperability issues. The LD-CAST project aims at enabling cross border cooperation between European chambers of commerce (CCs) for supporting the development of private company initiatives. The project objective is to build a European network of portals that will enable end users (mainly private companies) to access in a seamless mode services provided by public organizations registered in each portal. This paper briefly presents a cooperation framework for semantic interoperability mainly based on the following semantic technologies: ontology management, semantic annotation, and semantic search and discovery. Finally the business ontology produced in the course of the project is presented.The recent EU enlargement opens up new opportunities, but poses new issues to be addressed. In particular, in order to enable and support cooperation between firms from different countries, it is necessary to address interoperability issues. The LD-CAST project aims at enabling cross border cooperation between European chambers of commerce (CCs) for supporting the development of private company initiatives. The project objective is to build a European network of portals that will enable end users (mainly private companies) to access in a seamless mode services provided by public organizations registered in each portal. This paper briefly presents a cooperation framework for semantic interoperability mainly based on the following semantic technologies: ontology management, semantic annotation, and semantic search and discovery. Finally the business ontology produced in the course of the project is presented.Uninvited Submission

Semantics-based selection of everyday concepts in visual lifelogging

Concept-based indexing, based on identifying various semantic concepts appearing in multimedia, is an attractive option for multimedia retrieval and much research tries to bridge the semantic gap between the media’s low-level features and high-level semantics. Research into concept-based multimedia retrieval has generally focused on detecting concepts from high quality media such as broadcast TV or movies, but it is not well addressed in other domains like lifelogging where the original data is captured with poorer quality. We argue that in noisy domains such as lifelogging, the management of data needs to include semantic reasoning in order to deduce a set of concepts to represent lifelog content for applications like searching, browsing or summarisation. Using semantic concepts to manage lifelog data relies on the fusion of automatically-detected concepts to provide a better understanding of the lifelog data. In this paper, we investigate the selection of semantic concepts for lifelogging which includes reasoning on semantic networks using a density-based approach. In a series of experiments we compare different semantic reasoning approaches and the experimental evaluations we report on lifelog data show the efficacy of our approach

Maintenance Knowledge Management with Fusion of CMMS and CM

Abstract- Maintenance can be considered as an information, knowledge processing and management system. The management of knowledge resources in maintenance is a relatively new issue compared to Computerized Maintenance Management Systems (CMMS) and Condition Monitoring (CM) approaches and systems. Information Communication technologies (ICT) systems including CMMS, CM and enterprise administrative systems amongst others are effective in supplying data and in some cases information. In order to be effective the availability of high-quality knowledge, skills and expertise are needed for effective analysis and decision-making based on the supplied information and data. Information and data are not by themselves enough, knowledge, experience and skills are the key factors when maximizing the usability of the collected data and information. Thus, effective knowledge management (KM) is growing in importance, especially in advanced processes and management of advanced and expensive assets. Therefore efforts to successfully integrate maintenance knowledge management processes with accurate information from CMMSs and CM systems will be vital due to the increasing complexities of the overall systems. Low maintenance effectiveness costs money and resources since normal and stable production cannot be upheld and maintained over time, lowered maintenance effectiveness can have a substantial impact on the organizations ability to obtain stable flows of income and control costs in the overall process. Ineffective maintenance is often dependent on faulty decisions, mistakes due to lack of experience and lack of functional systems for effective information exchange [10]. Thus, access to knowledge, experience and skills resources in combination with functional collaboration structures can be regarded as vital components for a high maintenance effectiveness solution. Maintenance effectiveness depends in part on the quality, timeliness, accuracy and completeness of information related to machine degradation state, based on which decisions are made. Maintenance effectiveness, to a large extent, also depends on the quality of the knowledge of the managers and maintenance operators and the effectiveness of the internal & external collaborative environments. With emergence of intelligent sensors to measure and monitor the health state of the component and gradual implementation of ICT) in organizations, the conceptualization and implementation of E-Maintenance is turning into a reality. Unfortunately, even though knowledge management aspects are important in maintenance, the integration of KM aspects has still to find its place in E-Maintenance and in the overall information flows of larger-scale maintenance solutions. Nowadays, two main systems are implemented in most maintenance departments: Firstly, Computer Maintenance Management Systems (CMMS), the core of traditional maintenance record-keeping practices that often facilitate the usage of textual descriptions of faults and actions performed on an asset. Secondly, condition monitoring systems (CMS). Recently developed (CMS) are capable of directly monitoring asset components parameters; however, attempts to link observed CMMS events to CM sensor measurements have been limited in their approach and scalability. In this article we present one approach for addressing this challenge. We argue that understanding the requirements and constraints in conjunction - from maintenance, knowledge management and ICT perspectives - is necessary. We identify the issues that need be addressed for achieving successful integration of such disparate data types and processes (also integrating knowledge management into the “data types” and processes)

Understanding systems thinking:an agenda for applied research in industry

Why systems thinking is valuable is relatively easy to explain.  However, in the authors’ work as university educators, teaching a student processes of enquiry that are themselves systemic is a difficult undertaking.  The capacity to view the world in systemic ways seems an innate characteristic that some individuals possess.  Might it be the case that being a systems thinker is dependent on holding a particular worldview?  Systems theorists have evolved tools and methodologies to help people do systems thinking.  Is being a user of systems methods the same as being a systems thinker? Are certain cognitive competencies, styles, or preferences required for people to make effective use of such tools and methodologies