Abstract: Methods and systems for knowledge extraction that involve providing analytics and blending the analytics with analysis of one or more knowledge processes are provided. Knowledge extraction may be based on combining analytic approaches, such as statistical and machine learning approaches. Unstructured data, such as numerical, geo-spatial, text, speech, image, video, data, and music, may be used as input for these processes. The methods and systems may convert this unstructured data into a structured knowledge that has some specific utility to its user. Some embodiments may involve service requests delivery, information and knowledge extraction, information and knowledge retrieval, media mining, marketing, and other uses. Different granularity levels of knowledge and information extraction may be provided. This differentiation may be used for monetization of the service.

Abstract: Purpose – The purpose of this paper is to identify the knowledge strategies applied in aided secondary schools in Hong Kong and to explore the predictive relationship between knowledge strategies and school learning capacity.Design/methodology/approach – A cross‐sectional quantitative survey was designed to collect data from 427 teachers at 15 aided secondary schools in Hong Kong. Exploratory factor analysis and a structural equation model were applied to explore the factor structure of the latent variables and their relationships.Findings – The results showed that the schools use interpersonal interactive knowledge sharing as their major knowledge strategy. Knowledge retrieval, utilisation and sharing were identified as the predictive factors for individual learning capacity and organisation learning capacity.Practical implications – School administrators could consider the knowledge strategy of cultivating a community of practices in school organisation to develop teacher teaching competency and to enha...

Abstract: In the crisis-prone and complex contemporary business environment, modern organisations and their supply chains, large and small, are challenged by crises more than ever. Knowledge management has been acknowledged as an important discipline able to support the management of complexity in times of crisis. However, the role of effective knowledge retrieval and sharing in the process of crisis prevention, management and survival has been relatively underexplored. In this paper, it is argued that organisational crises create additional challenges for knowledge management, mainly because complex, polymorphic and both structured and unstructured knowledge must be efficiently harnessed, processed and disseminated to the appropriate internal and external supply chain actors, under specific time constraints. In this perspective, a process-based approach is proposed to address the knowledge management needs of organisations during a crisis and to help management in establishing the necessary risk avoidance and recovery mechanisms. Finally, the proposed methodological approach is applied in a knowledge-intensive Greek small and medium enterprise from the pharmaceutical industry, producing empirical results, insights on knowledge pathologies during crises and relevant evaluations. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract: This paper explores the knowledge demands of expertise seekers for the purpose of designing effective expertise locator systems. We conduct an empirical investigation, using conjoint analysis and within-subject tests, to determine the relative importance assigned to different expert attributes under two expertise seeking contexts: knowledge allocation and knowledge retrieval. Our results show that when choosing an expert to retrieve knowledge from (knowledge retrieval), expertise seekers will assign greater importance to the person's level of expertise. When selecting an expert to transfer knowledge to (knowledge allocation), attributes representing the network ties between the expert and the seeker as well as the benevolence of the expert will be perceived as more important. These results are important for the design of expertise locator systems that are better customized to fit the knowledge needs of their users, and to serve the organization as a whole.

Abstract: Web-scale knowledge retrieval can be enabled by distributed information retrieval; clustering Web clients to a large-scale computing infrastructure for knowledge discovery from Web documents. Based on this infrastructure, we propose to apply semiotic (i.e., sub-syntactical) and inductive (i.e., probabilistic) methods for inferring concept associations in human knowledge. These associations can be combined to form a fuzzy (i.e., gradual) semantic net representing a map of the knowledge in the Web. Thus, we propose to provide interactive visualizations of these cognitive concept maps to end users, who can browse and search the Web in a human-oriented, visual, and associative interface.

Abstract: Ontology is a conceptualization of a domain into machine readable format. Ontologies are becoming increasingly popular modelling schemas for knowledge management services and applications. Focus on developing tools to graphically visualise ontologies is rising to aid their assessment and analysis. Graph visualisation helps to browse and comprehend the structure of ontologies. A number of ontology visualizations exist that have been embedded in ontology management tools. The primary goal of this paper is to analyze recently implemented ontology visualization tools and their contributions in the enrichment of users’ cognitive support. This work also presents the preliminary results of an evaluation of three visualization tools to determine the suitability of each method for end user applications where ontologies are used as browsing aids with a case of Diabetes data..

Abstract: The purpose of this paper is to present different issues facing the problem of knowledge retention by construction sector companies in the UAE. Existing framework in the area of knowledge retention has been used to assess three large construction consultancies in the UAE. The case study methodology used in this paper highlights some key issues in the area of knowledge retention in the UAE. Based on the analysis of the knowledge retention system the major drivers for its successful implementation are prevalence of a culture of sharing knowledge, reward and recognition for sharing knowledge, a technology platform that can accommodate multi formats of files, awareness of knowledge retention system and its benefits among its employees, and top management support. The paper presents three case studies and draws conclusions about the UAE construction sector. Although the three companies are large companies, there are several Small and Medium sized Enterprise (SME) operating in the construction sector in the UAE. Future researchers need to look at these SMEs. Through the three case studies, several issues related to the implementation of robust knowledge retention practices have been identified and highlighted for the UAE construction sector.

Abstract: E-Learning is a critical support mechanism for educational institutions to grow the performance of their students, teachers, as well as useful for organizations to enhance the performance of their employees. The efficient retrieval of learning knowledge is a critical support mechanism for educational institutions to enhance the skills of their students and at the same time useful for learning process. Teachers and students face many difficulties when working with knowledge retrieval in education. Studies show that still it demands more effective approach. Semantic and Artificial Intelligence represent potential technologies for realizing e-Learning requirements. The convergence of e-Learning and digital libraries creates challenge to be solved not only at storage resources level but also at the knowledge retrieval level. Nowadays availability of infrastructure, flexibility of time, learning resources and their means of sharing has increased adaptability of Digital Library to learn and attain knowledge to a great extent. The objective of our study is to present an e-learning management system for Digital Library in Seville University that provides a general platform for learning environment. The idea is investigated from a search perspective possible intelligent infrastructures form constructing decentralized digital libraries where a global semantic schema exists. We suggest a conceptual architecture for a semantic and intelligent search engine. This project is a collaborative effort that proposes a new form of interaction between engineering students and E-learning platform, where the latter is adapted to individuals and their surroundings. We propose a comprehensive approach for discovering information objects in large digital collections based on analysis of recorded semantic metadata and the application of Artificial Intelligent technologies.

Abstract: This paper presents the experience gained on semantic web service composition technique applied to the bioinformatics domain. Specifically, the approach presented here consists of knowledge retrieval perspective in biological pathway. Semantic web services, annotated with domain ontology are used to describe services for pathway knowledge retrieval for Kyoto Encyclopedia of Gene and Genomes (KEGG) database. Retrieving knowledge can be seen as high level goals and the tasks involved can be decomposed into subtask to achieve the specified goals. We execute the composition of service by treating composition as planning problem using Hierarchical Task Network (HTN) planning system based on Simple Hierarchical Order Planner 2 (SHOP2). The approach for plan (task) decomposition using SHOP2 is implemented in automated way. We investigate the effectiveness of this approach by applying real world scenario in pathway information retrieval for Lactococcus Lactis (L. lactis) organism where biologists need to find out the pathway description from the given specific gene of interest.

Abstract: Rapid emergence of knowledge retrieval techniques have assists in innovation especially in search engine related field. However, there is still room for improvement related to data reliability. Most knowledge on the Web is presented as natural-language text that understandable by human but difficult for computers to interpret. Therefore, Semantic Web approach is widely used to give more reliable application. This paper presents an idea of combining three concepts to enhance knowledge retrieval processes using Semantic Web technologies. Instead of using ontology and clustering/categorization alone, we are injecting user profiling concept from Relational Database (RDB) to ensure more reliable data are obtained in terms of users? perspective. The impact of Knowledge Retrieval using proposed technique will be continues in future work.

Abstract: The Internet Medical Consultant-IMC is a knowledge sharing system for physicians. The system's main purpose is to collect and store the communication between its users and to provide easy retrieval of stored information. The system provides access to human generated knowledge at the point of care. Having that kind of knowledge at hand can be very helpful for physicians when they make decisions. This paper describes the process of knowledge capturing, creating and searching the knowledge archive, for final utilisation of that knowledge at point of care. The process of effective knowledge retrieval is represented more thoroughly by several modifications of algorithms for that purpose.

Abstract: There has been a considerable advance in computing, to mimic the way in which the brain tries to comprehend and structure the information to retrieve meaningful knowledge. It is identified that neuronal entities hold whole of the knowledge that the species makes use of. We intended to develop a modified knowledge based system, termed as Informledge System (ILS) with autonomous nodes and intelligent links that integrate and structure the pieces of knowledge. We conceive that every piece of knowledge is a cluster of cross-linked and correlated structure. In this paper, we put forward the theory of the nodes depicting concepts, referred as Entity Concept State which in turn is dealt with Concept State Diagrams (CSD). This theory is based on an abstract framework provided by the concepts. The framework represents the ILS as the weighted graph where the weights attached with the linked nodes help in knowledge retrieval by providing the direction of connectivity of autonomous nodes present in knowledge thread traversal. Here for the first time in the process of developing Informledge, we apply tenor computation for creating intelligent combinatorial knowledge with cross mutation to create fresh knowledge which looks to be the fundamentals of a typical thought process.

Abstract: In this paper, we propose a proficient method for knowledge management in Edaphology using self organizing map (SOM). The method will assist the edaphologists and those related with agriculture in a big way by finding out the plants apt for the input query. The method has three phases namely dataset processing, neuron training and testing phase. The input data is first converted and normalized in the data processing phase. The SOM is constructed from the processed dataset after the neuron training. The plant name is outputted in response to the input user query in the testing phase. We have added the screen shots of the proposed method in the result section and also evaluated the method with use of evaluation metric values of number of plants retrieved, time of computation and memory usage. The experimental results portrayed that the knowledge engineering approach achieved persistent and compact data storage and faster and knowledge retrieval even for the unknown variables.

Abstract: The rapid and perpetual growth of knowledge on the Web has given rise to many grand challenges (such as scalability, inconsistency, uncertainty, distribution and dynamics) for traditional knowledge processing methods and systems. Knowledge representation, retrieval and reasoning methods need to evolve and adapt to the Web to face these challenges and make this vast, heterogenous knowledge useful and accessible. In this light, the International Workshop on Web-scale Knowledge Representation, Retrieval, and Reasoning (Web-KR) is initiated. This workshop serves as the third one in this workshop series. This summary discusses the scope of Web-KR and introduces the advances in this field through the accepted papers in the Web-KR 2012 workshop, co-located with CIKM 2012.

Abstract: This paper presents a model that aims to facilitate the visualization of the knowledge stored in digital repositories using visual archetypes. Archetypes are structures that contain visual representations of the real world that are known a priori by the target group, and which have semantic structures for identifying the entities of the domain represented in each region. The proposed model is supported by the framework for knowledge visualization proposed by Burkhard and describes the users’ interactions with visual archetypes. The user through the archetypes can retrieve and view the knowledge related to the entities represented in the archetypes’ images. A prototype was developed to demonstrate the feasibility of the model using archetypes in the biomedical field, the Foundational Model of Anatomy and the Unified Medical Language System as domain knowledge and the Scientific Electronic Library Online database as a document repository. The use of visual representations in archetypes facilitates the dissemination of knowledge, because these are part of the world view of users and can easily be related with prior knowledge. Visual representations are processed quickly in the brain and require less effort than the processing of textual information.

Abstract: Adopting the idea of modularizing ontology,it studies and analyses knowledge organization in mechanical products manufacturing enterprises,under the environment of Web2.0.Also,based on Web2.0 environment,it frames model of knowledge organization in mechanical products manufacturing enterprises,as well as stating key technologies(including knowledge base building,the modular connections,knowledge retrieval rules)in the model.A model of knowledge organization in mechanical products manufacturing enterprises was designed;Most functions of this system were fulfilled,and explicated the specification of the knowledge organization.The result shows that the model can have practical application to product designing and products strategy in mechanical products manufacturing enterprises.It can give theoretical directions as well.

Abstract: This paper introduces the general process of information retrieval from the starting,analysis the works and general model of knowledge retrieval model based on ontology;Focus on introducing technologies such as IGA and Multi-Agent for designing an ontology -based intelligent knowledge retrieval model to improve the existing knowledge of the intelligent search.

Abstract: Functional modeling has been widely researched in the past decades and is considered to be effective in assisting concept design. However, construction of substantial design repository with abundant knowledge maintenance is still a problem. in this paper, we propose a model and approach for retrieve functional knowledge from existing products. First, we propose the functional knowledge retrieval model. the concept of F-S pair is introduced as functional knowledge carrier. Second, we present the approach with detailed steps based on our model and an example that capturing functional knowledge from a hydraulic jack to demonstrate our method is demonstrated. Specially, it is an iterative process for exploring all interesting functional knowledge that from high level to low level hierarchies in a product.

Abstract: This research empirically examines the online social network of a national, non-profit organization which we refer to as the national alliance to reduce violence (NARV), an organization designed to bridge the gap between non-profit organizations across the US which address the issue of interpersonal violence. As a network of practice, knowledge shared by the nonprofit organizations originates across a breadth of experts in the disciplines of advocacy, science, practice and policy. Two problems served as the motivation for this research. First, how does the online network structure support current knowledge contribution and knowledge retrieval within the network? Second, how could the online network structure enhance knowledge contribution and knowledge retrieval to meet the needs of the organization? We acquired network structure and knowledge sharing data through the collection of survey responses from NARV‟s membership list. The data were analyzed as a two-mode affiliation network using UCINET, a popular application for social network analysis. For the first research question, we found that the action groups of research and public awareness are positioned to be strong sources of knowledge contribution within the current network due to the number of nodes with whom they are connected. For the second research question, we identified training and mentoring as the action group from which other nodes desire knowledge.

Abstract: Focusing on the practical difficulties of sharing and application risk management knowledge resources due to the distributed and heterogeneous characteristics in today’s drilling industry, integration methods as well as key technology and retrieval applications of drilling risk management knowledge were studied by introducing the semantic web technology. Based on the field characteristics and requirements for the sharing of drilling risk knowledge, ontology was selected as the knowledge representation method, establishing a drilling risk knowledge ontology. A framework system supporting the risk knowledge integration based on semantic web was constructed, thereby realizing seamless knowledge integration on the semantics level. On the basis of the ontology model, a prototype of knowledge semantic retrieval system was developed. Finally, an example of knowledge retrieval analysis was provided, with results demonstrating that the retrieval method established in the new system can improve the recall ratio more effectively than the traditional method of keyword retrieval.

Abstract: Automated classification and summarization of websites, as well as knowledge retrieval from web contents, are central challenges for performing accurate and focused webometrics studies. As global approaches based on open web and full webpages content fail to cope with such challenges, in this paper we first focus our approach on organizational and institutional websites, and secondly, we consider the communicational value of the data provided, especially on navigation menus, as a central information source. Another key point of our approach is that we more especially focus on the exploitation of a recent unsupervised classification technique, that not only automatically groups together websites sharing a number of features but also explicitly associates each website class with a set of specific features, or labels, characteristic of that class. As compared to a supervised classification, our approach presents the main advantage to cope with the scaling problem whilst, as we show in our experiment, providi...

Abstract: To make knowledge retrieval better aid designers for product creative design, according to the analysis of the characteristics of various stages of product design, the knowledge was classified Combined with the characteristics of knowledge resources, aiming at new type, improving type and tracking type, this paper proposed three knowledge retrieval strategies, including function solution, technical solution, special solution, and formed a knowledge retrieval framework According to the cognitive process of conceptual design, a knowledge retrieval application was discussed

Abstract: Successfully fulfilling customers’ needs with world class products whilst remaining competitive and profitable are a major driver for the aerospace industry. The 21st Century is placing ever increasing pressure upon manufacturers to deliver high complexity, technologically enabled products to instantaneously fulfill a desired purpose at the point of use. To meet such stringent criteria, companies must find ways to continuously improve, reduce waste and accelerate the product development process whilst innovating.This paper presents a multiple case study approach of turbine blade manufacturing part families which has been used to further develop a manufacturing knowledge reuse method that is being developed in partnership with a high tech aerospace company for application within a PLM environment. This method is currently being explored within the company so as to accelerate the design-make process to enable earlier availability of, and easier access to, manufacturing knowledge, thus bringing about better product performance.The contents of the paper presents a methodical approach to the study of a number of products in an effort to ascertain how the complex interrelationships between design knowledge and manufacturing knowledge change across part families and, consequently, how they affect a developed feature knowledge relationship structure (FKRS) that maps design, manufacture and inspection viewpoints of product knowledge. Utilizing the FKRS, a pragmatic way has been developed in which people-to-people knowledge can be captured and shared to facilitate a reduction in the associated lead-time for information and knowledge retrieval and reuse. For this to be more widely applicable to different types of turbine blade it is necessary to widen the scope of the research. Four case studies are presented showing the aspects that constitute a part family and how knowledge varies across the products being studied. The FKRS is applied to the captured manufacturing knowledge in an effort to prove that it can represent and model multi-context knowledge across part families.The results have shown that the approach provides a basis for the representation of complex relationship viewpoints for product features and is valid for a number of manufacturing part families.Copyright © 2012 by Rolls-Royce plc

Abstract: From the perspective of knowledge retrieval and knowledge push, this paper introduces user preference based on the traditional search engines to meet the personalization demand of the domain knowledge of emergency. A retrieval system aiming at the emergency knowledge is designed by constructing the preference-based function module, setting the frame structure of user preference and improving the core operation process of the system.

Abstract: In order to assist the designer to effectively use knowledge for product innovative design in Function-Behavior-Structure(FBS) processAccording to the characteristics of FBS design process,one kind of knowledge organization and application method based on FBS was proposedBy classifying knowledge,and analyzing the characteristics and application of each knowledge,the ordinal transformation among Function,Behavior and Structure has been realizedThe knowledge organization model was built by using ontology technologyOn this basis,three modules as problem analysis,function expansion and resource retrieval has been carried out,the knowledge retrieval method based on AFBS has been establishedFinally,use the example to verify the knowledge application in FBS design process

Abstract: Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, and network traffic flow analysis and so on. The study of ant colonies behaviour and their self-organizing capabilities is of interest to knowledge retrieval/ management and decision support systems sciences, because it provides models of distributed adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others. Previous study on Web usage mining using a concurrent Clustering, Neural based approach has shown that the usage trend analysis very much depends on the performance of the clustering of the number of requests. In this paper, a novel approach Growing Neural Gas is introduced kind of neural network, in the process of Web

Abstract: Ontology-based knowledge retrieval is one of the mainstreams of the development of information retrieval field. The evaluation mechanism is an important way to improve the efficiency of knowledge retrieval, and to enhance the user satisfaction. This paper evaluates the efficiency of knowledge retrieval models, and advances the corresponding rules and the assessment mechanisms.