Semantic annotation for knowledge management:requirements and a survey of the state of the art

While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents. In the emerging Semantic Web, search, interpretation and aggregation can be addressed by ontology-based semantic mark-up. In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems. This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress.

A semantic modelling approach to knowledge based statistical software

The topic of this thesis is the development of knowledge based statistical software. The shortcomings of conventional statistical packages are discussed to illustrate the need to develop software which is able to exhibit a greater degree of statistical expertise, thereby reducing the misuse of statistical methods by those not well versed in the art of statistical analysis. Some of the issues involved in the development of knowledge based software are presented and a review is given of some of the systems that have been developed so far. The majority of these have moved away from conventional architectures by adopting what can be termed an expert systems approach. The thesis then proposes an approach which is based upon the concept of semantic modelling. By representing some of the semantic meaning of data, it is conceived that a system could examine a request to apply a statistical technique and check if the use of the chosen technique was semantically sound, i.e. will the results obtained be meaningful. Current systems, in contrast, can only perform what can be considered as syntactic checks. The prototype system that has been implemented to explore the feasibility of such an approach is presented, the system has been designed as an enhanced variant of a conventional style statistical package. This involved developing a semantic data model to represent some of the statistically relevant knowledge about data and identifying sets of requirements that should be met for the application of the statistical techniques to be valid. Those areas of statistics covered in the prototype are measures of association and tests of location.

State-of-the-art in lean design engineering:a literature review on white collar lean

Lean is usually associated with the ‘operations’ of a manufacturing enterprise; however, there is a growing awareness that these principles may be transferred readily to other functions and sectors. The application to knowledge-based activities such as engineering design is of particular relevance to UK plc. Hence, the purpose of this study has been to establish the state-of-the-art, in terms of the adoption of Lean in new product development, by carrying out a systematic review of the literature. The authors' findings confirm the view that Lean can be applied beneficially away from the factory; that an understanding and definition of value is key to success; that a set-based (or Toyota methodology) approach to design is favoured together with the strong leadership of a chief engineer; and that the successful implementation requires organization-wide changes to systems, practices, and behaviour. On this basis it is felt that this review paper provides a useful platform for further research in this topic.

Event trigger identification for biomedical events extraction using domain knowledge

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. © 2014 The Author 2014. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip.

Decision support based on genomics:integration of data-and knowledge-driven reasoning

The breadth and depth of available clinico-genomic information, present an enormous opportunity for improving our ability to study disease mechanisms and meet the individualised medicine needs. A difficulty occurs when the results are to be transferred 'from bench to bedside'. Diversity of methods is one of the causes, but the most critical one relates to our inability to share and jointly exploit data and tools. This paper presents a perspective on current state-of-the-art in the analysis of clinico-genomic data and its relevance to medical decision support. It is an attempt to investigate the issues related to data and knowledge integration. Copyright © 2010 Inderscience Enterprises Ltd.

Servitization:revisiting the state-of-the-art and research priorities

Purpose – This paper consolidates the servitization knowledge base from an organisational change perspective, identifying developed, developing and undeveloped topics to provide a platform that directs future research. Design/methodology/approach – This paper addresses three objectives : a) it comprehensively examines organisational change management literature for selection of a theoretical framework, b) it classifies extant studies within the framework through a systemic literature review, and (c) it analyses 232 selected papers and proposes a research agenda. Findings – Analysis suggests increasing global awareness of the importance of services to manufacturers. However, some topics, especially related to servitization transformation, remain undeveloped. Research limitations/implications – Although the authors tried to include all publications relevant to servitization, some might not have been captured. Evaluation and interpretation relied on the research team and subsequent research workshops. Practical implications - One of the most significant challenges for practitioners of servitization is how to transform a manufacturing organisation to exploit the opportunity. This paper consolidates literature regarding servitization, identifying progress concerning key research topics and contributing a platform for future research. The goal is to inform research to result eventually in a roadmap for practitioners seeking to servitize. Originality/value - Although extant reviews of servitization identify themes that are examined well, they struggle to identify unanswered questions. This paper addresses this gap by focusing on servitization as a process of organisational change.

Intersubjectivity and sentiment:from language to knowledge

Intersubjectivity is an important concept in psychology and sociology. It refers to sharing conceptualizations through social interactions in a community and using such shared conceptualization as a resource to interpret things that happen in everyday life. In this work, we make use of intersubjectivity as the basis to model shared stance and subjectivity for sentiment analysis. We construct an intersubjectivity network which links review writers, terms they used, as well as the polarities of the terms. Based on this network model, we propose a method to learn writer embeddings which are subsequently incorporated into a convolutional neural network for sentiment analysis. Evaluations on the IMDB, Yelp 2013 and Yelp 2014 datasets show that the proposed approach has achieved the state-of-the-art performance.