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The purpose of this paper is to discuss sentiment search, which not only retrieves data related to submitted keywords but also identifies sentiment opinion implied in the retrieved data and the subject targeted by this opinion.

The authors propose a retrieval framework known as Cross-Domain Sentiment Search (CSS), which combines the usage of domain ontologies with specific linguistic rules to handle sentiment terms in textual data. The CSS framework also supports incrementally enriching domain ontologies when applied in new domains.

The authors found that domain ontologies are extremely helpful when CSS is applied in specific domains. In the meantime, the embedded linguistic rules make CSS achieve better performance as compared to data mining techniques.

The approach has been initially applied in a real social monitoring system of a professional IT company. Thus, it is proved to be able to handle real data acquired from social media channels such as electronic newspapers or social networks.

The authors have placed aspect-based sentiment analysis in the context of semantic search and introduced the CSS framework for the whole sentiment search process. The formal definitions of Sentiment Ontology and aspect-based sentiment analysis are also presented. This distinguishes the work from other related works.

Enterprise knowledge graphs (EKG) in resource description framework (RDF) consolidate and semantically integrate heterogeneous data sources into a comprehensive dataspace. However, to make an external relational data source accessible through an EKG, an RDF view of the underlying relational database, called an RDB2RDF view, must be created. The RDB2RDF view should be materialized in situations where live access to the data source is not possible, or the data source imposes restrictions on the type of query forms and the number of results. In this case, a mechanism for maintaining the materialized view data up-to-date is also required. The purpose of this paper is to address the problem of the efficient maintenance of externally materialized RDB2RDF views.

This paper proposes a formal framework for the incremental maintenance of externally materialized RDB2RDF views, in which the server computes and publishes changesets, indicating the difference between the two states of the view. The EKG system can then download the changesets and synchronize the externally materialized view. The changesets are computed based solely on the update and the source database state and require no access to the content of the view.

The central result of this paper shows that changesets computed according to the formal framework correctly maintain the externally materialized RDB2RDF view. The experiments indicate that the proposed strategy supports live synchronization of large RDB2RDF views and that the time taken to compute the changesets with the proposed approach was almost three orders of magnitude smaller than partial rematerialization and three orders of magnitude smaller than full rematerialization.

The main idea that differentiates the proposed approach from previous work on incremental view maintenance is to explore the object-preserving property of typical RDB2RDF views so that the solution can deal with views with duplicates. The algorithms for the incremental maintenance of relational views with duplicates published in the literature require querying the materialized view data to precisely compute the changesets. By contrast, the approach proposed in this paper requires no access to view data. This is important when the view is maintained externally, because accessing a remote data source may be too slow.

Text mining has been used for various knowledge discovery based applications, and thus, a lot of research has been contributed towards it. Latest trending research in the text mining is adopting the incremental learning data, as it is economical while dealing with large volume of information.

The primary intention of this research is to design and develop a technique for incremental text categorization using optimized Support Vector Neural Network (SVNN). The proposed technique involves four major steps, such as pre-processing, feature selection, classification and feature extraction. Initially, the data is pre-processed based on stop word removal and stemming. Then, the feature extraction is done by extracting semantic word-based features and Term Frequency and Inverse Document Frequency (TF-IDF). From the extracted features, the important features are selected using Bhattacharya distance measure and the features are subjected as the input to the proposed classifier. The proposed classifier performs incremental learning using SVNN, wherein the weights are bounded in a limit using rough set theory. Moreover, for the optimal selection of weights in SVNN, Moth Search (MS) algorithm is used. Thus, the proposed classifier, named Rough set MS-SVNN, performs the text categorization for the incremental data, given as the input.

For the experimentation, the 20 News group dataset, and the Reuters dataset are used. Simulation results indicate that the proposed Rough set based MS-SVNN has achieved 0.7743, 0.7774 and 0.7745 for the precision, recall and F-measure, respectively.

In this paper, an online incremental learner is developed for the text categorization. The text categorization is done by developing the Rough set MS-SVNN classifier, which classifies the incoming texts based on the boundary condition evaluated by the Rough set theory, and the optimal weights from the MS. The proposed online text categorization scheme has the basic steps, like pre-processing, feature extraction, feature selection and classification. The pre-processing is carried out to identify the unique words from the dataset, and the features like semantic word-based features and TF-IDF are obtained from the keyword set. Feature selection is done by setting a minimum Bhattacharya distance measure, and the selected features are provided to the proposed Rough set MS-SVNN for the classification.

This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state‐of‐the‐art in the area of activity recognition, in particular, in the area of object‐based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity‐based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy‐of‐use.

The paper initially reviews the existing approaches and algorithms, which have been used for activity recognition in a number of related areas. From each of these, their strengths and weaknesses are discussed with particular emphasis being placed on the application domain of sensor enabled intelligent pervasive environments. Based on an analysis of existing solutions, the paper then proposes an integrated ontology‐based approach to activity recognition. The proposed approach adopts ontologies for modeling sensors, objects and activities, and exploits logical semantic reasoning for the purposes of activity recognition. This enables incremental progressive activity recognition at both coarse‐grained and fine‐grained levels. The approach has been considered within the realms of a real world activity recognition scenario in the context of assisted living within Smart Home environments.

Existing activity recognition methods are mainly based on probabilistic reasoning, which inherently suffer from a number of limitations such as ad hoc static models, data scarcity and scalability. Analysis of the state‐of‐the‐art has helped to identify a major gap between existing approaches and the need for novel recognition approaches posed by the emerging multimodal sensor technologies and context‐aware personalised activity‐based applications in intelligent pervasive environments. The proposed ontology based approach to activity recognition is believed to be the first of its kind, which provides an integrated framework‐based on the unified conceptual backbone, i.e. activity ontologies, addressing the lifecycle of activity recognition. The approach allows easy incorporation of domain knowledge and machine understandability, which facilitates interoperability, reusability and intelligent processing at a higher level of automation.

The comprehensive overview and critiques on existing work on activity recognition provide a valuable reference for researchers and system developers in related research communities. The proposed ontology‐based approach to activity recognition, in particular the recognition algorithm has been built on description logic based semantic reasoning and offers a promising alternative to traditional probabilistic methods. In addition, activities of daily living (ADL) activity ontologies in the context of smart homes have not been, to the best of one's knowledge, been produced elsewhere.

This paper presents a framework for ontology evolution tailored to Digital Libraries, which makes use of two different sources for change detection and propagation, the usage of ontologies by users and the changes of available data.

After presenting the logical architecture of the evolution framework, we first illustrate how to deal with usage‐driven changes, that is changes derived from the actual usage of ontologies. Second, we describe the generation of data‐driven ontology changes based on the constant flow of documents coming into digital libraries.

The proposed framework for ontology ontology evolution, which is currently applied and evaluated in the case studies, significantly reduces the costs of ontology updates and improves the quality of the ontology with respect to the users' requirements.

The management of dynamic knowledge is crucial for many knowledge management applications. Our approach for usage‐driven and data‐driven change discovery not only assures the consistency of ontologies modeling dynamic knowledge, but also reduces the burden of manual ontology engineering.

This paper presents the first approach towards a common framework for ontology evolution based on usage‐driven and data‐driven change discovery.

Domain-specific process modeling has been proposed in the literature as a solution to several problems in business process management. The problems arise when using only the generic Business Process Model and Notation (BPMN) standard for modeling. This language includes domain ambiguity and difficult long-term model evolution. Domain-specific modeling involves developing concept definitions, domain-specific processes and eventually industry-standard BPMN models. This entails a multi-layered modeling approach, where any of these artifacts can be modified by various stakeholders and changes done by one person may influence models used by others. There is therefore a need for tool support to keep track of changes done and their potential impacts. The paper aims to discuss these issues.

The authors use a multi-context systems-based approach to infer the impacts that changes may cause in the models; and alsothe authors incrementally map components of business process models to ontologies.

Advantages of the framework include: identifying conflicts/inconsistencies across different business modeling layers; expressing rich information on the relations between two layers; calculating the impact of changes taking place in one layer to the rest of the layers; and selecting incrementally the most appropriate semantic models on which the transformations can be based.

The authors consider this work as one of the foundational bricks that will enable further advances toward the governance of multi-layer business process modeling systems. Extensive usability tests would enable to further confirm the findings of the paper.

The approach described here should improve the maintainability, reuse and clarity of business process models and in extension improve data governance in large organizations. The approaches described here should improve the maintainability, reuse and clarity of business process models. This can improve data governance in large organizations and for large collections of processes by aiding various stakeholders to understand problems with process evolutions, changes and inconsistencies with business goals.

This paper fulfills an identified gap to enabling semantically aided domain–specific process modeling.

Seeking and retrieving information is an essential aspect of knowledge workers' activities during problem‐solving and decision‐making tasks. In recent years, user‐oriented Information Seeking (IS) research methods rooted in the social sciences have been integrated with Information Retrieval (IR) research approaches based on computer science to capitalize on the strengths of each field. Given this background, the objective is to develop a topic‐needs variation determination technique based on the observations of IS&R theories.

In this study, implicit and explicit methods for identifying users' evolving topic‐needs are proposed. Knowledge‐intensive tasks performed by academic researchers are used to evaluate the efficacy of the proposed methods. The paper conducted two sets of experiments to demonstrate and verify the importance of determining changes in topic‐needs during the IS&R process.

The results in terms of precision and discounted cumulated gain (DCG) values show that the proposed Stage‐Topic_W (G,S) and Stage‐Topic‐Interaction methods can retrieve relevant document sets for users engaged in long‐term tasks more efficiently and effectively than traditional methods.

The improved precision of the proposed methods means that they can retrieve more relevant documents for the searcher. Accordingly, the results of this research have implications for enhancing the search function in enterprise content management (ECM) applications to support the execution of projects/tasks by professionals and facilitate effective ECM.

The model observes a user's search behavior pattern to determine the personal factors (e.g. changes in the user's cognitive status), and content factors (e.g. changes in topic‐needs) simultaneously. The objective is to capture changes in the user's information needs precisely so that evolving information needs can be satisfied in a timely manner.

The paper shows how access to knowledge can be enhanced by using a set of innovative approaches and technologies based on the semantic web.

Emerging trends in knowledge access are considered followed by a description of how ontologies and semantics can contribute. A set of tools is then presented which is based on semantic web technology. For each of these tools a detailed description of the approach is given together with an analysis of related and future work as appropriate.

The tools presented are at the prototype stage but can already show how knowledge access can be improved by allowing users to more precisely express what they are looking for and by presenting to them in a form that is appropriate to their current context.

The tools show promising results in improving access to knowledge which will be further evaluated within a practical setting. The tools will be integrated and trialled as part of case studies within the SEKT project. This will allow their usability and practical applicability to be measured.

Ontologies as a form of knowledge representation are increasing in importance. Knowledge management, and in particular knowledge access, will benefit from their widespread acceptance. The use of open standards and compatible tools in this area will be important to support interoperability and widespread access to disparate knowledge repositories.

The paper presents research in an emerging but increasingly important field, i.e. semantic web‐based knowledge technology. It describes how this technology can satisfy the demand for improved knowledge access, including providing knowledge delivery to users at the right time and in the correct form.

The purpose of this paper is to propose a hybrid ontology‐based solution to expand user's queries.

The solution aims for ontology development and query expansion with ontology‐based approach. The first task is to develop an ontology (named OMP), which relates to key‐properties and key‐members of objects described in words/terms of English vocabulary. Its training methodology is also a hybrid, rule‐based with proposed patterns and statistical‐based solution for selecting the best candidates from TREC English corpus. The second is proposals for mechanisms not only to look for relative result in the ontology OMP to complete and expand user's entered query/noun phrase, but also to expand the search progress by linking the OMP ontology to indexes of information retrieval system. Especially, the base of these two tasks is our proposal for four kinds of semantic relationship of words.

Several semantic relationships among words in vocabulary has been introduced and currently used in WordNet to represent the system of semantic networks. In another way, our analyzing for words in English vocabulary found that there are some kinds of semantic dependency in some cases for part(s) of a noun phrase, and it can be represented in grammar noun phrase syntax. That affects not only the proposed approach of ontology OMP development via identifying four kinds of semantic relationship and organizing its structure including core element types such as object and key‐member and key‐property, but also ontology training mechanism and solutions of query expansion by adding extended correspondent words (based on that relationship) to original query.

In initial iteration, the approach is applied for English query only with limited size of ontology OMP and dependency on grammar rules‐based in creating patterns to extract data from corpus. For future research, applications for other languages (Vietnamese, Chinese …) with sharp focus on improvement of ontology training quality/quantity and query expansion precision are primary targets.

The developed ontology OMP can be shared as a support for other applications such as semantic data extraction or semantic information retrieval in other researches.

This paper fulfils an approach of ontology‐based query expansion and theoretical definitions of semantic relationship among words. Specially, these kinds of relationship can use to develop a useful semantic network system.

As they say, “Change is the only constant.” Thriving and surviving during a period of extraordinary collision of technological advances, globalization, and climate change can be daunting. At any given point in one’s life, a transition can be interpreted in terms of the magnitude of change (how big or small) and the individual’s ontological experience of change (whether it disrupts an equilibrium or adapts an emergent way of life). These four quadrants represent different ways to live in a highly dynamic and complex world. We share the resulting four-quadrant framework from a quantitative and a mixed methods study to examine responses to various ways we respond to transitions. Contingent upon these two dimensions, one can use a four-quadrant framework to mobilize resources to design a response and hypothesize a desired outcome. Individuals may find themselves at various junctions of these quadrants over a lifespan. These four quadrants provide “requisite variety” to navigate individual ontology as they move into and out of fluid spaces we often call instability during a time of transition. In this chapter, we identified social, cognitive, psychological, and behavioral factors that contribute to thriving transition experiences, embracing dynamic stability. Two new constructs were developed, the first measures the receptivity to change, Transformation Quotient (TQ) and second measures the range of responses to transitions from surviving to thriving, Thriving Transitional Experiences (TTE). We hope our work will pave the way for Thriving to become a “normal” outcome of experiencing change by transforming the lexicon and expectation of engaging with transitions.