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The purpose of this paper is to demonstrate the construction of a core ontology for food. To construct the core ontology, the authors propose here an approach called, yet another methodology for ontology plus (YAMO+). The goal is to exhibit the construction of a core ontology for a domain, which can be further extended and converted into application ontologies.

To motivate the construction of the core ontology for food, the authors have first articulated a set of application scenarios. The idea is that the constructed core ontology can be used to build application-specific ontologies for those scenarios. As part of the developmental approach to core ontology, the authors have proposed a methodology called YAMO+. It is designed following the theory of analytico-synthetic classification. YAMO+ is generic in nature and can be applied to build core ontologies for any domain.

Construction of a core ontology needs a thorough understanding of the domain and domain requirements. There are various challenges involved in constructing a core ontology as discussed in this paper. The proposed approach has proven to be sturdy enough to face the challenges that the construction of a core ontology poses. It is observed that core ontology is amenable to conversion to an application ontology.

The constructed core ontology for domain food can be readily used for developing application ontologies related to food. The proposed methodology YAMO+ can be applied to build core ontologies for any domain.

As per the knowledge, the proposed approach is the first attempt based on the study of the state of the art literature, in terms of, a formal approach to the design of a core ontology. Also, the constructed core ontology for food is the first one as there is no such ontology available on the web for domain food.

This paper aims to propose a brand new ontology development methodology, called Yet Another Methodology for Ontology (YAMO) and demonstrate, step by step, the building of a formally defined large-scale faceted ontology for food.

YAMO is motivated by facet analysis and an analytico-synthetic classification approach. The approach ensures quality of the system precisely; it makes the system flexible, hospitable, extensible, sturdy, dense and complete. YAMO consists of two-way approaches: top-down and bottom-up. Based on YAMO, domain food, formally defined as large-scale ontology, is designed. To design the ontology and to define the scope and boundary of the domain, a group of people were interviewed to get a practical overview, which provided more insight to the theoretical understanding of the domain.

The result obtained from evaluating the ontology is a very impressive one. Based on the study, it was found that 94 per cent of the user’s queries were successfully met. This shows the efficiency and effectiveness of the YAMO methodology. An evaluator opined that the ontology is very deep and exhaustive.

The authors envision that the current work will have great implications on ontology developers and practitioners. YAMO will allow ontologists to construct a very deep, high-quality and large-scale ontology.

This paper illustrates a brand new ontology development methodology and demonstrates how the methodology can be applied to build a large-scale high-quality domain ontology.

This paper applies the knowledge-based genetic algorithm to solve the optimization problem in complex products technological processes.

The knowledge-based genetic algorithm (KGA) is defined as a hybrid genetic algorithm (GA) which combined the GA model with the knowledge model. The GA model searches the feasible space of optimization problem based on the “neighborhood search” mechanism. The knowledge model discovers some knowledge from the previous optimization process, and applies the obtained knowledge to guide the subsequent optimization process.

The experimental results suggest that the proposed KGA is feasible and available. The effective integration of GA model and knowledge model has greatly improved the optimization performance of KGA.

The technological innovation of complex products is one of effective approaches to establish the core competitiveness in future. For this reason, the KGA is proposed to the technological processes optimization of complex products.

The purpose of this paper is to discuss the various issues involved in Indian languages computing, particularly Telugu, like creating, displaying, searching and retrieving digital content. The paper also aims to emphasize the issues involved in retrieval in Indian languages. The complexities presented by the grammar, syntax and morphology of Indian languages are discussed.

The paper undertakes and presents descriptive study of the issues and challenges in Indian languages computing in general and Telugu language in particular.

The problem of multilingual information retrieval in Indian languages is multi‐pronged. A major observation of this study is that, though digital content is available in Indian languages, it is mostly in non‐standard encoding format and fonts. There is an urgent need to work in the area of developing search algorithms for Indian languages, like soundex and metaphones to tolerate spelling variations and mistakes that a user might make in queries and suggest correct spelling(s).

With existing technologies libraries can now build online catalogues in the language of the documents or build digital repositories with content in various Indian languages. Though a few library automation software like NewGenLib and digital library software like DSpace, etc. are offering Unicode support for Indian languages, they do not allow for different types of search such as truncation search, word variants, etc. The present study is a step towards developing algorithms for indexing and searching in Indian languages.

The paper addresses various issues in Indian language computing with emphasis on search and retrieval.

This paper aims to propose an entity-based scientific metadata schema, i.e. Scientific Knowledge Object (SKO) Types. During the past 50 years, many metadata schemas have been developed in a variety of disciplines. However, current scientific metadata schemas focus on describing data, but not entities. They are descriptive, but few of them are structural and administrative.

To describe entities in scientific knowledge, the theory of SKO Types is proposed. SKO Types is an entity-based theory for representing and linking SKOs. It defines entities, relationships between entities and attributes of each entity in the scientific domain.

In scientific knowledge management, SKO Types serves as the basis for relating entities, entity components, aggregated entities, relationships and attributes to various tasks, e.g. linked entity, rhetorical structuring, strategic reading, semantic annotating, etc., that users may perform when consulting ubiquitous SKOs.

SKO Types can be widely applied in various digital libraries and scientific knowledge management systems, while for the existing legacy of scientific publications and their associated metadata schemas.

The purpose of this paper is to discuss the methodology in building an integrated domain information system with illustrations that provide proof of concept

The present work studies the usual search engine approach to information and its pitfalls. A methodology was adopted for construction of a domain‐based information system, known as Aerospace Information System (AERIS), comprising six distinct steps in identifying and sourcing, evaluating and then technically integrating resources into the information system. AERIS is an integrated gateway for resources in the domain of aerospace science and technology. AERIS is designed to provide information from varied sources such as formal publications (e.g. articles), aggregators (e.g. harvesters) and also informal resources such as blogs and discussion fora. Interaction is provided through a simple user interface.

The domain‐based information system with focussed collection and services serves patrons with more precision than general web search engines.

At present the AERIS system is populated with a limited number of resources. A fully‐fledged system may be developed based on the same model.

This original research work provides a model for a comprehensive integrated gateway to domain‐based information using open‐source tools.

This paper aims to share the experiences gained while developing an electronically accessible personalized and categorized current awareness service (CAS) for the Fellows of the Wissenschaftskolleg zu Berlin – Institute for Advanced Study. The paper also aims to communicate the design and technology behind the development of this service.

A list of journals of interest was compiled. The respective RSS feed links were handpicked from journal web pages. The service was configured to automatically fetch and classify the tables of contents (ToCs) according to the Fellows' interest. The same was presented in a user‐friendly interface.

RSS feeds from electronic journals can be used as a CAS tool to serve frequently updated information to the desk of the users.

The paper can be used as a starting point by practicing librarians to start similar services in small special libraries to strengthen the library's publicity and outreach activities.

This paper demonstrates how personalized current content service can be provided using open source tools.

The purpose of this paper is to propose the use of unified modelling language (UML) based ontologies to develop a faceted model to facilitate semantic browsing and searching in digital libraries.

The approach is to understand the way information gets logically organised in the human mind in terms of mutually defined concepts. Ontologies will be used to model the semantic structure of individual information sources using the faceted classification schema of Dr S.R. Ranganathan, though with improvisation to accommodate new concepts relating to digital resources.

UML can be the best option for knowledge representation if the target system is a digital library where the required type of reasoning about ontologies is more in terms of answering specific and specialised questions. Here, ontologies can be used to facilitate a distributed information retrieval system.

The proposed model will not only facilitate semantic searching using UML concept maps but will also enable domain experts, even those with little coding expertise, to build ontologies for their resources while submitting their documents to the digital library.

This paper aims to study the key success factors (KSFs) that determine the direction and context of a new university, Suan Dusit Rajabhat University (SDU), to formulate strategic human capital management (SHCM) for the university, and also to recommend a proposal for the human resources (HR) structure and systems that supports SHCM for a new university.

This study used mixed methods. There were four steps, including documentary research to develop a draft of SHCM prototype, in-depth interview and knowledge-sharing technique with 17 key informants to develop the underlying final SHCM prototype, collecting the quantitative data from a questionnaire to develop a prototype of SHCM, and validation and confirmation of the suitability and feasibility of SHCM for a new university by using a focus group and knowledge-sharing technique with 14 HR experts and re-confirm for practical implementation with SDU’s executive team.

The four KSFs were university positioning, talent capability, harmonization, and transformation. The SHCM formulation was categorized into two sections: components including strategy on thinking and planning, implementation and measurement; and procedures including HR policy committee, strategic and operational HR management. The HR proposal for implementation was emerging.

The tacit knowledge in SHCM, including human capital-centric driving for KSFs and innovative HR in university transformation comprising of the strategic and operational levels, was revealed.