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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.

Matter is an important topic of science as a discipline since its inception. Nevertheless, along with the evolution of semantic web, matter has got equal importance among the ontology developers. The current work describes an ontology of matter that the authors developed in the lab. The purpose of this paper is to come up with an exhaustive list of concepts and relations to cover matter domain under one umbrella, after identifying the gaps in the present ontologies.

Ontology was developed following faceted analytico-synthetic approach of knowledge organization. The authors followed hybrid developmental approach which includes top-down as well as bottom-up development strategy, for creating classes and subclasses. The authors modelled matter domain comprehensively considering different aspects of matter. The theories behind the modelling approach helps to maintain the consistency of further extensions.

Final ontology has around 280 concepts and as many as 60 properties which include both object property and datatype property.

There exists very vague definition of concepts in different subject areas, as matter is a domain of study in physics, chemistry, material science, metallurgy, etc. Same material has been adopted differently depending upon purpose of its study/use in that field. For example aspirin is simply a chemical compound in chemistry, whereas in medicine it is also an agent.

Present work claims to influence the ontology engineers to develop more extension to this core ontology of matter (COMAT). Also this will find its use in information retrieval, semantic annotations and in several other semantic knowledge-based systems.

COMAT is the most recent work of the domain. Originality lies in the way matter domain has been looked up, from a very wide perspective, as well as in the approach of modelling the domain.

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 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.

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.

The purpose of this paper is to describe a novel approach to the development and semantic enhancement of a social network to support the analysis and interpretation of digital oral history data from jazz archives and special collections.

A multi-method approach was applied including automated named entity recognition and extraction to create a social network, and crowdsourcing techniques to semantically enhance the data through the classification of relations and the integration of contextual information. Linked open data standards provided the knowledge representation technique for the data set underlying the network.

The study described here identifies the challenges and opportunities of a combination of a machine and a human-driven approach to the development of social networks from textual documents. The creation, visualization and enrichment of a social network are presented within a real-world scenario. The data set from which the network is based is accessible via an application programming interface and, thus, shareable with the knowledge management community for reuse and mash-ups.

This paper presents original methods to address the issue of detecting and representing semantic relationships from text. Another element of novelty is in that it applies semantic web technologies to the construction and enhancement of the network and underlying data set, making the data readable across platforms and linkable with external data sets. This approach has the potential to make social networks dynamic and open to integration with external data sources.