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Photographs are a kind of cultural heritage and very useful for cultural and historical studies. However, traditional or manual research methods are costly and cannot be applied on a large scale. This paper aims to present an exploratory study for understanding the cultural concerns of libraries based on the automatic analysis of large-scale image collections.

In this work, an image dataset including 85,023 images preserved and shared by 28 libraries is collected from the Flickr Commons project. Then, a method is proposed for representing the culture with a distribution of visual semantic concepts using a state-of-the-art deep learning technique and measuring the cultural concerns of image collections using two metrics. Case studies on this dataset demonstrated the great potential and promise of the method for understanding large-scale image collections from the perspective of cultural concerns.

The proposed method has the ability to discover important cultural units from large-scale image collections. The proposed two metrics are able to quantify the cultural concerns of libraries from different perspectives.

To the best of the authors’ knowledge, this is the first automatic analysis of images for the purpose of understanding cultural concerns of libraries. The significance of this study mainly consists in the proposed method of understanding the cultural concerns of libraries based on the automatic analysis of the visual semantic concepts in image collections. Moreover, this paper has examined the cultural concerns (e.g. important cultural units, cultural focus, trends and volatility of cultural concerns) of 28 libraries.

The goal of this paper is to re‐evaluate the role of knowledge‐based systems (KBS) in knowledge management (KM). While knowledge‐based systems and expert systems were widely used in the past, they have now fallen from favor and are largely ignored in the knowledge management literature. This paper aims to argue that several factors have changed and it is now time to re‐evaluate the contribution that such systems can make to knowledge management.

The role of KBS in KM is explored through a comprehensive analysis of both the management and the technical literature on knowledge. The literature on KBS and expert systems is reviewed and some of the problems faced by them are highlighted. Some of the probable causes of these problems and some of the solutions that might be used to overcome them are indicated. The paper describes how knowledge systems (KS) could be used as an effective tool for managing knowledge.

The lack of success of KBS technologies for managing knowledge is mainly due to organizational and managerial issues. These problems can be solved through feasibility studies before system development activities. KS technology is now being successfully applied in a variety of newer domains that exploit its capabilities.

Some conclusions are drawn concerning integration of knowledge systems with knowledge management, problems of the early implementation of knowledge systems technology, and possible solution to overcome these problems.

The main contribution of the article is in re‐evaluating the role of knowledge‐based systems as a tool for knowledge management.

The purpose of this study is to reduce the semantic distance by proposing a model for integrating indexes of textual and visual features via a multi-modality ontology and the use of DBpedia to improve the comprehensiveness of the ontology to enhance semantic retrieval.

A multi-modality ontology-based approach was developed to integrate high-level concepts and low-level features, as well as integrate the ontology base with DBpedia to enrich the knowledge resource. A complete ontology model was also developed to represent the domain of sport news, with image caption keywords and image features. Precision and recall were used as metrics to evaluate the effectiveness of the multi-modality approach, and the outputs were compared with those obtained using a single-modality approach (i.e. textual ontology and visual ontology).

The results based on ten queries show a superior performance of the multi-modality ontology-based IMR system integrated with DBpedia in retrieving correct images in accordance with user queries. The system achieved 100 per cent precision for six of the queries and greater than 80 per cent precision for the other four queries. The text-based system only achieved 100 per cent precision for one query; all other queries yielded precision rates less than 0.500.

This study only focused on BBC Sport News collection in the year 2009.

The paper includes implications for the development of ontology-based retrieval on image collection.

This study demonstrates the strength of using a multi-modality ontology integrated with DBpedia for image retrieval to overcome the deficiencies of text-based and ontology-based systems. The result validates semantic text-based with multi-modality ontology and DBpedia as a useful model to reduce the semantic distance.

The purpose of this paper is to present descriptive characteristics of the historical development of concept networks. The linguistic principles, mechanisms and motivations behind the evolution of concept networks are discussed. Implications emanating from the idea of the historical development of concept networks are discussed in relation to knowledge representation and organization schemes.

Natural language data including both speech and text are analyzed by examining discourse contexts in which a linguistic element such as a polysemy or homonym occurs. Linguistic literature on the historical development of concept networks is reviewed and analyzed.

Semantic sense relations in concept networks can be captured in a systematic and regular manner. The mechanism and impetus behind the process of concept network development suggest that semantic senses in concept networks are closely intertwined with pragmatic contexts and discourse structure. The interrelation and permeability of the semantic senses of concept networks are captured on a continuum scale based on three linguistic parameters: concrete shared semantic sense; discourse and text structure; and contextualized pragmatic information.

Research findings signify the critical need for linking discourse structure and contextualized pragmatic information to knowledge representation and organization schemes.

The idea of linguistic characteristics, principles, motivation and mechanisms underlying the evolution of concept networks provides theoretical ground for developing a model for integrating knowledge representation and organization schemes with discourse structure and contextualized pragmatic information.

With the rapid development of materials informatics and the Semantic Web, the semantic-driven solution has emerged to improve traditional query technology, which is hard to discover implicit knowledge from materials data. However, it is a nontrivial thing for materials scientists to construct a semantic query, and the query results are usually presented in RDF/XML format which is not convenient for users to understand. This paper aims to propose an approach to construct semantic query and visualize the query results for metallic materials domain.

The authors design a query builder to generate SPARQL query statements automatically based on domain ontology and query conditions inputted by users. Moreover, a semantic visualization model is defined based on the materials science tetrahedron to support the visualization of query results in an intuitive, dynamic and interactive way.

Based on the Semantic Web technology, the authors design an automatic semantic query builder to help domain experts write the normative semantic query statements quickly and simply, as well as a prototype (named MatViz) is developed to visually show query results, which could help experts discover implicit knowledge from materials data. Moreover, the experiments demonstrate that the proposed system in this paper can rapidly and effectively return visualized query results over the metallic materials data set.

This paper mainly discusses an approach to support semantic query and visualization of metallic materials data. The implementation of MatViz will be a meaningful work for the research of metal materials data integration.

It is suggested that the left hemispheric neurons and the magnocellular visual system are specialized in tasks requiring a relatively small number of large neurons having a fast reaction time due to a high firing rate or many dendritic synapses of the same neuron which are activated simultaneously. On the other hand the right hemispheric neurons and the neurons of the parvocellular visual system are specialized in tasks requiring a relatively larger number of short term memory (STM) Hebbian engrams (neural networks). This larger number of engrams is achieved by a combination of two strategies. The first is evolving a larger number of neurons, which may be smaller and have a lower firing rate. The second is evolving longer and more branching axons and thus producing more engrams, including engrams comprising neurons located at cortical areas distant from each other. This model explains why verbal functions of the brain are related to the left hemisphere, and the division of semantic tasks between the left hemisphere and the right one. This explanation is extended to other cognitive functions like visual search, ontological cognition, the detection of temporal order, and the dual cognitive interpretation of the perceived physical phenomena.

To provide a better‐informed view of the extent of the semantic gap in image retrieval, and the limited potential for bridging it offered by current semantic image retrieval techniques.

Within an ongoing project, a broad spectrum of operational image retrieval activity has been surveyed, and, from a number of collaborating institutions, a test collection assembled which comprises user requests, the images selected in response to those requests, and their associated metadata. This has provided the evidence base upon which to make informed observations on the efficacy of cutting‐edge automatic annotation techniques which seek to integrate the text‐based and content‐based image retrieval paradigms.

Evidence from the real‐world practice of image retrieval highlights the existence of a generic‐specific continuum of object identification, and the incidence of temporal, spatial, significance and abstract concept facets, manifest in textual indexing and real‐query scenarios but often having no directly visible presence in an image. These factors combine to limit the functionality of current semantic image retrieval techniques, which interpret only visible features at the generic extremity of the generic‐specific continuum.

The project is concerned with the traditional image retrieval environment in which retrieval transactions are conducted on still images which form part of managed collections. The possibilities offered by ontological support for adding functionality to automatic annotation techniques are considered.

The paper offers fresh insights into the challenge of migrating content‐based image retrieval from the laboratory to the operational environment, informed by newly‐assembled, comprehensive, live data.

This paper aims to propose a system for the semantic annotation of audio‐visual media objects, which are provided in the documentary domain. It presents the system's architecture, a manual annotation tool, an authoring tool and a search engine for the documentary experts. The paper discusses the merits of a proposed approach of evolving semantic network as the basis for the audio‐visual content description.

The author demonstrates how documentary media can be semantically annotated, and how this information can be used for the retrieval of the documentary media objects. Furthermore, the paper outlines the underlying XML schema‐based content description structures of the proposed system.

Currently, a flexible organization of documentary media content description and the related media data is required. Such an organization requires the adaptable construction in the form of a semantic network. The proposed approach provides semantic structures with the capability to change and grow, allowing an ongoing task‐specific process of inspection and interpretation of source material. The approach also provides technical memory structures (i.e. information nodes), which represent the size, duration, and technical format of the physical audio‐visual material of any media type, such as audio, video and 3D animation.

The proposed approach (architecture) is generic and facilitates the dynamic use of audio‐visual material using links, enabling the connection from multi‐layered information nodes to data on a temporal, spatial and spatial‐temporal level. It enables the semantic connection between information nodes using typed relations, thus structuring the information space on a semantic as well as syntactic level. Since the description of media content holds constant for the associated time interval, the proposed system can handle multiple content descriptions for the same media unit and also handle gaps. The results of this research will be valuable not only for documentary experts but for anyone with a need to manage dynamically audiovisual content in an intelligent way.

The purpose of this paper is to introduce an interpreting schema and semantic description framework for a collection of images of Xilankapu, a traditional Chinese form of embroidered fabric and brocade artwork.

First, the authors interpret the artwork of Xilankapu through Gillian Rose’s “four site” theory by presenting how the brocades were made, how the patterns of Xilankapu are classified and the geometrical abstraction of visual images. To further describe the images of this type of brocade, this paper presents semantic descriptions that include objective–non-objective relations and a multi-layered semantic framework. Furthermore, the authors developed corresponding methods for scanning, storage and indexing images for retrieval.

As exploratory research on describing, preserving and indexing images of Xilankapu in the context of the preservation of cultural heritage, the authors collected 1,000+ images of traditional Xilankapu, classifying and storing some of the images in a database. They developed an index schema that combines concept- and content-based approaches according to the proposed semantic description framework. They found that the framework can describe, store and preserve semantic and non-semantic information of the same image. They relate the findings of this paper to future research directions for the digital preservation of traditional cultural heritages.

The framework has been designed especially for brocade, and it needs to be extended to other types of cultural image.

The semantic description framework can describe connotative semantic information on Xilankapu. It can also assist the later information retrieval work in organizing implicit information about culturally related visual materials.

This paper presents a questionnaire study of brand‐specific perceptions of automotive design using subjective rating methods. The purpose of the paper is to explore the multiple modalities of the visual product experience of automobile design as perceived by the general public. Furthermore, the experiences were analysed using a framework for visual product experience (VPE).

Respondents were asked to assess the design of two car models at an international car show in relation to brand perceptions and visually perceived attributes using, among other tools, visual analogue scales. Analysis was done using a qualitative technique.

Results from the study indicate that there is a correlation/relation between experiential modes, in that respondents tended to rate attributes consistently high or low across modes. This implies that if the aesthetics are not perceived as favourable, neither is the expression of the car. Furthermore, respondents' assessments of aesthetic appeal and expression are on an average strikingly similar, suggesting that the level of aesthetic appeal correlates with the level of semantic understanding of the design. The general rating of emotional response follows a similar consistent pattern for the two studied cars.

Study approach as a way to gain insights into subjective perceptions of products based on appreciation and interpretation of visual product form. VPE framework recognising, mapping and clarifying the multiple modes of the visual experience.