CanFind-a semantic image indexing and retrieval system

[[abstract]]We present CanFind, a semantic image indexing and retrieval system in this paper. To identify the target images of interest in the database in the conceptual level, the presented system makes use of keywords as the input of searching vehicle. The system consists of two subsystems, i.e., semantic indexing and query expansion. In the semantic indexing, the subsystem includes three main building blocks, namely, keyword extraction, keyword expansion, and keyword weighting. The information of WordNet is used to extend existing keywords associated with images. This design intends to overcome the drawbacks in conventional keyword-based image retrieval system. Next, the resulting word set is filtered by a filter to extract common words from the word set and set up the image indexing for the corresponding image. In the query expansion, corpus is used to help users find relative or precise results in the facing dilemma of too few or too many query results for a given query. The designed semantic image indexing and retrieval system is integrated with IWiLL, a web-based language learning platform to further illustrate the value of the designed system.[[conferencetype]]國際[[conferencedate]]20030525~20030528[[booktype]]紙本[[conferencelocation]]Bangkok, Thailan

Image indexing and retrieval: some problems and proposed solutions.

Image processing technologies are offering considerable potential for library and information units to extend their databases by the inclusion of images such as photographs, paintings, monograph title-pages and maps. Discusses problems and potential solutions in a structured fashion based on categories of thesauri (text and visual), hybrids, description language and automatic content analysis, with state-of-the-art examples

The best of both worlds: highlighting the synergies of combining manual and automatic knowledge organization methods to improve information search and discovery.

Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have. In response, many organizations have deployed some form of enterprise search to improve the 'findability' of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a knowledge organization system (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. The oil and gas industry was used as a case study using a representative organization. Drawing on prior research, a theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help to re-conceptualize the 'manual' versus 'automatic' debate in many enterprises, accommodating a broader range of information needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what arc often perceived as mutually exclusive competing approaches. Certain aspects of the theoretical model may be transferable to other industries, which is an area for further research

Review of Indexing Techniques Applied in Information Retrieval

Indexing is one of the important tasks of Information Retrieval that can be applied to any form of data, generated from the web, databases, etc. As the size of corpora increases, indexing becomes too time consuming and labor intensive, therefore, the introduction of computer aided indexer. A review of indexing techniques, both human and automatic indexing has been done in this paper. This paper gives an outline of the use of automatic indexing by discussing various hashing techniques including fuzzy finger printing and locality-sensitive hashing. Two different processes of matching that are used in automatic subject indexing are also reviewed. Accepting the need of automatic indexing in a possible replacement to manual indexing, studies in the development of automatic indexing tools must continu

Subject-based knowledge organisation: an OER for supporting (digital) humanities research

Humanities scholars can today engage in research inquiry using data from a range of varied collections which are often characterised by poor subject access, often resulting in systems that underperform and even effectively prevent access to data, information and knowledge. In spite of the availability of professional standards and guidelines to provide quality-controlled subject access through knowledge organisation systems (KOS), subject access in such collections is rarely based on KOS. At the same time, KOS themselves may come with problems such as being slow to update, being rigidly structured and not incorporating end-users' vocabulary. It may therefore be useful to consider methods for remediating these deficiencies in KOSs, such as collecting user-generated metadata via social tagging or complementing automated indexing techniques with manual ones. To help address the above problems, the paper discusses these challenges and points to possible solutions in different contexts. It does so by reflecting on an open educational resource (OER) devoted to this theme, titled Introduction to Knowledge Organisation Systems for Digital Humanities. It was developed as part of an EU project called DiMPAH (Digital Methods Platform for the Arts and Humanities), 2021-2023, creating seven OERs for inclusion in DARIAH Teach

Information Retrieval Methods in Libraries and Information Centers

The volumes of information created, generated and stored are immense thatwithout adequate knowledge of information retrieval methods, the retrievalprocess for an information user would be cumbersome and frustrating.Studies have further revealed that information retrieval methods are essentialin information centers for storage and retrieval of information. The paperdiscusses the concept of Information retrieval, the various informationretrieval methods. It examines the users of these information methods andtheir information behavior. The conclusion emphasizes the need for acontinuous evaluation of the information retrieval methods to make for andeffective and efficient information retrieval syste

Transforming Thesaurus Records into MARC 21 and MADS: Designing a Framework for Libraries

Purpose This paper analyzes various thesaurus formats for converting data and how they can easily be implemented in libraries. These data formats are very important and necessary because they can easily transfer data from one system to another. The main focus of this system is on the data format of the Thesaurus Constructon. Methodology It is made with the TemaTres tool, which is used by many other tools. It has many new and modern features that librarians can use to create a new interface. In other words, it is possible to link other software very easily through these formats. There are four main steps to follow to build this system such as (i) Study the Thesaurus Subject Repositories; (ii) Comparative Study of Controlled Vocabulary Tools; (iii) Construction of Controlled Vocabularies; (iv) Creation of Formats for Thesaurus. Findings Users will benefit a lot from using this interface as they will be able to access all the information they need very easily. In addition, two of these formats, MARC21 and MADS, can be imported into Koha, allowing users to access additional information from Koha\u27s OPAC interface that is located within TemaTres. Originality With these concepts, thesaurus of any subject can be created and data linking between other software can be done. It is possible to publish any types of linked data formats with the help of Apache Jena and Apache Jena Fuseki to external integration for easy access of metadata. Therefore a prototype vocabulary can be created through this system from which all libraries can benefit