Improving self-organising information maps as navigational tools: A semantic approach

Purpose - The goal of the research is to explore whether the use of higher-level semantic features can help us to build better self-organising map (SOM) representation as measured from a human-centred perspective. The authors also explore an automatic evaluation method that utilises human expert knowledge encapsulated in the structure of traditional textbooks to determine map representation quality. Design/methodology/approach - Two types of document representations involving semantic features have been explored - i.e. using only one individual semantic feature, and mixing a semantic feature with keywords. Experiments were conducted to investigate the impact of semantic representation quality on the map. The experiments were performed on data collections from a single book corpus and a multiple book corpus. Findings - Combining keywords with certain semantic features achieves significant improvement of representation quality over the keywords-only approach in a relatively homogeneous single book corpus. Changing the ratios in combining different features also affects the performance. While semantic mixtures can work well in a single book corpus, they lose their advantages over keywords in the multiple book corpus. This raises a concern about whether the semantic representations in the multiple book corpus are homogeneous and coherent enough for applying semantic features. The terminology issue among textbooks affects the ability of the SOM to generate a high quality map for heterogeneous collections. Originality/value - The authors explored the use of higher-level document representation features for the development of better quality SOM. In addition the authors have piloted a specific method for evaluating the SOM quality based on the organisation of information content in the map. © 2011 Emerald Group Publishing Limited

Towards improving WEBSOM with multi-word expressions

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaLarge quantities of free-text documents are usually rich in information and covers several topics. However, since their dimension is very large, searching and filtering data is an exhaustive task. A large text collection covers a set of topics where each topic is affiliated to a group of documents. This thesis presents a method for building a document map about the core contents covered in the collection. WEBSOM is an approach that combines document encoding methods and Self-Organising Maps (SOM) to generate a document map. However, this methodology has a weakness in the document encoding method because it uses single words to characterise documents. Single words tend to be ambiguous and semantically vague, so some documents can be incorrectly related. This thesis proposes a new document encoding method to improve the WEBSOM approach by using multi word expressions (MWEs) to describe documents. Previous research and ongoing experiments encourage us to use MWEs to characterise documents because these are semantically more accurate than single words and more descriptive

Organizing learning materials through hierarchical topic maps: an illustration through Chinese herb medication

[[abstract]]This research aims to use hierarchical topic maps to compile digital learning material and to discuss its design and application possibilities. The system renders tremendous original assets and then embeds a self-organizing map (SOM) in the material database to produce topical learning materials, as in this case, an illustration through Chinese herb medication. It helps to demonstrate robust professional information as well as knowledge structures, and provides a customized and interactive learning dynamic to support both progressive and constructive learning styles. The paper first gives a detailed procedural description of the material construction, explains how topic map techniques were applied, and observes the implications and potentials of the technology to education. Both the technical and educational evaluations of using SOM topic maps in compilation of learning materials have resulted in positive feedback. SOM allows users to review the complete databank in structural hierarchical order, which provides comprehensive understanding of the entire set of learning materials, and also brings opportunities to users to discover knowledge related to their study area.[[notice]]補正完