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

Improving Retrieval Experience Exploiting Semantic Representation of Documents

Abstract. The traditional strategy performed by Information Retrieval (IR) systems is ranked keyword search: for a given query, a list of documents, ordered by relevance, is returned. Relevance computation is primarily driven by a basic string-matching operation. To date, several attempts have been made to deviate from the traditional keyword search paradigm, often by introducing some techniques to capture word meanings in documents and queries. The general feeling is that dealing explicitly with only semantic information does not improve significantly the performance of text retrieval systems. This paper presents SENSE (SEmantic N-levels Search Engine), an IR system that tries to overcome the limitations of the ranked keyword approach, by introducing semantic levels which integrate (and not simply replace) the lexical level represented by keywords. Semantic levels provide information about word meanings, as described in a reference dictionary, and named entities. We show how SENSE is able to manage documents indexed at three separate levels