Ana María Muñoz

A model of fuzzy linguistic IRS based on multi-granular linguistic information

An important question in IRSs is how to facilitate the IRS-user interaction, even more when the complexity of the fuzzy query language makes difficult to formulate user queries. The use of linguistic variables to represent the input and output information in the retrieval process of IRSs significantly improves the IRS-user interaction. In the activity of an IRS, there are aspects of different nature to be assessed, e.g., the relevance of documents, the importance of query terms, etc. Therefore, these aspects should be assessed with different uncertainty degrees, i.e., using several label sets with different granularity of uncertainty. In this contribution, an IRS based on fuzzy multi-granular linguistic information and a method to process the multi-granular linguistic information are proposed. The system accepts Boolean queries whose terms can be simultaneously weighted by means of ordinal linguistic values according to three semantics: a symmetrical threshold semantics, a relative importance semantics and a quantitative semantics. In the three semantics, the linguistic weights are represented by the linguistic variable ‘‘Importance’’, but assessed on different label sets S 1 , S 2 and S 3 , respectively. The IRS evaluates weighted queries and obtains the linguistic retrieval status values of documents represented by the linguistic variable ‘‘Relevance’’ which is expressed on a different label set S 0 .

Sitios webs de centros universitarios de estudios de las mujeres en españa: selección y evaluación 10.5007/1518-2924.2006v11nesp2p49

After a brief introduction about the level of development of Women’s Studies in Spain and a list of their parent university institutions, Women’s Studies Web sites are assessed by measuring their usability. The aim is to improve use and accessibility of existing Web sites and, above all, provide encouragement and a model to follow by future Web sites in this area.