By Hai Zhuge
The writer, an the world over pointed out specialist within the wisdom grid box, introduces the source house version (RSM) that will help you successfully manage and deal with assets by way of normalizing category semantics. After atmosphere forth easy types of RSM and the Semantic hyperlink community, the writer establishes the connection among the 2 versions and units forth an method of integrating the 2 and exploring their semantic wealthy interconnections.
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Extra resources for The Web Resource Space Model (Web Information Systems Engineering and Internet Technologies Book Series)
The Resource Space Model does not have this limitation. Relational table raises its search efficiency by establishing one- dimensional index on attributes. The resource space uses one multidimensional index on the whole space. This multi-dimensional nature requires special storage mechanism different from relational database. As a descrete multi-dimensional index, the resource space has advantages in search efficiency. Complex resources are properties if they are correctly stored. The existing resources bring users’ classification viewpoint so they can be used to enrich the semantics of axes, coordinates and even points.
Answer 3. The Resource Space Model is good at managing the content that can be clearly classified. Designers can design resource spaces of different normal forms according to different applications. The following approaches can be used to deal with this issue: 1. add undetermined coordinates to specify those un-determined resources; 2. add a cross-class coordinate to appropriate axis; 3. introduce fuzzy theory to establish a fuzzy Resource Space Model as introduced in (Zhuge, 2004c); and, 4. introduce probability into Resource Space Model to reflect the probability world (see chapter 9).
Extract the keywords from the new paper and put them into set KI representing the internal features. Obtain the external features about the new paper such as journal name, publisher, publishing date and impact factor, and put them into set KE. The main external features are usually available from the metadata of journals. Unite the external features and the internal features on the new paper K=KI∪KE. , share a certain number of keywords in the meaning of domain ontology), the paper is likely to match the point and therefore put the new paper into the point, otherwise put it into the candidate pool awaiting additional techniques.