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Semantic-Based Navigation in Open Spaces 297<br />

Several solutions provided support for link generation though this was limited to<br />

links between instances related via predefined properties or clusters. None of the examined<br />

solutions apart from our faceted browser Factic supported personalization, although<br />

there was limited support for content-based adaptation – linking to related instances<br />

and sorting based on statistical metrics.<br />

The most used visualization approach seems to be textual visualization in tables. No<br />

solution employed any kind of advanced visual/graph-based presentation approach for<br />

navigation, or for actual content though /facet employed timeline visualization to show<br />

temporal distribution of instances.<br />

Table 10-1. Overview of navigation solutions.<br />

Factic<br />

(faceted<br />

browser)<br />

OntoViews<br />

(faceted<br />

browser)<br />

BrowseRDF<br />

(faceted<br />

browser)<br />

/facet<br />

(faceted<br />

browser)<br />

IGroup<br />

(keywordbased,<br />

query-byexample)<br />

Flamenco<br />

(faceted<br />

browser)<br />

RB++<br />

(view-based<br />

search)<br />

Semantic<br />

support<br />

OWL<br />

RDFS<br />

RDF<br />

Link<br />

generation<br />

Yes<br />

Yes, related<br />

instances<br />

Yes<br />

Adaptivity Personalization Visualization<br />

Adaptation,<br />

annotation and<br />

recommendation<br />

of facets, restrictions,<br />

results<br />

Related instances<br />

Statistical facet<br />

ranking<br />

Yes, automatic<br />

user modeling<br />

No<br />

No<br />

RDFS Yes No No<br />

No<br />

Yes, clusters<br />

No<br />

No<br />

No Yes No No<br />

No No No No<br />

Text/images<br />

and tables<br />

Text/images<br />

and tables<br />

Test and<br />

tables<br />

Text/images<br />

and tables,<br />

timeline<br />

Text/images<br />

and tables<br />

Text/images<br />

and tables<br />

Text and<br />

tables<br />

10.8 Looking Ahead<br />

We identified several problems in the previous sections, such as the size and dynamics of<br />

open information spaces, which current information retrieval systems must deal with.<br />

While existing search engines are definitely not ideal, they are still pretty good at what<br />

they do – gathering metadata and building indices. Although, there is steady progress in<br />

keyword-based search engine improvement, we believe that this area is not interesting<br />

enough to warrant further attention and thus leave it to others. Furthermore, additional<br />

approaches already exist that can classify documents (web pages) and create metadata<br />

where they are unavailable (albeit they are not used much in practice and of somewhat<br />

questionable quality).

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