-We present Squiggle, a Semantic Web framework that eases the deployment of semantic search engines. Search engines are becoming such an easy way to find textual resources that we wish to use them also for multimedia content; however, syntactic techniques, even if promising, are not up to the task. With Squiggle we prove that Semantic Web technologies provide real benefits to end users in terms of an easier and more effective access to information. The effectiveness of our approach is fully demonstrated by real-world deployments available on the web.
Swhen we need to find something. However, finding what
EARCHING everything everywhere is becoming our habit
we need is often a hard job. Current search engine technology
is very good in finding complete Web pages, but it lacks the
desired precision1 and recall2 when searching for multimedia
resources. For instance, searching “jaguar” in an image search
engine results in a mix of felines and cars, which are difficult
to tell apart. Squiggle [
The interaction of a final user with Squiggle is intuitive and very similar to the use of a traditional search engine; however, the results are better and more meaningful. In the following we provide examples of searches with Squiggle, explaining how it works.
2 Recall is the proportion of retrieved relevant data, out of all available relevant data.
The user is presented with a simple search form in which he
can insert some keywords. Squiggle, like syntactic search
engines, can immediately retrieve all results containing those
keywords, since part of Squiggle is based on the well known
search engine library Lucene3 [
The results of the previous disambiguation phase are displayed, together with the syntactic results, in a lateral box under the heading “Did you mean...?”. Therefore, the user can manually choose the meaning of his query among the proposed ones. In response, he obtains more precise and numerous results; this is possible because, during the contents indexing, Squiggle is able to semantically annotate those resources with regards to the domain ontology. In the previous example, during the conceptual indexing phase, all the images whose syntactic annotations contained both Hermann Maier complete name (the concept’s skos:prefLabel) and his nickname “herminator” (represented with a skos:altLabel relation) were annotated with his concept URI.
But there’s more: accessing the domain ontology, Squiggle
is able to exploit all its content, i.e. not only the alternative
labels of its concepts, but also their relations. This capability
lets Squiggle expand the user query, by following the
relations between the concepts identified in it and other
ontological elements, and propose to the user possible
searches of his interest. For example, a fan of the Queen band,
looking for audio files of their songs, could be presented with
a lateral box suggesting an expansion of his query to include
results related to Freddy Mercury (who could be in relation
with the Queen band by a skos:relatedPartOf property in an
ontology of the music domain). This meaning suggestion, as
well as the disambiguation phase described before, is possible
because Squiggle accesses a semantic repository built on
Sesame4 [
To prove our approach, we built two different domainspecific search engines with the Squiggle framework. The following sections explain some of their distinctive characteristics.
CEFRIEL, as Official Supplier of Applied Academic Research for Torino 2006 Olympic Winter Games5, caught the opportunity to demonstrate Squiggle by developing Squiggle Ski, which helps the international public in finding images of the athletes involved in the alpine skiing races.
In order to instantiate Squiggle Ski, we built a SKOS based domain ontology partially by hand, developing a small multilingual taxonomy of the disciplines in the sectors of alpine skiing, and partially by collecting information on the FIS-Ski web site6. Then we built an experimental focused crawler that exploits the knowledge in the ski ontology to collect images of skiers from sport news Web sites all over the world. The awareness about all relevant terms (names of athletes, disciplines, places, etc. with possible alternative labels in different languages) helps both the focused crawler to filter the appropriate photos and the conceptual indexer to semantically annotate them before the indexing process.
Squiggle Ski is freely available at http://squiggle.cefriel.it/ski.
Squiggle Music is an instantiation of Squiggle framework that indexes audio files (mainly mp3 files) enriching them with information about artists, song titles and music genres.
We created a SKOS-based ontology by merging two freely
available meta-databases: MusicMoz [
Squiggle Music is on-line at http://squiggle.cefriel.it/music.
We enlightened how the employment of Semantic Web technologies to the development of search engines provides real benefits to end users, enabling an easier and more effective access to information; a semantic search engine like Squiggle appears to be more usable, in that users are supported with semantic “suggestions”, as our test-beds demonstrate at a glance.
Moreover, we designed Squiggle keeping in mind the particular needs of searching when dealing with multimedia contents: the extensible nature of Squiggle fully enables the joint employment of smart machines to process media and domain ontologies.