Search (3 results, page 1 of 1)

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Abstract

The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless, if we limit our attention only to lookup search we are missing another important search task. In exploratory search, the user is willing not only to find documents relevant with respect to her query but she is also interested in learning, discovering and understanding novel knowledge on complex and sometimes unknown topics. In the paper we address this issue presenting LED, a web based system that aims to improve (lookup) Web search by enabling users to properly explore knowledge associated to her query. We rely on DBpedia to explore the semantics of keywords within the query thus suggesting potentially interesting related topics/keywords to the user.

Theme

Semantic Web

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Abstract

Thanks to the recent Linked Data initiative, the foundations of the Semantic Web have been built. Shared, open and linked RDF datasets give us the possibility to exploit both the strong theoretical results and the robust technologies and tools developed since the seminal paper in the Semantic Web appeared in 2001. In a simplistic way, we may think at the Semantic Web as a ultra large distributed database we can query to get information coming from different sources. In fact, every dataset exposes a SPARQL endpoint to make the data accessible through exact queries. If we know the URI of the famous actress Nicole Kidman in DBpedia we may retrieve all the movies she acted with a simple SPARQL query. Eventually we may aggregate this information with users ratings and genres from IMDB. Even though these are very exciting results and applications, there is much more behind the curtains. Datasets come with the description of their schema structured in an ontological way. Resources refer to classes which are in turn organized in well structured and rich ontologies. Exploiting also this further feature we go beyond the notion of a distributed database and we can refer to the Semantic Web as a distributed knowledge base. If in our knowledge base we have that Paris is located in France (ontological level) and that Moulin Rouge! is set in Paris (data level) we may query the Semantic Web (interpreted as a set of interconnected datasets and related ontologies) to return all the movies starred by Nicole Kidman set in France and Moulin Rouge! will be in the final result set. The ontological level makes possible to infer new relations among data.
The Linked Data initiative and the state of the art in semantic technologies led off all brand new search and mash-up applications. The basic idea is to have smarter lookup services for a huge, distributed and social knowledge base. All these applications catch and (re)propose, under a semantic data perspective, the view of the classical Web as a distributed collection of documents to retrieve. The interlinked nature of the Web, and consequently of the Semantic Web, is exploited (just) to collect and aggregate data coming from different sources. Of course, this is a big step forward in search and Web technologies, but if we limit our investi- gation to retrieval tasks, we miss another important feature of the current Web: browsing and in particular exploratory browsing (a.k.a. exploratory search). Thanks to its hyperlinked nature, the Web defined a new way of browsing documents and knowledge: selection by lookup, navigation and trial-and-error tactics were, and still are, exploited by users to search for relevant information satisfying some initial requirements. The basic assumptions behind a lookup search, typical of Information Retrieval (IR) systems, are no more valid in an exploratory browsing context. An IR system, such as a search engine, assumes that: the user has a clear picture of what she is looking for ; she knows the terminology of the specific knowledge space. On the other side, as argued in, the main challenges in exploratory search can be summarized as: support querying and rapid query refinement; other facets and metadata-based result filtering; leverage search context; support learning and understanding; other visualization to support insight/decision making; facilitate collaboration. In Section 3 we will show two applications for exploratory search in the Semantic Web addressing some of the above challenges.

Theme

Semantic Web

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Abstract

Peter and Alice are at home, it is a calm winter night, snow is falling, and it is too cold to go outside. "Why don't we just order a pizza and watch a movie?" says Alice wrapped in her favorite blanket. "Why not?"-Peter replies-"Which movie do you wanna watch?" "Well, what about some comedy, romance-like one? Com'on Pete, look on Facebook, there is that nice application Kara suggested me some days ago!" answers Alice. "Oh yes, MORE, here we go, tell me a movie you like a lot," says Peter excited. "Uhm, I wanna see something like the Bridget Jones's Diary or Four Weddings and a Funeral, humour, romance, good actors..." replies his beloved, rubbing her hands. Peter is a bit concerned, he is more into fantasy genre, but he wants to please Alice, so he looks on MORE for movies similar to the Bridget Jones's Diary and Four Weddings and a Funeral: "Here we are my dear, MORE suggests the sequel or, if you prefer, Love Actually," I would prefer the second." "Great! Let's rent it!" nods Peter in agreement. The scenario just presented highlights an interesting and useful feature of a modern Web application. There are tasks where the users look for items similar to the ones they already know. Hence, we need systems that recommend items based on user preferences. In other words, systems should allow an easy and friendly exploration of the information/data related to a particular domain of interest. Such characteristics are well known in the literature and in common applications such as recommender systems. Nevertheless, new challenges in this field arise whenthe information used by these systems exploits the huge amount of interlinked data coming from the Semantic Web. In this chapter, we present MORE, a system for 'movie recommendation' in the Web of Data.

Source

Semantic search over the Web. Eds.: R. De Virgilio, et al

Theme

Semantic Web