Search (2 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

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