Semantic search

Guha, R.; McCool, Rob; Miller, Eric J.

doi:10.1145/775248.775250

Cited by 194 publications

(258 citation statements)

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“…It uses IR engine to index the crawled semantic web documents using either n-gram or URIrefs as terms and computes a ranked list of these documents given the search terms. [1] augments keyword search with semantic information collected from different sources while [2] uses keyword search results as a seed to do spread activation on semantic networks. These work takes a pure IR approach and does not support structured queries on the semantic web data.…”

Section: Related Workmentioning

confidence: 99%

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

et al. 2007

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Abstract. As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we breifly describe how Semplore is used for searching Wikipedia and an IBM customer's product information.

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Section: Related Workmentioning

confidence: 99%

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

et al. 2007

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“…We are testing our techniques on a corpus of documents from the CNN web site, 5 comprising 145,316 documents (445 MB). The domain ontology KB was taken from the KIM Platform [8], developed by Ontotext Lab, 6 with minor adjustments, plus the…”

Section: Early Experimentsmentioning

confidence: 99%

“…Ontologies achieve a reduction of ambiguity, and bring powerful inferencing schemes for reasoning and querying. Not surprisingly, there is a growing body of literature in the last few years that studies the use of ontologies to improve the effectiveness of information retrieval [5,8,10,11] and personalized search [4]. In this paper, we present a comprehensive personalized retrieval framework where the advantages of ontologies are exploited in different parts of the retrieval cycle: query-based relevance measures, semantic user preference representation, automatic preference update, and personalized result ranking.…”

Section: Introductionmentioning

confidence: 99%

Self-tuning Personalized Information Retrieval in an Ontology-Based Framework

Castells

Fernández

Vallet

et al. 2005

On the Move to Meaningful Internet Systems 2005: OTM 2005 Workshops

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“…In [4], a decomposition of PageRank to basic components is suggested that may be able to scale the different PageRank computations to a bigger number of topics or even distinct users. Another approach to web search is presented in [2], where a rich extension of the web, called semantic web, and the application of searching over this new setting is described.In this work we depart from the above lines of research and propose a new conceptual framework for representing the web and potentially improving search results. In particular, the new framework views the web as a collection of webrelated data collected and semi-organized by individual users inside their information spaces.…”

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Searching the Web Through User Information Spaces

Papagelis

Zaroliagis

2005

Lecture Notes in Computer Science

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During the last years web search engines have moved from the simple but inefficient syntactical analysis (first generation) to the more robust and usable web graph analysis (second generation). Much of the current research is focussed on the so-called third generation search engines that, in principle, inject "human characteristics" on how results are obtained and presented to the end user. Approaches exploited towards this direction include (among others): an alteration of PageRank [1] that takes into account user specific characteristics and bias the page ordering using the user preferences (an approach, though, that does not scale well with the number of users). The approach is further exploited in [3], where several PageRanks are computed for a given number of distinct search topics. A similar idea is used in [6], where the PageRank computation takes into account the content of the pages and the query terms the surfer is looking for. In [4], a decomposition of PageRank to basic components is suggested that may be able to scale the different PageRank computations to a bigger number of topics or even distinct users. Another approach to web search is presented in [2], where a rich extension of the web, called semantic web, and the application of searching over this new setting is described.In this work we depart from the above lines of research and propose a new conceptual framework for representing the web and potentially improving search results. In particular, the new framework views the web as a collection of webrelated data collected and semi-organized by individual users inside their information spaces. These data can be explicitly collected (e.g., bookmarks) or implicitly collected (e.g., web-browsing history). Our approach is based on the observation that users act as small crawlers seeking information on the web using various media (search engines, catalogs, word-of-mouth, hyperlinks, direct URL typing, etc). They tend to store and organize important-for-them pages in tree-like structures, referred to as bookmark collections, where the folder names act as tags over the collected URLs. This method of organizing data helps people to recall collected URLs faster, but can also be used as a kind of semantic tagging over the URLs (the path to the URL can be perceived as different ways to communicate the URL itself). This information constitutes part of the user's personal information space and it is indicative of his interests. One might argue that people do not collect bookmarks or that they do not organize them in any reasonable manner. However, as our experiments show, people indeed collect and organize bookmarks under certain patterns that follow power law distributions.

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Semantic search

Cited by 194 publications

References 0 publications

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

Self-tuning Personalized Information Retrieval in an Ontology-Based Framework

Searching the Web Through User Information Spaces

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