Semantic search

Guha, Ramanathan V.; McCool, Rob; Miller, Éric

doi:10.1145/775152.775250

Cited by 489 publications

(73 citation statements)

References 5 publications

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“… Disambiguation: Semantic search goes through disambiguation, to remove any ambiguity and multiple word meanings, to return the most probable search term meaning [44] [45].…”

Section: Semantic Searchmentioning

confidence: 99%

Web Search Query Privacy, an End-User Perspective

Mivule¹

2017

JIS

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While search engines have become vital tools for searching information on the Internet, privacy issues remain a growing concern due to the technological abilities of search engines to retain user search logs. Although such capabilities might provide enhanced personalized search results, the confidentiality of user intent remains uncertain. Even with web search query obfuscation techniques, another challenge remains, namely, reusing the same obfuscation methods is problematic, given that search engines have enormous computation and storage resources for query disambiguation. A number of web search query privacy procedures involve the cooperation of the search engine, a nontrusted entity in such cases, making query obfuscation even more challenging. In this study, we provide a review on how search engines work in regards to web search queries and user intent. Secondly, this study reviews material in a manner accessible to those outside computer science with the intent to introduce knowledge of web search engines to enable non-computer scientists to approach web search query privacy innovatively. As a contribution, we identify and highlight areas open for further investigative and innovative research in regards to end-user personalized web search privacy-that is methods that can be executed on the user side without third party involvement such as, search engines. The goal is to motivate future web search obfuscation heuristics that give users control over their personal search privacy.

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“… Disambiguation: Semantic search goes through disambiguation, to remove any ambiguity and multiple word meanings, to return the most probable search term meaning [44] [45].…”

Section: Semantic Searchmentioning

confidence: 99%

Web Search Query Privacy, an End-User Perspective

Mivule¹

2017

JIS

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“…Another goal of our work is to demonstrate the use of crowdsourcing for a large-scale evaluation campaign for a novel search task, which in our case is adhoc object retrieval over RDF. Many semantic search systems of this type, such as [5,6,19], have appeared in the past few years, but none have been evaluated against each other except on a very small scale. Semantic search systems are a subset of information retrieval systems, and thus it would be natural to apply existing IR benchmarks for their evaluation in a large-scale campaign.…”

Section: Crowdsourcing-based Evaluationmentioning

confidence: 99%

“…The advantage of the crowd is that it is always available, it is accessible to most people at a relatively small cost, and the workforce scales elastically with increasing evaluation demands. Further, platforms such as Amazon Mechanical Turk 5 provide integrated frameworks for running crowdsourced tasks with minimal effort. We show how crowdsourcing can help execute an evaluation campaign for a search task that has not yet been sufficiently addressed to become part of a large evaluation effort such as TREC: ad-hoc Web object retrieval [10], for which we created a standard data set and queries for the task of object retrieval using real-world data, and the way we employed Mechanical Turk to elicit high quality judgments from the noise of unreliable workers in the crowd.…”

Section: Reliability and Repeatability Of The Evaluation Frameworkmentioning

confidence: 99%

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Repeatable and reliable semantic search evaluation

Blanco

Halpin

Herzig

et al. 2013

Journal of Web Semantics

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An increasing amount of structured data on the Web has attracted industry attention and renewed research interest in what is collectively referred to as semantic search. These solutions exploit the explicit semantics captured in structured data such as RDF for enhancing document representation and retrieval, or for finding answers by directly searching over the data. These data have been used for different tasks and a wide range of corresponding semantic search solutions have been proposed in the past. However, it has been widely recognized that a standardized setting to evaluate and analyze the current state-of-the-art in semantic search is needed to monitor and stimulate further progress in the field. In this paper, we present an evaluation framework for semantic search, analyze the framework with regard to repeatability and reliability, and report on our experiences on applying it in the Semantic Search Challenge 2010 and 2011.

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“…Another work under the same context can be found in [12] where the architecture of a pure peer-to-peer system is presented that offers a distributed, cooperative, and adaptive environment for URL sharing. Potential benefits of integrating new types of searching data (bookmarks and web history data) for personalization and improved searching accuracy are discussed in [13], [18]. The implication of social bookmarks as a way to enhance search in the web was considered in [20], [35] with mixed results.…”

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confidence: 99%

A Collaborative Decentralized Approach to Web Search

Papagelis¹,

Zaroliagis²

2012

IEEE Trans. Syst., Man, Cybern. A

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Most explanations of the user behavior while interacting with the web are based on a top-down approach, where the entire Web, viewed as a vast collection of pages and interconnection links, is used to predict how the users interact with it. A prominent example of this approach is the random-surfer model, the core ingredient behind Google's PageRank. This model exploits the linking structure of the Web to estimate the percentage of web surfers viewing any given page. Contrary to the top-down approach, a bottom-up approach starts from the user and incrementally builds the dynamics of the web as the result of the users' interaction with it. The second approach has not being widely investigated, although there are numerous advantages over the top-down approach regarding (at least) personalization and decentralization of the required infrastructure for web tools. In this paper, we propose a bottom-up approach to study the web dynamics based on web-related data browsed, collected, tagged, and semi-organized by end users. Our approach has been materialized into a hybrid bottom-up search engine that produces search results based solely on user provided web-related data and their sharing among users. We conduct an extensive experimental study to demonstrate the qualitative and quantitative characteristics of user generated web-related data, their strength, and weaknesses as well as to compare the search results of our bottom-up search engine with those of a traditional one. Our study shows that a bottom-up search engine starts from a core consisting of the most interesting part of the Web (according to user opinions) and incrementally (and measurably) improves its ranking, coverage, and accuracy. Finally, we discuss how our approach can be integrated with PageRank, resulting in a new page ranking algorithm that can uniquely combine link analysis with users' preferences.Index Terms-Collaborative search engine, personalization, person-oriented system, user information space.

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Semantic search

Cited by 489 publications

References 5 publications

Web Search Query Privacy, an End-User Perspective

Web Search Query Privacy, an End-User Perspective

Repeatable and reliable semantic search evaluation

A Collaborative Decentralized Approach to Web Search

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