Information Retrieval Models: Foundations and Relationships

Roelleke, Thomas

doi:10.2200/s00494ed1v01y201304icr027

Cited by 26 publications

(20 citation statements)

References 70 publications

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“…also each are not identical, different users in the collaborative retrieval has great difference in cognition. Individual users can work together to solve the problem of insufficient knowledge structure and ability to interact in the process of the personal characteristic, the cognitive style and emotional status to the team's overall cognitive that make group cognitive complexity [9].…”

Section: Our Proposed Methodologymentioning

confidence: 99%

Research on the Multimedia Information Retrieval Algorithm based on Information Restructuring and Image Reconstruction

Li¹

2016

Proceedings of the 2016 2nd International Conference on Social Science and Technology Education (ICSSTE 2016)

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Abstract-In this paper, we conduct research on the multimedia information retrieval algorithm based on the information restructuring and image reconstruction. With the massive growth of information resources, people through various retrieval tools for too much information, led directly to information overload. In vector space model and probability retrieval model based on information retrieval tools rarely consider the user's personalized information needs and features, has resulted in a large amount of information retrieval result and correlation information the user's information demand is not big. In order to improve the existing retrieval system, in recent years, scholars to study looked that context information retrieval context factors need to be considered, such as the retrieval time, place and the interactive history, mission, environment and other factors stated or implied in the retrieval process. At present, the context research has become the information behavior, information search process and the research hotspot in the field of information retrieval interaction.

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Section: Our Proposed Methodologymentioning

confidence: 99%

Research on the Multimedia Information Retrieval Algorithm based on Information Restructuring and Image Reconstruction

Li¹

2016

Proceedings of the 2016 2nd International Conference on Social Science and Technology Education (ICSSTE 2016)

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“…We also used another common measurement for search precision or relevance, cumulative gain, and its corollary, discounted cumulative gain. Cumulative gain is a standard strategy for assigning degrees of relevance search results (Dupret, 2011;Järvelin and Kekäläinen, 2002;Roelleke, 2013). 13 The NSDL portal uses Lucene, an open-source Apache search engine, as the core of its discovery service.…”

Section: Relevance and Efficiency Of The Searchmentioning

confidence: 99%

Digital Resources for STEM Educators and Recommendations for Cyberlearning Initiatives: Results from the National Science Foundation Digital Library/Distributed Learning Program Evaluation

Bikson¹,

Straus²,

Agnew³

et al. 2017

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“…Here we are interested in estimating the within-document term frequency tf(t, d) of a term t in a document d. There are several variants of computing this value [14]. One way is using the probability P (t|d) as…”

Section: Full-text Searchmentioning

confidence: 99%

Scalable DB+IR Technology: Processing Probabilistic Datalog with HySpirit

Frommholz

Roelleke

2016

Datenbank Spektrum

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Probabilistic Datalog (PDatalog, proposed in 1995) is a probabilistic variant of Datalog and a nice conceptual idea to model Information Retrieval in a logical, rule-based programming paradigm. Making PDatalog work in real-world applications requires more than probabilistic facts and rules, and the semantics associated with the evaluation of the programs. We report in this paper some of the key features of the HySpirit system required to scale the execution of PDatalog programs.Firstly, there is the requirement to express probability estimation in PDatalog. Secondly, fuzzy-like predicates are required to model vague predicates (e.g. vague match of attributes such as age or price). Thirdly, to handle large data sets there are scalability issues to be addressed, and therefore, HySpirit provides probabilistic relational indexes and parallel and distributed processing. The main contribution of this paper is a consolidated view on the methods of the HySpirit system to make PDatalog applicable in real-scale applications that involve a wide range of requirements typical for data (information) management and analysis.

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Information Retrieval Models: Foundations and Relationships

Cited by 26 publications

References 70 publications

Research on the Multimedia Information Retrieval Algorithm based on Information Restructuring and Image Reconstruction

Research on the Multimedia Information Retrieval Algorithm based on Information Restructuring and Image Reconstruction

Digital Resources for STEM Educators and Recommendations for Cyberlearning Initiatives: Results from the National Science Foundation Digital Library/Distributed Learning Program Evaluation

Scalable DB+IR Technology: Processing Probabilistic Datalog with HySpirit

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