Information filtering based on wiki index database

@article{Smirnov2008InformationFB,
  title={Information filtering based on wiki index database},
  author={Alexander V. Smirnov and Andrew Krizhanovsky},
  journal={ArXiv},
  year={2008},
  volume={abs/0804.2354}
}
In this paper we present a profile-based approach to information filtering by an analysis of the content of text documents. The Wikipedia index database is created and used to automatically generate the user profile from the user document collection. The problem-oriented Wikipedia subcorpora are created (using knowledge extracted from the user profile) for each topic of user interests. The index databases of these subcorpora are applied to filtering information flow (e.g., mails, news). Thus… 

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References

SHOWING 1-10 OF 20 REFERENCES

Integrating Semantic Knowledge into Text Similarity and Information Retrieval

It is found that integrating lexical semantic knowledge improves performance for both tasks: ad-hoc information retrieval and text similarity.

Mining Domain-Specific Thesauri from Wikipedia: A Case Study

It is shown how the classic thesaurus structure of terms and links can be mined automatically from Wikipedia, and it is found that Wikipedia contains a substantial proportion of its concepts and semantic relations.

Synonym search in Wikipedia: Synarcher

Adapted HITS algorithm for synonym search, program architecture, and program work evaluation with test examples are presented in the paper.

Exploiting Synergy Between Ontologies and Recommender Systems

This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web, and the ontology's interest-acquisition problem.

WikiRelate! Computing Semantic Relatedness Using Wikipedia

This work presents experiments on using Wikipedia for computing semantic relatedness and compares it to WordNet on various benchmarking datasets, and shows that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose.

Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis

This work proposes Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia that results in substantial improvements in correlation of computed relatedness scores with human judgments.

An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet

This paper presents an adaptation of Lesk's dictionary-based word sense disambiguation algorithm that uses the lexical database WordNet as the source of glosses for this approach, and attains an overall accuracy of 32%.

Automatic Assignment of Wikipedia Encyclopedic Entries to WordNet Synsets

An approach taken for automatically associating entries from an on-line encyclopedia with concepts in an ontology or a lexical semantic network is described, which will be applied to enriching ontologies with encyclopedic knowledge.

A Fuzzy Linguistic Multi-agent Model for Information Gathering on the Web Based on Collaborative Filtering Techniques

A fuzzy linguistic multi-agent model that incorporates information filtering techniques in its structure, i.e., a collaborative filtering agent is described, in such a way that the information filtering possibilities of multi- agent system on the Web are increased and its retrieval results are improved.

Using WordNet to Improve User Modelling in a Web Document Recommender System

There is disclosed a combined support and locator for underground fixtures intended to be buried which includes a support base formed of a moldable material, metallic means embedded within the support base and having an elliptical configuration.