Last modified on October 31, 2013, at 16:12

The value of everything: ranking and association with encyclopedic knowledge

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Publication (help)
The value of everything: ranking and association with encyclopedic knowledge
Authors: Kino High Coursey [edit item]
Citation: University of North Texas  : . 2009. United States, Texas.
Publication type: Thesis
Peer-reviewed: Yes
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DOI: Define doi.
Google Scholar cites: Not available
Link(s): Paper link
Added by Wikilit team: Added on initial load
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The value of everything: ranking and association with encyclopedic knowledge is a publication by Kino High Coursey.


[edit] Abstract

This dissertation describes {WikiRank}, an unsupervised method of assigning relative values to elements of a broad coverage encyclopedic information source in order to identify those entries that may be relevant to a given piece of text. The valuation given to an entry is based not on textual similarity but instead on the links that associate entries, and an estimation of the expected frequency of visitation that would be given to each entry based on those associations in context. This estimation of relative frequency of visitation is embodied in modifications to the random walk interpretation of the {PageRank} algorithm. {WikiRank} is an effective algorithm to support natural language processing applications. It is shown to exceed the performance of previous machine learning algorithms for the task of automatic topic identification, providing results comparable to that of human annotators. Second, {WikiRank} is found useful for the task of recognizing text-based paraphrases on a semantic level, by comparing the distribution of attention generated by two pieces of text using the encyclopedic resource as a common reference. Finally, {WikiRank} is shown to have the ability to use its base of encyclopedic knowledge to recognize terms from different ontologies as describing the same thing, and thus allowing for the automatic generation of mapping links between ontologies. The conclusion of this thesis is that the knowledge access heuristic" is valuable and that a ranking process based on a large encyclopedic resource can form the basis for an extendable general purpose mechanism capable of identifying relevant concepts by association which in turn can be effectively utilized for enumeration and comparison at a semantic level."

[edit] Research questions

"The primary focus of my research was to explore the use of a form of encyclopedic knowledge to aid automatic tasks. To do this I developed a method to implement a context sensitive simulation of a visitation process applied to a graph of encyclopedic knowledge."

Research details

Topics: Other natural language processing topics, Ontology building [edit item]
Domains: Information science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Other [edit item]
Theories: "Undetermined" [edit item]
Research design: Simulation, Statistical analysis [edit item]
Data source: [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Article [edit item]
Wikipedia data extraction: Clone [edit item]
Wikipedia page type: Article [edit item]
Wikipedia language: English [edit item]

[edit] Conclusion

"I developed a method to implement a context sensitive simulation of a visitation process applied to a graph of encyclopedic knowledge. The result of the process is an estimate of the frequency of accessing each entry (and by extension each concept) in an encyclopedia. The relative visitation values assigned offer what I think of as a ‚knowledge access heuristic‛: that the more often a piece of knowledge or concept is accessed the more important it is to that context. Using such a visitation simulation one can suggest better allocation of processing resources, perform analysis and inferences based on the distribution of knowledge access (allowing topic identification), and analyze the way the simulated knowledge access varied based on different stimuli (a form of semantic or topical similarity)."

[edit] Comments

"This dissertation describes WikiRank, an unsupervised method of assigning relative values to elements of a broad coverage encyclopedic information source in order to identify those entries that may be relevant to a given piece of text"


Further notes[edit]