|The value of everything: ranking and association with encyclopedic knowledge|
|Authors:||Kino High Coursey|
|Citation:||University of North Texas : . 2009. United States, Texas.|
|Google Scholar cites:||Not available|
|Added by Wikilit team:||Added on initial load|
|Article:||Google Scholar BASE PubMed|
|Other scholarly wikis:||AcaWiki Brede Wiki WikiPapers|
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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.
"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."
|Topics:||Other natural language processing topics, Ontology building|
|Theory type:||Design and action|
|Research design:||Simulation, Statistical analysis|
|Data source:||Simulation results, Wikipedia pages|
|Collected data time dimension:||Cross-sectional|
|Unit of analysis:||Article|
|Wikipedia data extraction:||Dump|
|Wikipedia page type:||Article|
"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)."
"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"