An Ontology-Based Binary-Categorization Approach for Recognizing Multiple-Record Web Documents Using a Probabilistic Retrieval Model

摘要

The Web contains a tremendous amount of information. It is challenging to determine which Web documents are relevant to a user query, and even more challenging to rank them according to their degrees of relevance. In this paper, we propose a probabilistic retrieval model using logistic regression for recognizing multiple-record Web documents against an application ontology, a simple conceptual modeling approach. We notice that many Web documents contain a sequence of chunks of textual information, each of which constitutes a "record." This type of documents is referred to as multiple-record documents. In our categorization approach, a document is represented by a set of term frequencies of index terms, a density heuristic value, and a grouping heuristic value. We first apply the logistic regression analysis on relevant probabilities using the (ⅰ) index terms, (ⅱ) density value, and (ⅲ) grouping value of each training document. Hereafter, the relevant probability of each test document is interpolated from the fitting curves. Contrary to other probabilistic retrieval models, our model makes only a weak independent assumption and is capable of handling any important dependent relationships among index terms. In addition, we use logistic regression, instead of linear regression analysis, because the relevance probabilities of training documents are discrete. Using a test set of car-ads and another one for obituary Web documents, our probabilistic model achieves the averaged recall ratio of 100%, precision ratio of 83.3%, and accuracy ratio of 92.5%.