Multidimensional Web Page Evaluation Model Using Segmentation And Annotations

TL;DR

This paper introduces a hybrid, multidimensional model for web page evaluation that combines structural and content semantics through segment annotations, improving semantic assessment accuracy.

Contribution

It presents a novel hybrid evaluation model using segment-level annotations and multidimensional scoring for more accurate web page assessment.

Findings

Prototype experiments confirm the model's efficiency

Segment scoring improves semantic evaluation

Hybrid approach outperforms traditional methods

Abstract

The evaluation of web pages against a query is the pivot around which the Information Retrieval domain revolves around. The context sensitive, semantic evaluation of web pages is a non-trivial problem which needs to be addressed immediately. This research work proposes a model to evaluate the web pages by cumulating the segment scores which are computed by multidimensional evaluation methodology. The model proposed is hybrid since it utilizes both the structural semantics and content semantics in the evaluation process. The score of the web page is computed in a bottom-up process by evaluating individual segment's score through a multi-dimensional approach. The model incorporates an approach for segment level annotation. The proposed model is prototyped for evaluation; experiments conducted on the prototype confirm the model's efficiency in semantic evaluation of pages.