Information Content Model Research Articles

A novel family of IC-based similarity measures with a detailed experimental survey on WordNet

This paper introduces a novel family of ontology-based similarity measures based on the Information Content (IC) theory, a detailed state of the art, a large experimental survey into ontology-based similarity measures on WordNet, and a new comparison between intrinsic and corpus-based IC models. Our experiments are based on our implementation of a large set of similarity measures, intrinsic and corpus-based IC models, which are evaluated on two known datasets and three different WordNet versions. The new measures are called weighted Jiang–Conrath distance (wJ&Cdist) and similarity (wJ&Csim), cosine-normalized Jiang–Conrath similarity (cosJ&Csim) and cosine-normalized weighted Jiang–Conrath similarity (coswJ&Csim). Two of our similarity measures outperform the state-of-the-art measures on the RG65 dataset, and one of them obtains the third overall score on all the datasets and evaluated WordNet versions. The cosine-normalized similarity measures are a non-linear normalization of the classic Jiang–Conrath (J&C) distance and the new wJ&C distance. On the other hand, the wJ&C distance is a generalization of the classic J&C distance which is based on the length of the shortest path between concepts within an IC-based weighted graph. Our measures are based on two not previously considered notions: (1) a generalization of the classic J&C distance to any type of taxonomy, based on an IC-based weighted graph derived from the conditional probabilities between child and parent concepts, and (2) a non-linear normalization function that converts the ontology-based semantic distances into similarity functions. Finally, the corpus-based IC models based on the Resnik method obtain rivaling results as regards the state-of-the-art intrinsic IC models, when they are used with some unexplored WordNet-based frequency files. Therefore, this latter fact allows us to reconsider some previous conclusions about the outperformance of the intrinsic IC models over the corpus-based ones.

A new family of information content models with an experimental survey on WordNet

This paper introduces a new family of intrinsic and corpus-based Information Content (IC) models for ontology-based similarity measures based on the IC theory, a detailed state of the art, an experimental survey of IC models and IC-based similarity measures on WordNet, and a comparison between intrinsic and corpus-based IC models. The family of IC models is made up of five intrinsic IC models, called CondProbHypo, CondProbUniform, CondProbLeaves, CondProbLogistic, and CondProbCosine, and one corpus-based IC model called CondProbCorpus which completes the family. The proposed IC models rely on two previously unconsidered notions: (1) the preservation of the probabilistic structure of the taxonomy associated to the conditional probabilities between child and parent concepts, and (2) the explicit consideration of a cognitive similarity notion in the definition of the IC model. The family of IC models defines a new method for the proposal of new intrinsic IC models based on the exploration of other alternatives for the intrinsic estimation of the conditional probabilities between child and parent concepts. Our work is inspired by an unexplored relationship between the Jiang–Conrath distance and a shortest path on an IC-based weighted graph, derived from the conditional probabilities between concepts, as well as certain cognitive evidence about the perception distance between concepts. The new IC models obtain results comparable to the state of the art and satisfy a set of well-founded structure axioms. In addition, we prove that most of intrinsic IC models and IC-based similarity measures do not show a significant statistical difference as regards a baseline corpus-based IC model and the Jiang–Conrath similarity, with the exception of the overall outperformance shown by the Sánchez et al. (2012) IC model and the cosJ&C similarity measure, which has recently been introduced by the authors.

Assimilation of Dual-Polarization Radar Observations in Mixed- and Ice-Phase Regions of Convective Storms: Information Content and Forward Model Errors

Abstract Dual-polarization Doppler radar has proven useful for the estimation of hydrometeor content and the classification of hydrometeor type. Recent studies have leveraged dual-polarization-specific information to produce improved assimilated cloud and precipitation fields from the warm rain (above freezing) portion of deep convective storms. While the strengths of dual-polarization radar observations have been conclusively shown for rain and hail hydrometeors, it is less clear how much information is provided in mixed-phase and ice-only regions. In this paper, a Markov chain Monte Carlo (MCMC) algorithm is used to examine the information content of dual-polarization-specific variables in the ice-phase region of a convective storm. Results are used to quantify how much information is added by specific differential phase and radar correlation coefficient, as well as how this information is degraded when the assumed particle size distribution and particle density are allowed to vary. It is found that dual-polarization-specific observations (Kdp and ρhv) provide significant information on rimed ice content, and moderate information on pristine ice, especially where snow mass is more than 10% of the total volume hydrometeor mass. There is a significant reduction in information content for rain and a near-complete loss of information for graupel–hail and snow when the particle size distribution and ice particle densities are not well known, and there are systematic changes in radar information gain and loss with changes in hydrometeor mass. The results highlight the need for a thorough exploration of forward model sensitivities prior to performing radar data assimilation.

An Information Axiom based decision making approach under hybrid uncertain environments

This paper aims to develop a decision making approach based on the Information Axiom under hybrid uncertain environments. In these environments fuzziness and randomness co-exist. To model the hybrid uncertainties, the design range and system range of an evaluation criterion are expressed as a fuzzy variable and a random variable respectively. Calculating the Information Contents for these criteria would involve integrals whose bounds are fuzzy variables. To calculate these integrals, an expected Information Content Model (eICM) is proposed based upon the credibility theory. To reflect the designer’s risk appetite accurately, the confidence levels are introduced and a credibility Information Content Model (cICM) based on the chance constrains programming model is constructed. Two algorithms based on fuzzy simulation methods and Genetic Algorithm are developed for these two models. Finally, a case study is illustrated to highlight the effectiveness and accuracy of the proposed approach.

Obtaining Information Content of Concepts: An Overview

Semantic similarity measure between concepts is a generic issue for many applications of computational linguistics and artificial intelligence. Information Content (IC) of concept is an important dimension of accessing semantic similarity between two concepts. Recently it has attracted great concern and become a hot topic. The paper gives a general overview of usage of information content in semantic similarity computing and then focuses on how to obtain information content. It reviews and analyses state-of-art IC models, including Corpus-dependent and Corpus-independent IC approach. Hyponymbased IC Model, leaves-based IC Model, concept’s topology structure based IC Model and relation-based IC Model are discussed respectively in detail. The important related issues are described. Finally further research is outlined for the improvement of IC.

Information content of advertising: Empirical evidence from the OTC analgesic industry

We develop an empirical study of the information–persuasion trade-off in advertising using data on the information content of ads, which we measure with the number of information cues in ads within an entire industry. The data are from video files of all advertisements in the OTC analgesics industry from 2001 to 2005. We propose a simple theoretical framework to motivate an ordered probit model of information content. We find that stronger vertical differentiation is positively associated with the delivery of more product information in a brand's advertisements: brands with higher levels of quality include more information cues. Next, comparative advertisements contain significantly more product information than self-promotional advertisements. Finally, brands with larger market shares and brands competing against generic substitutes with large market shares use ads that have less information content.

Information Content-Based Gene Ontology Semantic Similarity Approaches: Toward a Unified Framework Theory

Several approaches have been proposed for computing term information content (IC) and semantic similarity scores within the gene ontology (GO) directed acyclic graph (DAG). These approaches contributed to improving protein analyses at the functional level. Considering the recent proliferation of these approaches, a unified theory in a well-defined mathematical framework is necessary in order to provide a theoretical basis for validating these approaches. We review the existing IC-based ontological similarity approaches developed in the context of biomedical and bioinformatics fields to propose a general framework and unified description of all these measures. We have conducted an experimental evaluation to assess the impact of IC approaches, different normalization models, and correction factors on the performance of a functional similarity metric. Results reveal that considering only parents or only children of terms when assessing information content or semantic similarity scores negatively impacts the approach under consideration. This study produces a unified framework for current and future GO semantic similarity measures and provides theoretical basics for comparing different approaches. The experimental evaluation of different approaches based on different term information content models paves the way towards a solution to the issue of scoring a term's specificity in the GO DAG.

Study on spatial prediction and time forecast of landslide

Landslide is one of natural hazards in mountainous regions, which has resulted in a large quantity of casualties and property losses and also has absorbed high attention of the researchers and government. A considerable amount of research has been carried out in the past 30 years in the landslide field. In this paper, the contribution and existing problems on landslide are analyzed and summarized in the previous studies. Spatial prediction and zonation of the regional landslide are developed by using information content model that is a new method, with the example of landslide in Xincheng District of Badong County. On the other hand, by learning from the forecast theories and methods of earthquake forecast, probability of excess for landslide that will take place in the studied area is calculated quantitatively in next 5 and 10 years. All the calculated results are mainly accordant with the regional fact. Therefore, it may provide scientific data for landslide prevention and reduction as well as landslide management. Based on the achievement obtained in this study, it was found that 29.11% of the total area was prone to landslide due to the adverse effects of topography, reservoir water in the leading edge of bank, and improper land use. At the same time, the theory of spatial prediction and probability of excess will be example and reference for the other region of China or the world.

Information Content of Advertising: Empirical Evidence from the OTC Analgesic Industry

We empirically study the information-persuasion trade-off in advertising using data on the information content of advertisements, which we measure with the number of information cues in ads. We propose a simple theoretical framework to motivate an ordered probit model of information content. We find that stronger vertical differentiation is positively associated with the delivery of more product information in a brand’s advertisements: brands with higher levels of quality include more information cues. Next, comparative advertisements contain significantly more product information than self-promotional advertisements. Finally, brands with higher market shares and brands competing against strong generic substitutes have less information content.

Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.