Konstantinos Kovas

Difficulty Estimator for Converting Natural Language into First Order Logic

The NLtoFOL system is an interactive web-based system for learning to convert natural language (NL) sentences into first order logic (FOL). In this paper, we present a difficulty estimating expert system that determines the difficulty level of a sentence’s conversion process. Our approach is based on the complexity of the corresponding FOL formula instead of the NL sentence itself. Parameters like the number, the type and the order of quantifiers, the number of implications and the number of different connectives are taken into account. Experimental results show that for a significant part of sentences the difficulty estimating system produces the correct outputs.

A Tool for Automatic Creation of Rule-Based Expert Systems with CFs

This paper introduces a tool, namely ACRES (Automatic CReator of Expert Systems), which can automatically produce rule-based expert systems as CLIPS scripts from a dataset containing knowledge about a problem domain in the form of a large number of cases. The rules are created via a simple systematic approach and make use of certainty factors (CFs). CFs of same conclusions can be combined either using the MYCIN method or a generalization of MYCIN’s method. This latter method requires calculation of some weights, based on a training dataset, via the use of a genetic algorithm. Creation of an expert system is outlined. Small scale experimental results comparing the above methods with each other and a neural network are finally presented.

A System for Aspect-based Opinion Mining of Hotel Reviews.

Online travel portals are becoming important parts for sharing travel information. User generated content and information in user reviews is valuable to both travel companies and to other people and can have a substantial impact on their decision making process. The automatic analysis of used generated reviews can provide a deeper understanding of users attitudes and opinions. In this paper, we present a work on the automatic analysis of user reviews on the booking.com portal and the automatic extraction and visualization of information. An aspect based approach is followed where latent dirichlet allocation is utilized in order to model topic opinion and natural language processing techniques are used to specify the dependencies on a sentence level and determine interactions between words and aspects. Then Naïve Bayes machine learning method is used to recognize the polarity of the user’s opinion utilizing the sentence’s dependency triples. To evaluate the performance of our method, we collected a wide set of reviews for a series of hotels from booking.com. The results from the evaluation study are very encouraging and indicate that the system is fast, scalable and most of all accurate in analyzing user reviews and in specifying users’ opinions and stance towards the characteristics of the hotels and can provide comprehensive hotel information.