Knowledge Representation with Ontologies: The Present and Future

Recently, we have seen an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the semantic Web, business-to-business applications, and several other application areas. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. However, little discussion has occurred regarding the actual range of knowledge an ontology can successfully represent

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences

We describe the state-of-the-art in performance modeling and prediction for Information Retrieval (IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its shortcomings and strengths. We present a framework for further research, identifying five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performanc

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences

Non peer reviewe

Ontology for Application Development

The chapter describes the process of ontology development for different subject domains for application designing. The analysis of existing approaches to ontology development for software platform realization in some subject domains is depicted. The example of ontology model development for telecom operator billing system based on descriptive logic is shown. For ontology model designing, it is proposed to use two formal theories: descriptive logic and set theory, which allow to systematize data and knowledge, to organize search and navigation, and to describe informational and computational recourses according to the meta-notion standards

Thirty years of Artificial Intelligence and Law:the second decade

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments

A text-mining based model to detect unethical biases in online reviews: a case-study of Amazon.com

The rapid growth of social media in the last decades led e-commerce into a new era of value co-creation between the seller and the consumer. Since there is no contact with the product, people have to rely on the description of the seller, knowing that sometimes it may be biased and not entirely truth. Therefore, reviewing systems emerged in order to provide more trustworthy sources of information, since customer opinions may be less biased. The problem was, once sellers realized the importance of reviews and their direct impact on sales, the need to control this key factor arose. One of the methods developed was to offer customers a certain product in exchange for an honest review. However, in the light of the results of some studies, these "honest" reviews were proved to be biased and skew the overall rating of the product. The purpose of this work is to find patterns in these incentivized reviews and create a model that may predict whether a new review is biased or not. To study this subject, besides the sentiment analysis performed on the data, some other characteristics were taken into account, such as the overall rating, helpfulness rate, review length and the timestamp when the review was written. Results show that some of the most significant characteristics when predicting an incentivized review are the length of a review, its helpfulness rate and the overall polarity score, calculated through VADER algorithm, as the most important sentiment-related factor.O rápido crescimento das redes sociais nas últimas décadas levaram o comércio electrónico a uma nova era de co-criação de valor entre o vendedor e o consumidor. Uma vez que não há contacto com o produto, os clientes têm de se basear na descrição do vendedor, mesmo sabendo que por vezes tal descrição pode ser tendenciosa e não totalmente verdadeira. Deste modo, surgiu um sistema de reviews com o propósito de disponibilizar um meio de informação de maior confiança, uma vez que se trata de partilha de informação entre clientes e por isso mais imparcial. No entanto, quando os vendedores se aperceberam da importância das "reviews" e o seu impacto direto nas vendas, surgiu a necessidade de controlar este fator chave. Uma das formas de o fazer foi através da oferta de determinados produtos em troca de "reviews" honestas. Contudo, à luz dos resultados de alguns estudos, foi demonstrado que estas "reviews" "honestas" são tendenciosas e enviesam a classificação geral do produto. O objetivo deste estudo foi o de encontrar padrões na forma como estas "reviews" incentivadas são escritas e criar um modelo para prever se uma determinada review seria enviesada. Para esta análise, além da análise de sentimentos realizada sobre os dados, outras características foram tidas em conta, tal como a classificação geral, a taxa de "helpfulness", o tamanho da "review" e a hora a que foi escrita. Os modelos gerados mostraram que as características mais importantes na previsão de parcialidade numa "review" são o tamanho e a taxa de utilidade e como característica sentimental mais relevante a pontuação geral da "review", calculada através do algoritmo VADER