A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge

We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in proving the existence of an entailment between them. We conceive our system as a modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis we combine a deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and the accuracy of results. For RTE we use logical inference of first-order employing model-theoretic techniques and automated reasoning tools. The inference is supported with problem-relevant background knowledge extracted automatically and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or other, more experimental sources with, e.g., manually defined presupposition resolutions, or with axiomatized general and common sense knowledge. The results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.Comment: 25 pages, 10 figure

Text Analytics for Android Project

Most advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis, automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article

A conceptual integrated theoretical model for online consumer behaviour

The study addresses the limited and fragmented approaches of consumer behaviour studies in the existing literature and a lack of comprehensive integrated theoretical models of online consumer behaviour. The aim of the study is to propose a conceptual integrated theoretical model for online consumer behaviour which suggests a deviation from the existing purchasing approaches to consumer behaviour - hence a move towards an understanding of consumer behaviour in terms of two new approaches, namely the web-based communication exposure and internal psychological behavioural processes approaches, is proposed. The study addresses two main research problems, namely that inadequate knowledge and information exist on online consumers’ behavioural processes, especially their internal psychological behavioural processes during their exposure to web-based communication messages and their progression through the complete web-based communication experience; and that there is no conceptual integrated theoretical model for online consumer behaviour in the literature. This study, firstly, allows for systematic theoretical exploration, description, interpretation and integration of existing literature and theory on offline and online consumer behaviour including the following: theoretical perspectives and approaches; determinants; decision making; consumer information processing and response; and theoretical foundations. This systematic theoretical exploration and description of consumer behaviour literature and theory commences with the contextualisation and proposal of a new definition, perspective and theoretical approaches to online consumer behaviour; the discussion and analysis of the theory of the determinants of consumer behaviour; the discussion and analysis of decision-making theory; the proposition of a new online information decision-making perspective and model; the discussion and analysis of consumer information-processing and response theory and models; the discussion and analysis of the theoretical foundations of consumer behaviour; and the identification of theoretical criteria for online consumer behaviour. Declaration – acknowledgements - abstract Secondly, the study develops a conceptual integrated theoretical model for online consumer behaviour, thereby theoretically grounding online consumer behavioural processes in the context of internal psychological behavioural processes and exposure to web-based communication messages. It is hence posited that the study provides a more precise understanding of online consumers’ complicated internal cognitive and psychological behavioural processes in their interactive search for and experience of online web-based communication and information, which can be seen as a major contribution to the field of study.Communication ScienceD. Litt. et Phil. (Communication

Integrating E-Commerce and Data Mining: Architecture and Challenges

We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based on our expe-rience at Blue Martini Software, for supporting this integration. The architecture can dramatically reduce the pre-processing, cleaning, and data understanding effort often documented to take 80% of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the web server) in order to support logging of data and metadata that is essential to the discovery process. We describe the data transformation bridges required from the transaction processing systems and customer event streams (e.g., clickstreams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization, and OLAP. We con-clude with a set of challenges.Comment: KDD workshop: WebKDD 200

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering