Using Internet News Flows as Marketing Data Component

2006

In this contribution we present our results concerning the influence of news on market trends. We processed stock news with a grammar-driven classification. We found some potentialities of market trend prediction and present promising experimental results. As a main result we present the fact that there are two points (altogether only two) representing the minimum of good news/bad news relation for two long-term trends breaking points (altogether only two) during the last 10 years and that both of these points representing news appear in both cases about six months before the long-term trend of markets changed.

International Journal of Web Engineering and Technology, 2010

Due to the market sensitivity to emerging news, investors on financial markets need to continuously monitor financial events when deciding on buying and selling equities. We propose the use of lexico-semantic patterns for financial event extraction from RSS news feeds. These patterns use financial ontologies, leveraging the commonly used lexico-syntactic patterns to a higher abstraction level, thereby enabling lexico-semantic patterns to identify more and more precisely events than lexico-syntactic patterns from text. We have developed rules based on lexico-semantic patterns used to find events, and semantic actions that allow for updating the domain ontology with the effects of the discovered events. Both the lexico-semantic patterns and the semantic actions make use of the triple paradigm that fosters their easy construction and understanding by the user. Based on precision, recall, and F1 measures, we show the effectiveness of the proposed approach. Biographical notes: Jethro Borsje obtained a bachelor and a cum laude master degree in informatics and economics at the Erasmus University, Rotterdam, The Netherlands in 2006 and 2007 focusing on economics and ICT. During his bachelor and master degree programme he published research related to the Semantic Web. His master degree research was related to rule based ontology learning. From 2007 until early 2010 he worked as a software engineer for a research oriented firm, where he specialized in applying Semantic Web technologies to the financial domain. Other research interests include machine learning, natural language processing and pattern recognition. Currently he holds a position as an enterprise software engineer at a government agency.

An Ontology-Based Framework, 2009

In this paper we present StockWatcher, an OWL-based web application that enables the extraction of relevant news items from RSS feeds concerning the NASDAQ-100 listed companies. The application's goal is to present a customized, aggregated view of the news categorized by different topics. We distinguish between four relevant news categories: i) news regarding the company itself, ii) news regarding direct competitors of the company, iii) news regarding important people of the company, and iv) news regarding the industry in which the company is active. At the same time, the system presented in this chapter is able to rate these news items based on their relevance. We identify three possible effects that a news message can have on the company, and thus on the stock price of that company: i) positive, ii) negative, and iii) neutral. Currently, StockWatcher provides support for the NASDAQ-100 companies. The selection of the relevant news items is based on a customizable user portfolio that may consist of one or more of these companies.

The increase in business, the intense competition between companies, make the marketing an operation and a management mechanism essential for enterprises. On the other hand, social networks are expanding rapidly and can help companies in collecting customers’ feedback, establishing a brand presence, observing the way their brands are discussed and perceived. This paper focuses on the advantages of analysing data extracted from unstructured sources for marketing purpose and shows the results obtained in a case study in the agri-food sector. The innovative architecture proposed is realized through the integration of some existing java based tools and of one or many ontologies, in order to retrieve more frequent concepts from unstructured sources, suggest links of articles and images, detect the language used in the sources, suggest other concepts related to the research and filter the results obtained from the elaboration of the unstructured sources.

2006

E-Commerce is a direct extension of digitalization of our economy; it begins to have an important place in the internet’s user’s lives. The main problems of e-Commerce are the extraction of relevant informations about products, how to provide users with products that meet their needs, hence the need to have relevant information retrieval system ie with high accuracy and the lack of works in this field. This paper presents an information retrieval system based on an e-commerce ontology representing the different properties of the products on the internet and which will be used to establish the model of the index. At the end of this paper we have applied our product information retrieval system on a famous e-commerce web site, the results show that our system will help users to acquire the desired information on products that meet their needs.

Web Semantics: Science, Services and Agents on the World Wide Web, 2010

This paper presents the development of an ontology to represent financial headline news. This ontology is developed using the On-To-Knowledge methodology where the focus is on the design steps of the Knowledge Meta Process. This development is part of an ongoing project which aims to design a virtual stock market simulator based on multi-agent systems. The proposed ontology has 31 concepts and includes 201 attributes. The testing results conducted on reliable headline news show that 99% of these headline news can be properly represented by the attributes of the right category in the ontology. Unreliable headline news characterized by news having uncertainties, incompleteness, ill-definition, or imprecision cannot be represented by the proposed ontology. Approaches for representing these unreliable headline news are discussed.

2005

The h-TechSight Knowledge Management Portal (KMP) enables support for knowledgeintensive industries in monitoring information resources on the Web, as an important factor in business competitiveness. The portal contains tools for identification of concepts and terms from an ontology relevant to the user's interests, and enables the user to monitor them over time. It also contains tools for ontology management and modification, based on the results of targeted knowledge extraction from the web. The platform provides a means for businesses to keep track of trends and topics of interest in their field, and alert them to changes. In this paper we focus on the tools for targeted search and ontology management, driven by an ontology-based information extraction system, which has been evaluated over a test set of 38 documents and achieves 97% Precision and 92% Recall.

The International Arab Journal of Information Technology, 2011

Although varieties of investigations have been done on human semantic interactions with Web resources, no advanced and considerable progresses have been achieved. It could be said that comparative shopping systems are the last generations of B2C eCommerce systems that connect to multiple online stores and collect the information requested by the user. In some cases, the information is extracted from the online store sites through keyword search and other means of textual analysis. These processes make use of assumptions about the proximity of certain pieces of information. These heuristic approaches are error-prone and are not always guaranteed to work. In this paper, we propose an ontology-based approach to extract the products' information and the vendors' price from their public Web sites' pages. Although most vendors on the Web present their products' information in HTML documents that are not semantic formats. However, our approach is based on understanding semantics of HTML documents and extracting the information automatically.

2011 IEEE International Conference on Granular Computing, 2011

A price information retrieval (IR) system allows users to search and view differences among prices of specific products. Building product-price driven IR system is a challenging and active research area. Approaches entirely depending products information provided by shops via interface environment encounter limitations of database. While automatic systems specifically require product names and commercial websites for their input. For both paradigms, approaches of building product-price IR system for Vietnamese are still very limited. In this paper, we introduce an automatic Vietnamese IR system for product-price by identifying and storing Xpath patterns to extract prices of products from commercial websites. Experiments of our system show promising results.