Ontology population from web product information

With the vast amount of information available on the Web, there is an increasing need to structure Web data in order to make it accessible to both users and machines. E-commerce is one of the areas in which growing data congestion on the Web has serious consequences. This paper proposes a frame- work that is capable of populating a product ontology us- ing tabular product information from Web shops. By for- malizing product information in this way, better product comparison or recommendation applications could be built. Our approach employs both lexical and syntactic matching for mapping properties and instantiating values. The per- formed evaluation shows that instantiating consumer elec- Tronics from Best Buy and Newegg.com results in an F1 score of approximately 77%

Online Product Search with Focus on Customers\u27 Needs

The success of e-commerce depends on the Information Systems that support it. Currently, the most used approach in e-commerce systems is the faceted search, that requires the customer to be familiar with the technical specification, to find the products that best meet their needs. The aim of this research is to evaluate a novel proposal to improve the online product search. Our solution will automatically map products\u27 features with less technical criteria, which will replace the filters in a faceted search. To achieve this goal, we have adopted a multi-criteria analysis method to rank the result. The proposed solution was evaluated through an empirical experiment with some product categories, using as data set the reviews of experts retrieved from the web. Results showed a strong rank correlation between our solution and the expert reviews, proving its feasibility and effectiveness

Semantic and Syntactic Matching of Heterogeneous e-Catalogues

In e-procurement, companies use e-catalogues to exchange product infor-mation with business partners. Matching e-catalogues with product requests helps the suppliers to identify the best business opportunities in B2B e-Marketplaces. But various ways to specify products and the large variety of e-catalogue formats used by different business actors makes it difficult. This Ph.D. thesis aims to discover potential syntactic and semantic rela-tionships among product data in procurement documents and exploit it to find similar e-catalogues. Using a Concept-based Vector Space Model, product data and its semantic interpretation is used to find the correlation of product data. In order to identify important terms in procurement documents, standard e-catalogues and e-tenders are used as a resource to train a Product Named Entity Recognizer to find B2B product mentions in e-catalogues. The proposed approach makes it possible to use the benefits of all availa-ble semantic resources and schemas but not to be dependent on any specific as-sumption. The solution can serve as a B2B product search system in e-Procurement platforms and e-Marketplaces

Automated Detection of Financial Events in News Text

Today’s financial markets are inextricably linked with financial events like acquisitions, profit announcements, or product launches. Information extracted from news messages that report on such events could hence be beneficial for financial decision making. The ubiquity of news, however, makes manual analysis impossible, and due to the unstructured nature of text, the (semi-)automatic extraction and application of financial events remains a non-trivial task. Therefore, the studies composing this dissertation investigate 1) how to accurately identify financial events in news text, and 2) how to effectively use such extracted events in financial applications. Based on a detailed evaluation of current event extraction systems, this thesis presents a competitive, knowledge-driven, semi-automatic system for financial event extraction from text. A novel pattern language, which makes clever use of the system’s underlying knowledge base, allows for the definition of simple, yet expressive event extraction rules that can be applied to natural language texts. The system’s knowledge-driven internals remain synchronized with the latest market developments through the accompanying event-triggered update language for knowledge bases, enabling the definition of update rules. Additional research covered by this dissertation investigates the practical applicability of extracted events. In automated stock trading experiments, the best performing trading rules do not only make use of traditional numerical signals, but also employ news-based event signals. Moreover, when cleaning stock data from disruptions caused by financial events, financial risk analyses yield more accurate results. These results suggest that events detected in news can be used advantageously as supplementary parameters in financial applications