The ever-increasing amount of Web information offered to news readers (e.g., news analysts) stimulates the need for news selection, so that informed decisions can be made with up-to-date knowledge.Hermes is an ontologybased framework for building news personalization services.It uses an ontology crafted from available news sources, allowing users to select and filter interesting concepts from a domain ontology.The Aethalides framework enhances the Hermes framework by enabling news classification through lexicographic and semantic properties.For this, Aethalides applies word sense disambiguation and ontology learning methods to news items.When tested on a set of news items on finance and politics, the Aethalides implementation yields a precision and recall of 74.4% and 49.4%, respectively, yielding an F 0.5 -measure of 67.6% when valuing precision more than recall.

In this paper we present a framework for automatic exploitation of news in stock trading strategies. Events are extracted from news messages presented in free text without annotations. We test the introduced framework by deriving trading strategies based on technical indicators and impacts of the extracted events. The strategies take the form of rules that combine technical trading indicators with a news variable, and are revealed through the use of genetic programming. We find that the news variable is often included in the optimal trading rules, indicating the added value of news for predictive purposes and validating our proposed framework for automatically incorporating news in stock trading strategies.

As today’s financial markets are sensitive to breaking news on economic events, accurate and timely automatic identification of events in news items is crucial. Unstructured news items originating from many heterogeneous sources have to be mined in order to extract knowledge useful for guiding decision making processes. Hence, we propose the Semantics-Based Pipeline for Economic Event Detection (SPEED), focusing on extracting financial events from news articles and annotating these with meta-data at a speed that enables real-time use. In our implementation, we use some components of an existing framework as well as new components, e.g., a high-performance Ontology Gazetteer, a Word Group Look-Up component, a Word Sense Disambiguator, and components for detecting economic events. Through their interaction with a domain-specific ontology, our novel, semantically enabled components constitute a feedback loop which fosters future reuse of acquired knowledge in the event detection process.

Value-at-Risk (VaR) is an important tool to assess portfolio risk. When calculating VaR based on historical stock return data, we hypothesize that this historical data is sensitive to outliers caused by news events in the sampled period. In this paper, we research whether the VaR accuracy can be improved by considering news events as additional input in the calculation. This involves processing the historical data in order to reflect the impact of news on the stock returns. Our experiments show that when an event occurs, removing the noise (that is caused by an event) from the measured stock prices for a small time window can improve VaR predictions.

In order to effectively and efficiently disclose the ever-growing amount of widely distributed RDF data to demanding users in real-time environments, RDF query engines need to optimize the join order of partial query results. For this, a two-phase optimization (2PO) algorithm and a genetic algorithm (GA) have already been proposed. We propose an alternative approach - an ant colony system (ACS). On a large RDF data source, our approach significantly outperforms both 2PO and the GA in terms of execution time and solution quality for RDF chain queries consisting of up to about ten joins. For larger queries, our novel ACS delivers solutions of better quality than 2PO does, while realizing a solution quality that is comparable to the solution quality of the GA method. However, the GA approach offers the best trade-off between execution time and solution quality for such larger queries.

In this paper we evaluate several novelty control mechanisms for ranking Web news articles depicting a story. These mechanisms rank individual news items based on a novelty measure using as context the items which have been previously reviewed. An evaluation within the Hermes news personalization framework is performed for pair wise and non-pair wise novelty control mechanisms based on various distance measures and vector-based news representations. On average, the most effective distance measure is Kullback-Leibler and the best performing news representation vector uses named entities.

Information systems are crucial for effective supply chain management in today's complex supply chains for durable goods. Complex decision making processes on strategic, tactical, and operational level require substantial support in order to contribute to the agility of organizations. Supply chain regimes, i.e., regimes encompassing both the sales and the procurement market in a complex supply chain, provide a way of intuitively and meaningfully characterizing and modeling supply chain market conditions without a need for explicit modeling of individual aspects of the market. This paper makes a first explorative step towards a model incorporating such regimes, while maintaining the dynamics which enable the model to be utilized in the sales process, e.g., for dynamic product pricing. Initial results show that supply chain regimes have feasible characteristics, based on both sales and procurement market indicators. Taking into consideration these regimes enables a more deliberate sales model.

We investigate the effects of adding procurement information (component offer prices) to a sales-based economic regime model, which is used for strategic, tactical, and operational decision making in dynamic supply chains. The performance of the regime model is evaluated through experiments with the MinneTAC trading agent, which competes in the TAC SCM game. We find that the new regime model has a similar overall predictive performance as the existing model. Regime switches are predicted more accurately, whereas the prediction accuracy of dominant regimes is slightly worse. However, by adding procurement information, we have enriched the model and we have further opportunities for applications in the procurement market, such as procurement reserve pricing.