Dv Viorel Milea

RDF-GL : a SPARQL-based graphical query language for RDF

This chapter presents RDF-GL, a graphical query language (GQL) for RDF. The GQL is based on the textual query language SPARQL and mainly focuses on SPARQL SELECT queries. The advantage of a GQL over textual query languages is that complexity is hidden through the use of graphical symbols. RDF-GL is supported by a Java-based editor, SPARQLinG, which is presented as well. The editor does not only allow for RDF-GL query creation, but also converts RDF-GL queries to SPARQL queries and is able to subsequently execute these. Experiments show that using the GQL in combination with the editor makes RDF querying more accessible for end users.

tOWL: A Temporal Web Ontology Language

Through its interoperability and reasoning capabilities, the Semantic Web opens a realm of possibilities for developing intelligent systems on the Web. The Web Ontology Language (OWL) is the most expressive standard language for modeling ontologies, the cornerstone of the Semantic Web. However, up until now, no standard way of expressing time and time-dependent information in OWL has been provided. In this paper, we present a temporal extension of the very expressive fragment SHIN(D) of the OWL Description Logic language, resulting in the temporal OWL language. Through a layered approach, we introduce three extensions: 1) concrete domains, which allow the representation of restrictions using concrete domain binary predicates; 2) temporal representation , which introduces time points, relations between time points, intervals, and Allens 13 interval relations into the language; and 3) timeslices/fluents, which implement a perdurantist view on individuals and allow for the representation of complex temporal aspects, such as process state transitions. We illustrate the expressiveness of the newly introduced language by using an example from the financial domain.

Knowledge Engineering in a Temporal Semantic Web Context

The emergence of Web 2.0 and the semantic Web as established technologies is fostering a whole new breed of Web applications and systems. These are often centered around knowledge engineering and context awareness. However, adequate temporal formalisms underlying context awareness are currently scarce. Our focus in this paper is two-fold. We first introduce a new OWL-based temporal formalism - TOWL - for the representation of time, change, and state transitions. Based hereon we present a financial Web-based application centered around the aggregation of stock recommendations and financial data.

tOWL: Integrating Time in OWL

The Web Ontology Language (OWL) is the most expressive standard language for modeling ontologies on the Semantic Web. In this chapter, we present the temporal OWL (tOWL) language: a temporal extension of the OWL DL language. tOWL is based on three layers added on top of OWL DL. The first layer is the Concrete Domains layer, which allows the representation of restrictions using concrete domain binary predicates. The second layer is the Time Representation layer, which adds time points, intervals, and Allen’s 13 interval relations. The third layer is the Change Representation layer which supports a perdurantist view on the world, and allows the representation of complex temporal axioms, such as state transitions. A Leveraged Buyout process is used to exemplify the different tOWL constructs and show the tOWL applicability in a business context.

RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms

The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.

Temporal optimisations and temporal cardinality in the tOWL language

The tOWL language is a temporal web ontology language based on OWL-DL without nominals. The language enables the representation of time and time-related aspects, such as state transitions. The design choices of the language pose new challenges from a temporal perspective. One such challenge is the representation of temporal cardinality. Another challenge consists of optimising the temporal representations in order to reduce the number of axioms. One such optimisation is temporal coalescing, which merges concepts that are associated with time intervals that either meet or share at least one instant with each other. In this paper we formally introduce these concepts into the tOWL language and illustrate how they can be applied.

A general framework for time-aware decision support systems

In this paper we present a general framework for time-aware decision support systems. The framework uses the state-of-the-art tOWL language for the representation of temporal knowledge and enables temporal reasoning over the information that is represented in a knowledge base. Our approach uses state-of-the-art Semantic Web technology for handling temporal data. Through such an approach, the designer of a system can focus on the application intelligence rather than enforcing/checking data related restrictions manually. Also, there is an increased support for reuse of temporal reasoning tools across applications. We illustrate the applicability of our framework by building a market recommendations aggregation system. This system automatically collects market recommendations from online sources and, based on the past performance of the analysts that issued a recommendation, generates an aggregated recommendation in the form of a buy, hold, or sell advice. We illustrate the flexibility of our proposed system by implementing multiple methods for the aggregation of market recommendations.

A fuzzy model of the MSCI EURO index based on content analysis of European Central Bank statements

In this paper we investigate whether the MSCI EURO index can be predicted based on the content of European Central Bank (ECB) statements. We propose a new model to retrieve information from free text and transform it into a quantitative output. For this purpose, we first identify all adjectives in an ECB statement by using the Stanford Part-of-Speech Tagger and feed these to the General Inquirer (GI) content analysis tool. From GI we obtain a matrix that provides for each document and for each content category the percentage of words in the document that fall under each category. After normalizing the data, we develop a Takagi-Sugeno (TS) fuzzy model using fuzzy c-means clustering. The TS fuzzy system is used to model the levels of the MSCI EURO index. To determine the performance of the model, we focus on the accuracy of predicting upward or downward movement in the index, and obtain, on average, an accuracy of 66%, that corresponds to an improvement of 16% over a random classifier.

On Temporal Cardinality in the Context of the TOWL Language

The TOWL language is a temporal ontology language built on top of OWL-DL that enables descriptions involving time and temporal aspects such as change and state transitions. Extending OWL-DL into a temporal context does not only relate to providing the adequate expressiveness for such a goal, but also ensuring that static concepts preserve their meaning in a temporal environment. One such concept relates to cardinality. In this paper, we discuss temporal cardinality in the context of the TOWL language, and provide a possible approach towards representing temporal cardinality in this context.

A fuzzy model of a European index based on automatically extracted content information

In this paper we build on previous work related to predicting the MSCI EURO index based on content analysis of ECB statements. Our focus is on reducing the number of features employed for prediction through feature selection. For this purpose we rely on two methodologies: (stepwise) linear regression and greedy forward feature subset selection. The original dataset consists of 13 features (General Inquirer content categories). Both methodologies provide an improvement in the overall accuracy of the model, while reducing the number of features employed. Through linear regression we achieve an accuracy of 67.58% on the testing set by relying on six features, while greedy forward selection enables an accuracy on the test set of 69.50% while relying on eight features.