Web-Based Measure of Semantic Relatedness

Semantic relatedness measures quantify the degree in which some words or concepts are related, considering not only similarity but any possible semantic relationship among them. Relatedness computation is of great interest in different areas, such as Natural Language Processing, Information Retrieval, or the Semantic Web. Different methods have been proposed in the past; however, current relatedness measures lack some desirable properties for a new generation of Semantic Web applications: maximum coverage, domain independence, and universality. In this paper, we explore the use of a semantic relatedness measure between words, that uses the Web as knowledge source. This measure exploits the information about frequencies of use provided by existing search engines. Furthermore, taking this measure as basis, we define a new semantic relatedness measure among ontology terms. The proposed measure fulfils the above mentioned desirable properties to be used on the Semantic Web. We have tested extensively this semantic measure to show that it correlates well with human judgment, and helps solving some particular tasks, as word sense disambiguation or ontology matching

Semantic Interpretation of User Queries for Question Answering on Interlinked Data

The Web of Data contains a wealth of knowledge belonging to a large number of domains. Retrieving data from such precious interlinked knowledge bases is an issue. By taking the structure of data into account, it is expected that upcoming generation of search engines is approaching to question answering systems, which directly answer user questions. But developing a question answering over these interlinked data sources is still challenging because of two inherent characteristics: First, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain question. Second, constructing a federated formal query across different datasets requires exploiting links between these datasets on both the schema and instance levels. In this respect, several challenges such as resource disambiguation, vocabulary mismatch, inference, link traversal are raised. In this dissertation, we address these challenges in order to build a question answering system for Linked Data. We present our question answering system Sina, which transforms user-supplied queries (i.e. either natural language queries or keyword queries) into conjunctive SPARQL queries over a set of interlinked data sources. The contributions of this work are as follows: 1. A novel approach for determining the most suitable resources for a user-supplied query from different datasets (disambiguation approach). We employed a Hidden Markov Model, whose parameters were bootstrapped with different distribution functions. 2. A novel method for constructing federated formal queries using the disambiguated resources and leveraging the linking structure of the underlying datasets. This approach essentially relies on a combination of domain and range inference as well as a link traversal method for constructing a connected graph, which ultimately renders a corresponding SPARQL query. 3. Regarding the problem of vocabulary mismatch, our contribution is divided into two parts, First, we introduce a number of new query expansion features based on semantic and linguistic inferencing over Linked Data. We evaluate the effectiveness of each feature individually as well as their combinations, employing Support Vector Machines and Decision Trees. Second, we propose a novel method for automatic query expansion, which employs a Hidden Markov Model to obtain the optimal tuples of derived words. 4. We provide two benchmarks for two different tasks to the community of question answering systems. The first one is used for the task of question answering on interlinked datasets (i.e. federated queries over Linked Data). The second one is used for the vocabulary mismatch task. We evaluate the accuracy of our approach using measures like mean reciprocal rank, precision, recall, and F-measure on three interlinked life-science datasets as well as DBpedia. The results of our accuracy evaluation demonstrate the effectiveness of our approach. Moreover, we study the runtime of our approach in its sequential as well as parallel implementations and draw conclusions on the scalability of our approach on Linked Data

The OntoNL semantic relatedness measure for owl ontologies

Summarization: An effect of the growing importance of the Semantic Web used for sharing knowledge over the Internet was the development and publishing of many ontologies in different domains. This led to the need of developing mechanisms for capturing the semantics of the ontologies. In this paper, we introduce the OntoNL Semantic Relatedness Measure, a fully automated way of measuring, in an asymmetric way, semantic relatedness between concepts of domain ontologies. We have developed metrics to guide the automation of the procedure by using feedback from an extensive evaluation with human subjects.Παρουσιάστηκε στο: In the Proceedings of the Second IEEE International Conference on Digital Information Management (IEEE ICDIM), Lyon, Franc

Ontology-driven semantic ranking for natural language disambiguation in the ontoNL framework

Summarization: The measurement of the semantic relatedness has many applications in natural language processing, and many different measures have been proposed. Most of these measures use WordNet as their central resource and not domain ontologies of a particular context. We propose and evaluate a semantic relatedness measure for OWL domain ontologies that concludes to the semantic ranking of ontological, grammatically-related structures. This procedure is used to disambiguate in a particular domain of context and represent in an ontology query language, natural language expressions. The ontology query language that we use is the SPARQL. The construction of the queries is automated and also dependent on the semantic relatedness measurement of ontology concepts. The methodology has been successfully integrated into the OntoNL Framework, a natural language interface generator for knowledge repositories. The experimentations show a good performance in a number of OWL ontologies.Παρουσιάστηκε στο: Ιn the Proceedings of the 4th European Semantic Web Conference (ESWC), Innsbruck, Austria, 3-7 Jun

The OntoNL framework for natural language interface generation and a domain-specific application.

Summarization: We present in this paper the design and implementation of the OntoNL Framework, a natural language interface generator for knowledge repositories, as well as a natural language system for interactions with multimedia repositories which was built using the OntoNL Framework. The system allows the users to specify natural language requests about the multimedia content with rich semantics that result to digital content delivery. We propose and evaluate a semantic relatedness measure for OWL domain ontologies that concludes to the semantic ranking of ontological, grammatically-related structures. This procedure is used to disambiguate in a particular domain of context and represent in an ontology query language, natural language expressions. The ontology query language that we use is the SPARQL. The construction of the queries is automated and also dependent on the semantic relatedness measurement of ontology concepts. We also present the results of experimentation with the system.Παρουσιάστηκε στο: In the Proceedings of the DELOS Conference on Digital Libraries, Tirrenia, Pisa, Ital