Rafael Valencia-García

Social knowledge-based recommender system. Application to the movies domain

With the advent of the Social Web and the growing popularity of Web 2.0 applications, recommender systems are gaining momentum. The recommendations generated by these systems aim to provide end users with suggestions about information items, social elements, products or services that are likely to be of their interest. The traditional syntactic-based recommender systems suffer from a number of shortcomings that hamper their effectiveness. As semantic technologies mature, they provide a consistent and reliable basis for dealing with data at the knowledge level. Adding semantically empowered techniques to recommender systems can significantly improve the overall quality of recommendations. In this work, a hybrid recommender system based on knowledge and social networks is presented. Its evaluation in the cinematographic domain yields very promising results compared to state-of-the-art solutions.

An ontology, intelligent agent-based framework for the provision of semantic web services

Intelligent agents and semantic web services are two technologies with great potential. Striking new applications can be developed by using the tools and techniques they provide. However, semantic web services need for an upper software entity able to deal with them and, on the other hand agent technology has historically suffered from a number of drawbacks that must be addressed. Integrating these two technologies in a joint environment can overcome their problems while strengthening their advantages. In this paper, the necessity for integrating these technologies and the potential benefits of their combination are analyzed. Based on this study, we present SEMMAS, an ontology-based framework for seamlessly integrating Intelligent Agents and Semantic Web Services. The basics of the framework are detailed and a proof-of-concept implementation described.

A Systematic Review and Comparison of Security Ontologies

The use of ontologies for representing knowledge provides us with organization, communication and reusability. Information security is a serious requirement which must be carefully considered. Concepts and relations managed by any scientific community need to be formally defined and ontological engineering supports their definition. In this paper, the method of systematic review is applied with the purpose of identifying, extracting and analyzing the main proposals for security ontologies. The main identified proposals are compared using a formal framework and we conclude by stating their early state of development and the need of additional research efforts.

Ontology-based annotation and retrieval of services in the cloud

Cloud computing is a technological paradigm that permits computing services to be offered over the Internet. This new service model is closely related to previous well-known distributed computing initiatives such as Web services and grid computing. In the current socio-economic climate, the affordability of cloud computing has made it one of the most popular recent innovations. This has led to the availability of more and more cloud services, as a consequence of which it is becoming increasingly difficult for service consumers to find and access those cloud services that fulfil their requirements. In this paper, we present a semantically-enhanced platform that will assist in the process of discovering the cloud services that best match user needs. This fully-fledged system encompasses two basic functions: the creation of a repository with the semantic description of cloud services and the search for services that accomplish the required expectations. The cloud services semantic repository is generated by means of an automatic tool that first annotates the cloud service descriptions with semantic content and then creates a semantic vector for each service. The comprehensive evaluation of the tool in the ICT domain has led to very promising results that outperform state-of-the-art solutions in similarly broad domains.

Combining Semantic Web technologies with Multi-Agent Systems for integrated access to biological resources

The increasing volume and diversity of information in biomedical research is demanding new approaches for data integration in this domain. Semantic Web technologies and applications can leverage the potential of biomedical information integration and discovery, facing the problem of semantic heterogeneity of biomedical information sources. In such an environment, agent technology can assist users in discovering and invoking the services available on the Internet. In this paper we present SEMMAS, an ontology-based, domain-independent framework for seamlessly integrating Intelligent Agents and Semantic Web Services. Our approach is backed with a proof-of-concept implementation where the breakthrough and efficiency of integrating disparate biomedical information sources have been tested.

OWLPath: An OWL Ontology-Guided Query Editor

Most Semantic Web technology-based applications need users to have a deep background on the formal underpinnings of ontology languages and some basic skills in these technologies. Generally, only experts in the field meet these requirements. In this paper, we present OWLPath, a natural language-query editor guided by multilanguage OWL-formatted ontologies. This application allows nonexpert users to easily create SPARQL queries that can be issued over most existing ontology storage systems. Our approach is a fully fledged solution backed with a proof-of-concept implementation and the empirical results of two challenging use cases: one in the domain of e-finance and the other in e-tourism.

Financial news semantic search engine

An increasingly large amount of financial information available in a number of heterogeneous business sources implies that the traditional methods of analysis are no longer applicable. These financial data sources are characterized by the use of disparate data models and their unstructured content with implicit knowledge. In addition, the most up-to-date financial information typically resides in the vast amount of financial-related news that brokers take into account when investing. As Semantic Technologies mature, they provide a consistent and reliable basis for the development of superior, more precise mechanisms to deal with heterogeneous data. In this paper, we present a financial news semantic search engine based on Semantic Web technologies. The search engine is accompanied by an ontology population tool that assists in keeping the financial ontology up-to-date. In addition, a further module has been developed that is capable of crawling the Web in search of financial news and annotating it with knowledge entities from the financial ontology that match with the contents of the news. Our contribution is an overall solution based on a fully fledged architecture that has been validated in a use case scenario for the Spanish stock exchange.

Basis for an integrated security ontology according to a systematic review of existing proposals

The use of ontologies to represent knowledge provides us with organization, communication and reusability. The concepts and relations managed by any scientific community need to be formally defined. Since security in information technologies has evolved as a critical aspect and many related topics have been developed, this paper applies the method of systematic review for identifying, extracting and analyzing the principal proposals for security ontologies. The most mature proposals have been selected and compared by using a formal framework, extracting the key requirements that an integrated and unified security ontology should have, and providing the first steps towards its definition.

Measuring individual learning performance in group work from a knowledge integration perspective

Evaluation is an important part of the teaching-learning process, and it becomes more difficult when individuals are developing a joint project and individual marks have to be assigned to the group members. Different strategies can be used to perform this task. In this work, an approach that combines the global group results and the individual performance is presented. This approach makes use of a semantic framework to rank the individual participation of each group member and to compare their results with those they should have obtained to achieve the final mark. An experiment performed in real settings is also reported in this paper.

Ontology learning from biomedical natural language documents using UMLS

The generation of new knowledge is continuous in biomedical domains, thus biomedical literature is becoming harder to understand. Ontologies provide vocabulary standardization, so they can be helpful to facilitate the understanding of biomedical texts. In this work, a methodology for building biomedical ontologies from texts is presented. This approach relies on natural language processing and incremental knowledge acquisition techniques to obtain the relevant concepts and relations to be included in an OWL ontology. Additionally, we provide an algorithm to connect the isolated concepts regions in the ontology using UMLS. We also discuss in this paper the experiment carried out to validate our approach and its positive results in terms of performance and scalability.