The purpose of this paper is twofold. It describes a new similarity measure which is applied in a new ontology matching algorithm, OntoPhil that exploits both the lexical and structural information of the input ontologies. The different steps of the algorithm as well as some clarifying examples are also provided. In addition, OntoPhil is also compared and evaluated using the datasets from well-known Ontology Alignment Evaluation Initiative.

This paper presents a new ontology matching algorithm, OntoPhil. The algorithm relies on the exploitation of some initial correspondences or binding points that connect the two ontologies used as input. First, it computes these binding points using a new lexical similarity measure which combines the information from a terminological matcher and an external one. Next, it discovers new binding points, by taking the initial ones as basis and by exploiting the specific features of the external structure of the ontologies matched. Finally, the binding points are automatically sifted out to obtain the final alignment. The proposed algorithm was tested on the benchmarks provided by the well known evaluation initiative OAEI, and compared to other matching algorithms. The experimental results show that OntoPhil is an effective approach and outperform other algorithms that share the same principles.

This paper describes a real solution applied to an enterprise of ornamental plant selling and distribution. The platform that we propose uses intelligent agent technologies and ontologies to meet the special needs of an enterprise of this kind. We present the architecture defined with the agents involved in both parties, the plant wholesaler and the plant producers. A description of the ontologies that these agents use to interact is also provided. In the final section some relevant issues detected and conclusions will be presented.

The market of ornamental plants is very important and strategical in Galicia and yet is one of the sectors where fewer technological change has been incorporated in recent years. This paper describes a real solution applied to an enterprise of ornamental plant selling and distribution. The solution that we propose uses intelligent agent technologies and ontologies to meet the special needs of an enterprise of this kind. We present the architecture defined with the agents involved in both parties, the plant wholesaler and the plant producers. A description of the ontologies that these agents use to interact is also provided, as well an example of systems behavior. In the final section some relevant issues detected and conclusions will be presented.

In this paper we introduce our future lines of investigation on the field of Intelligent Agents, focusing on analysing why agents which were well defined according to the guidelines of Agent Oriented Software Engineering, do not show the expected behaviour when they are introduced on a real environment, causing the system to collapse.When addressing to agents malfunctions we will use a metaphor in which agents are considered as patients waiting to be diagnosed. Problems agents show would be equivalent to diseases patients suffer from, and the diagnostic process in agents will also be compared to the procedure followed by physicians when they need to heal patients. Therefore we introduce the concepts of SithGent and SithGent Diagnose, to refer to, sick agents and its diagnostic process.

In recent years, the quality of Phrase-Based Statistical Machine Translation has increased dramatically partially due to the significant increase of available parallel corpus. If we talk in terms of space, this advantage becomes a disadvantage because the increased size of the parallel corpus implies an exponential increase in the size of the translation tables. To solve this problem, there are solutions that reduce the size of the translation tables limiting the length of sentences that are incorporated into the tables. This solution reduces the space, but at the expense of increasing the possibility of worsening the translation of long sentences. In this paper, we propose the compression of the phrase-based translation tables using End-Tagged Dense Code to codify the phrases in source and target languages. The use of this technique allows us to reduce the size of translation tables and therefore it is possible to add longer sentences.