Geochemical composition and crystallization conditions of melts from the Mid Atlantic Ridge near Ascension Island (7-11°S)

Phase equilibria simulations were performed on naturally quenched basaltic glasses to determine crystallization conditions prior to eruption of magmas at the Mid-Atlantic Ridge (MAR) east of Ascension Island (7°11°S).The results indicate that midocean ridge basalt (MORB) magmas beneath different segments of the MAR have crystallized over a wide range of pressures (100-900MPa). However, each segment seems to have a specific crystallization history. Nearly isobaric crystallization conditions (100-300MPa) were obtained for the geochemically enriched MORB magmas of the central segments, whereas normal (N)-MORB magmas of the bounding segments are characterized by polybaric crystallization conditions (200-900MPa). In addition, our results demonstrate close to anhydrous crystallization conditions of N-MORBs, whereas geochemically enriched MORBs were successfully modeled in the presence of 0.4-1wt% H2O in the parental melts.These estimates are in agreement with direct (Fourier transform IR) measurements of H2O abundances in basaltic glasses and melt inclusions for selected samples. Water contents determined in the parental melts are in the range 0.04-0.09 and 0.30-0.55 wt% H2O for depleted and enriched MORBs, respectively. Our results are in general agreement (within ±200MPa) with previous approaches used to evaluate pressure estimates in MORB. However, the determination of pre-eruptive conditions of MORBs, including temperature and water content in addition to pressure, requires the improvement of magma crystallization models to simulate liquid lines of descent in the presence of small amounts of water. KEY WORDS: MORB; Mid-Atlantic Ridge; depth of crystallization; water abundances; phase equilibria calculations; cotectic crystallization; pressure estimates; polybaric fractionation

Engineering an Ontology for Autonomous Systems - The OASys Ontology

This paper describes the development of an ontology for autonomous systems, as the initial stage of a research programe on autonomous systems’ engineering within a model-based control approach. The ontology aims at providing a unified conceptual framework for the autonomous systems’ stakeholders, from developers to software engineers. The modular ontology contains both generic and domain-specific concepts for autonomous systems description and engineering. The ontology serves as the basis in a methodology to obtain the autonomous system’s conceptual models. The objective is to obtain and to use these models as main input for the autonomous system’s model-based control system.

Essentials in Ontology Engineering: Methodologies, Languages, and Tools

In the beginning of the 90s, ontology development was similar to an art: ontology developers did not have clear guidelines on how to build ontologies but only some design criteria to be followed. Work on principles, methods and methodologies, together with supporting technologies and languages, made ontology development become an engineering discipline, the so-called Ontology Engineering. Ontology Engineering refers to the set of activities that concern the ontology development process and the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. Thanks to the work done in the Ontology Engineering field, the development of ontologies within and between teams has increased and improved, as well as the possibility of reusing ontologies in other developments and in final applications. Currently, ontologies are widely used in (a) Knowledge Engineering, Artificial Intelligence and Computer Science, (b) applications related to knowledge management, natural language processing, e-commerce, intelligent information integration, information retrieval, database design and integration, bio-informatics, education, and (c) the Semantic Web, the Semantic Grid, and the Linked Data initiative. In this paper, we provide an overview of Ontology Engineering, mentioning the most outstanding and used methodologies, languages, and tools for building ontologies. In addition, we include some words on how all these elements can be used in the Linked Data initiative.

'Enhancement of the crystallization process of TbxFe1-x thin films upon the formation of alpha-Tb phase'

TbxFe1−x thin films deposited by sputtering on Mo were investigated structurally and magnetically. The microstructure consists of TbFe2 nanoparticles embedded in an amorphous matrix, and the Tb content can be correlated with an increase in the volume of these nanoparticles. Similar microstructure and behavior were found when TbFe2 was deposited on glass and on a Pt buffer layer. Nevertheless, thermal treatments promote a different effect, depending on the mechanical stiffness of the buffer layer. The layers deposited on Mo, a rigid material, show crystalline TbFe2 together with α-Tb phase upon thermal treatment. In contrast, TbFe2 does not crystallize properly on Pt, a material with a lower stiffness than Mo. Intermediate results were observed on the film deposited on glass. Experimental results show the impact of the buffer stiffness on the crystallization process. Moreover, the formation of α-Tb appears to be fundamental to crystallized TbFe2 on layers deposited on rigid buffers

Mapping of the Music Ontology to the Media Value Chain Ontology and the PROV Ontology

Mapping of the Music Ontology to the Media Value Chain Ontology and the PROV Ontology

The Landscape of Ontology Reuse in Linked Data

Abstract. The uptake of Linked Data (LD) has promoted the proliferation of datasets and their associated ontologies for describing different domains. Ac-cording to LD principles, developers should reuse as many available terms as possible to describe their data. Importing ontologies or referring to their terms’ URIs are the two main ways to reuse knowledge from available ontologies. In this paper, we have analyzed 18589 terms appearing within 196 ontologies in-cluded in the Linked Open Vocabularies (LOV) registry with the aim of under-standing the current state of ontology reuse in the LD context. In order to char-acterize the landscape of ontology reuse in this context, we have extracted sta-tistics about currently reused elements, calculated ratios for reuse, and drawn graphs about imports and references between ontologies. Keywords: ontology, vocabulary, reuse, linked data, ontology import

Cross-lingual ontology matching as a challenge for the Multilingual Semantic Web

Recently, the Semantic Web has experienced significant advancements in standards and techniques, as well as in the amount of semantic information available online. Nevertheless, mechanisms are still needed to automatically reconcile information when it is expressed in different natural languages on the Web of Data, in order to improve the access to semantic information across language barriers. In this context several challenges arise [1], such as: (i) ontology translation/localization, (ii) cross-lingual ontology mappings, (iii) representation of multilingual lexical information, and (iv) cross-lingual access and querying of linked data. In the following we will focus on the second challenge, which is the necessity of establishing, representing and storing cross-lingual links among semantic information on the Web. In fact, in a “truly” multilingual Semantic Web, semantic data with lexical representations in one natural language would be mapped to equivalent or related information in other languages, thus making navigation across multilingual information possible for software agents.

Using Cross-Lingual Explicit Semantic Analysis for Improving Ontology Translation

Semantic Web aims to allow machines to make inferences using the explicit conceptualisations contained in ontologies. By pointing to ontologies, Semantic Web-based applications are able to inter-operate and share common information easily. Nevertheless, multilingual semantic applications are still rare, owing to the fact that most online ontologies are monolingual in English. In order to solve this issue, techniques for ontology localisation and translation are needed. However, traditional machine translation is difficult to apply to ontologies, owing to the fact that ontology labels tend to be quite short in length and linguistically different from the free text paradigm. In this paper, we propose an approach to enhance machine translation of ontologies based on exploiting the well-structured concept descriptions contained in the ontology. In particular, our approach leverages the semantics contained in the ontology by using Cross Lingual Explicit Semantic Analysis (CLESA) for context-based disambiguation in phrase-based Statistical Machine Translation (SMT). The presented work is novel in the sense that application of CLESA in SMT has not been performed earlier to the best of our knowledge.

Did you validate your ontology? OOPS!

The application of methodologies for building ontologies can im-prove ontology quality. However, such quality is not guaranteed because of the difficulties involved in ontology modelling. These difficulties are related to the inclusion of anomalies or bad practices within the ontology development. Sev-eral authors have provided lists of typical anomalies detected in ontologies dur-ing the last decade. In this context, our aim in this paper is to describe OOPS! (OntOlogy Pitfall Scanner!), a tool for detecting pitfalls in ontologies.

MultiFarm: A benchmark for multilingual ontology matching

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish – we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism.

OPPL-Galaxy, a Galaxy tool for enhancing ontology exploitation as part of bioinformatics workflows

Biomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists’ toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment. Results We present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy’s capability for enriching, modifying and querying biomedical ontologies. Conclusions Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses.