Metaheurística particle swarm utilizada para alocação ótima de bancos de capacitores em sistemas de distribuição radial

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Simulação numérica de escoamentos hipersônicos sobre corpos rombudos pelo método de elementos finitos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Redução de vibrações de rotores utilizando atuadores magnéticos e sistema de controle feedforward

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estabilidade, desempenho e observação de estado de sistemas chaveados afins: aplicações aos conversores CC-CC

Pós-graduação em Engenharia Elétrica - FEIS

Desempenho da tilápia-do-Nilo arraçoada com dietas contendo farinha de sangue bovino atomizado ou convencional

The study evaluated the performance and carcass composition index of Nile tilapia (Oreochromis niloticus) fed with diets containing increasing levels of spray-dried blood meal (SDBM) and vat-dried blood meal (VDBM) and formulated based on digestible amino acids. Two hundred and fifty-two fingerlings were distributed in a completely randomized design, in a (2 x 4) + 1 factorial model, two types of blood meal with four levels of each blood meal in the diet, and a control diet (without blood meal), with four replications. The treatments consisted of soybean meal-based control diet, with 34% digestible protein (DP) and 3,200 kcal of digestible energy kg-1 (DE), plus four diets formulated with SDBM and four diets with VDBM, containing 5, 10, 15 and 20% of each meal in feed, maintaining identical DP, DE, phosphorus, calcium, lysine, methionine, threonine and tryptophan levels as those of the control diet. The results show that it is possible to use up to 15% VDBM in diets of Nile tilapia (O. niloticus) between 5 to 150 g of body weight.

Despacho ótimo de potências ativa e reativa de sistema elétricos multi-áreas considerando restrições físicas, econômicas e ambientais =: Envronmentally constrained active-reactive optimal power flow-a compromising strategy for economic-emission dispatch and a multi-area paradigm

Pós-graduação em Engenharia Elétrica - FEIS

Dynamic Routing in Translucent WDM Optical Networks

Translucent WDM optical networks use sparse placement of regenerators to overcome the impairments and wavelength contention introduced by fully transparent networks, and achieve a performance close to fully opaque networks with much less cost. Our previous study proved the feasibility of translucent networks using sparse regeneration technique. We addressed the placement of regenerators based on static schemes allowing only fixed number of regenerators at fixed locations. This paper furthers the study by proposing a suite of dynamical routing schemes. Dynamic allocation, advertisement and discovery of regeneration resources are proposed to support sharing transmitters and receivers between regeneration and access functions. This study follows the current trend in optical networking industry by utilizing extension of IP control protocols. Dynamic routing algorithms, aware of current regeneration resources and link states, are designed to smartly route the connection requests under quality constraints. A hierarchical network model, supported by the MPLS-based control plane, is also proposed to provide scalability. Experiments show that network performance is improved without placement of extra regenerators.

La qualità erogata da un'azienda di trasporto collettivo in ambito urbano: calcolo del livello di servizio delle principali linee di ATC (BO)

Oggetto di questa tesi è lo studio della qualità del servizio di trasporto erogato che condiziona la qualità percepita dall’utente, poiché spesso proprio a causa di un errato processo di pianificazione e gestione della rete, molte aziende non sono in grado di consolidare un alto livello di efficienza che permetta loro di attrarre e servire la crescente domanda. Per questo motivo, si è deciso di indagare sugli aspetti che determinano la qualità erogata e sui fattori che la influenzano, anche attraverso la definizione di alcuni indicatori rappresentativi del servizio erogato. L’area di studio considerata è stata quella urbana di Bologna, e sono state prese in esame due linee di ATC, la 19 e la 27, caratterizzate entrambe da una domanda di trasporto molto elevata. L’interesse è ricaduto in modo particolare sugli aspetti legati alla regolarità del servizio, ovvero al rispetto della cadenza programmata delle corse e alla puntualità, ossia il rispetto dell’orario programmato delle stesse. Proprio da questi due aspetti, infatti, dipende in larga misura la percezione della qualità che gli utenti hanno del servizio di trasporto collettivo. Lo studio è stato condotto sulla base di dati raccolti attraverso due campagne di rilevamento, una effettuata nel mese di maggio dell’anno 2008 e l’altra nel mese di settembre dello stesso anno. La scelta del periodo, della zona e delle modalità di rilevamento è strettamente connessa all’obiettivo prefissato. Il servizio è influenzato dalle caratteristiche del sistema di trasporto: sia da quelle legate alla domanda che da quelle legate all’offerta. Nel caso della domanda di trasporto si considera l’influenza sul servizio del numero di passeggeri saliti e del tempo di sosta alle fermate. Nel caso dell’offerta di trasporto si osservano soprattutto gli aspetti legati alla rete di trasporto su cui si muovono gli autobus, analizzando quindi i tempi di movimento e le velocità dei mezzi, per vedere come le caratteristiche dell’infrastruttura possano condizionare il servizio. A tale proposito è opportuno dire che, mentre i dati della prima analisi ci sono utili per lo studio dell’influenza del tempo di sosta sull’intertempo, nella seconda analisi si vuole cercare di effettuare ulteriori osservazioni sull’influenza del tempo di movimento sulla cadenza, prendendo in esame altri elementi, come ad esempio tratti di linea differenti rispetto al caso precedente. Un’attenzione particolare, inoltre, verrà riservata alla verifica del rispetto della cadenza, dalla quale scaturisce la definizione del livello di servizio per ciò che riguarda la regolarità. Per quest’ultima verrà, inoltre, determinato anche il LOS relativo alla puntualità. Collegato al problema del rispetto della cadenza è il fenomeno dell’accodamento: questo si verifica quando i mezzi di una stessa linea arrivano contemporaneamente ad una fermata uno dietro l’altro. L’accodamento ha, infatti, origine dal mancato rispetto della cadenza programmata tra i mezzi ed è un’evidente manifestazione del mal funzionamento di un servizio di trasporto. Verrà infine condotta un’analisi dei fattori che possono influenzare le prestazioni del servizio di trasporto pubblico, così da collocare i dati ottenuti dalle operazioni di rilevamento in un quadro più preciso, capace di sottolineare alcuni elementi di criticità e possibili rapporti di causalità.

Mobi - kids - rischio di tumori cerebrali ed esposizione a campi a radiofrequenza nei bambini e negli adolescenti

Introduzione. Il rapido e globale incremento dell’utilizzo dei telefoni cellulari da parte degli adolescenti e dei bambini ha generato un considerevole interesse circa i possibili effetti sulla salute dell’esposizione a campi elettromagnetici a radiofrequenza. Perciò è stato avviato lo studio internazionale caso-controllo Mobi-kids, all’interno del quale si colloca quello italiano condotto in 4 Regioni (Piemonte, Lombardia, Toscana, Emilia-Romagna). Obiettivi. Lo studio ha come obiettivo quello di valutare la stima del rischio degli effetti potenzialmente avversi di queste esposizioni sul sistema nervoso centrale nei bambini e negli adolescenti. Materiali e Metodi. La popolazione include tutte le persone di età compresa tra 10 e 24 anni residenti nelle 4 Regioni, con una diagnosi confermata di neoplasia cerebrale primitiva, diagnosticata durante il periodo di studio (3 anni). Sono stati selezionati due controlli - ospedalizzati per appendicite acuta - per ciascun caso. I controlli sono stati appaiati individualmente a ciascun caso per età, sesso e residenza del caso. Risultati. In Italia sono stati intervistati a Giugno 2014, 106 casi e 191 controlli. In Emilia-Romagna i casi reclutati sono stati fino ad ora 21 e i controlli 20, con una rispondenza del’81% e dell’65% rispettivamente. Dei 41 soggetti totali, il 61% era di sesso maschile con un’età media generale pari a 16,5 (±4,5) anni. Inoltre il 44% degli intervistati proveniva dalla classe di età più giovane (10-14). In merito allo stato di appaiamento, nella nostra Regione sono state effettuate 7 triplette (33%) - 1 caso e 2 controlli - e 6 doppiette (29%) - 1 caso ed 1 controllo. Conclusioni. Nonostante le varie difficoltà affrontate data la natura del progetto, l’esperienza maturata fin ad ora ha comunque portato alla fattibilità dello studio e porterà probabilmente a risultati che contribuiranno alla comprensione dei potenziali rischi di neoplasie cerebrali associati all'uso di telefoni cellulari tra i giovani.

EVALUATION OF MAGNESIUM AS A HALL THRUSTER PROPELLANT

In this study, the use of magnesium as a Hall thruster propellant was evaluated. A xenon Hall thruster was modified such that magnesium propellant could be loaded into the anode and use waste heat from the thruster discharge to drive the propellant vaporization. A control scheme was developed, which allowed for precise control of the mass flow rate while still using plasma heating as the main mechanism for evaporation. The thruster anode, which also served as the propellant reservoir, was designed such that the open area was too low for sufficient vapor flow at normal operating temperatures (i.e. plasma heating alone). The remaining heat needed to achieve enough vapor flow to sustain thruster discharge came from a counter-wound resistive heater located behind the anode. The control system has the ability to arrest thermal runaway in a direct evaporation feed system and stabilize the discharge current during voltage-limited operation. A proportional-integral-derivative control algorithm was implemented to enable automated operation of the mass flow control system using the discharge current as the measured variable and the anode heater current as the controlled parameter. Steady-state operation at constant voltage with discharge current excursions less than 0.35 A was demonstrated for 70 min. Using this long-duration method, stable operation was achieved with heater powers as low as 6% of the total discharge power. Using the thermal mass flow control system the thruster operated stably enough and long enough that performance measurements could be obtained and compared to the performance of the thruster using xenon propellant. It was found that when operated with magnesium, the thruster has thrust ranging from 34 mN at 200 V to 39 mN at 300 V with 1.7 mg/s of propellant. It was found to have 27 mN of thrust at 300 V using 1.0 mg/s of propellant. The thrust-to-power ratio ranged from 24 mN/kW at 200 V to 18 mN/kW at 300 volts. The specific impulse was 2000 s at 200 V and upwards of 2700 s at 300 V. The anode efficiency was found to be ~23% using magnesium, which is substantially lower than the 40% anode efficiency of xenon at approximately equivalent molar flow rates. Measurements in the plasma plume of the thruster—operated using magnesium and xenon propellants—were obtained using a Faraday probe to measure off-axis current distribution, a retarding potential analyzer to measure ion energy, and a double Langmuir probe to measure plasma density, electron temperature, and plasma potential. Additionally, the off axis current distributions and ion energy distributions were compared to measurements made in krypton and bismuth plasmas obtained in previous studies of the same thruster. Comparisons showed that magnesium had the largest beam divergence of the four propellants while the others had similar divergence. The comparisons also showed that magnesium and krypton both had very low voltage utilization compared to xenon and bismuth. It is likely that the differences in plume structure are due to the atomic differences between the propellants; the ionization mean free path goes down with increasing atomic mass. Magnesium and krypton have long ionization mean free paths and therefore require physically larger thruster dimensions for efficient thruster operation and would benefit from magnetic shielding.

NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.

Recent improvements in antigene technology

DNA triple helix based approaches to control and modulate cellular functions on the level of genomic DNA (antigene technology) suffered in the past from a stepmother like treatment in comparison to the flourishing field of oligonucleotide based control of translation (antisense technology). This was mostly due to lack of affinity of triplex forming oligonucleotides (TFOs) to their DNA target, to sequence restriciton constraints imposed by the triple helical recogniton motifs and by open questions to the accessibility of the target DNA. Recent developments in the area have brought about new bases that specifically recognize pyrimidine-purine inversion sites as well as sugar modifications, e.g. the 2'-aminoethoxy-oligonucleotides or oligonucleotides based on the locked nucleic acid (LNA) sugar unit, that greatly enhance triplex stability and alleviate in part the sequence restriction constraints. With this, sequence specific genomic DNA manipulation starts to become a useful tool in biotechnology

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.

Catadioptric panoramic stereovision for humanoid robots

This paper proposes a novel design of a reconfigurable humanoid robot head, based on biological likeness of human being so that the humanoid robot could agreeably interact with people in various everyday tasks. The proposed humanoid head has a modular and adaptive structural design and is equipped with three main components: frame, neck motion system and omnidirectional stereovision system modules. The omnidirectional stereovision system module being the last module, a motivating contribution with regard to other computer vision systems implemented in former humanoids, it opens new research possibilities for achieving human-like behaviour. A proposal for a real-time catadioptric stereovision system is presented, including stereo geometry for rectifying the system configuration and depth estimation. The methodology for an initial approach for visual servoing tasks is divided into two phases, first related to the robust detection of moving objects, their depth estimation and position calculation, and second the development of attention-based control strategies. Perception capabilities provided allow the extraction of 3D information from a wide range of visions from uncontrolled dynamic environments, and work results are illustrated through a number of experiments.

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the triple structure recommended for annotations in these works (which is isomorphic to the triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which  Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO;  Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. • The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. • These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. • In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag’s annotation scheme and OntoTagger’s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).