On the dynamic adaptation of language models based on dialogue information

We present an approach to adapt dynamically the language models (LMs) used by a speech recognizer that is part of a spoken dialogue system. We have developed a grammar generation strategy that automatically adapts the LMs using the semantic information that the user provides (represented as dialogue concepts), together with the information regarding the intentions of the speaker (inferred by the dialogue manager, and represented as dialogue goals). We carry out the adaptation as a linear interpolation between a background LM, and one or more of the LMs associated to the dialogue elements (concepts or goals) addressed by the user. The interpolation weights between those models are automatically estimated on each dialogue turn, using measures such as the posterior probabilities of concepts and goals, estimated as part of the inference procedure to determine the actions to be carried out. We propose two approaches to handle the LMs related to concepts and goals. Whereas in the first one we estimate a LM for each one of them, in the second one we apply several clustering strategies to group together those elements that share some common properties, and estimate a LM for each cluster. Our evaluation shows how the system can estimate a dynamic model adapted to each dialogue turn, which helps to improve the performance of the speech recognition (up to a 14.82% of relative improvement), which leads to an improvement in both the language understanding and the dialogue management tasks.

Using Open Geographic Data to Generate Natural Language Descriptions for Hydrological Sensor Networks

Providing descriptions of isolated sensors and sensor networks in natural language, understandable by the general public, is useful to help users find relevant sensors and analyze sensor data. In this paper, we discuss the feasibility of using geographic knowledge from public databases available on the Web (such as OpenStreetMap, Geonames, or DBpedia) to automatically construct such descriptions. We present a general method that uses such information to generate sensor descriptions in natural language. The results of the evaluation of our method in a hydrologic national sensor network showed that this approach is feasible and capable of generating adequate sensor descriptions with a lower development effort compared to other approaches. In the paper we also analyze certain problems that we found in public databases (e.g., heterogeneity, non-standard use of labels, or rigid search methods) and their impact in the generation of sensor descriptions.

Natural language processing as a foundation of the semantic Web

The main argument of this paper is that Natural Language Processing (NLP) does, and will continue to, underlie the Semantic Web (SW), including its initial construction from unstructured sources like the World Wide Web (WWW), whether its advocates realise this or not. Chiefly, we argue, such NLP activity is the only way up to a defensible notion of meaning at conceptual levels (in the original SW diagram) based on lower level empirical computations over usage. Our aim is definitely not to claim logic-bad, NLP-good in any simple-minded way, but to argue that the SW will be a fascinating interaction of these two methodologies, again like the WWW (which has been basically a field for statistical NLP research) but with deeper content. Only NLP technologies (and chiefly information extraction) will be able to provide the requisite RDF knowledge stores for the SW from existing unstructured text databases in the WWW, and in the vast quantities needed. There is no alternative at this point, since a wholly or mostly hand-crafted SW is also unthinkable, as is a SW built from scratch and without reference to the WWW. We also assume that, whatever the limitations on current SW representational power we have drawn attention to here, the SW will continue to grow in a distributed manner so as to serve the needs of scientists, even if it is not perfect. The WWW has already shown how an imperfect artefact can become indispensable.

Functional near infrared spectroscopy study of language, joint attention and motor skills

Near infrared spectroscopy (NIRS) is an emerging non-invasive optical neuro imaging technique that monitors the hemodynamic response to brain activation with ms-scale temporal resolution and sub-cm spatial resolution. The overall goal of my dissertation was to develop and apply NIRS towards investigation of neurological response to language, joint attention and planning and execution of motor skills in healthy adults. Language studies were performed to investigate the hemodynamic response, synchrony and dominance feature of the frontal and fronto-temporal cortex of healthy adults in response to language reception and expression. The mathematical model developed based on granger causality explicated the directional flow of information during the processing of language stimuli by the fronto-temporal cortex. Joint attention and planning/ execution of motor skill studies were performed to investigate the hemodynamic response, synchrony and dominance feature of the frontal cortex of healthy adults and in children (5-8 years old) with autism (for joint attention studies) and individuals with cerebral palsy (for planning/execution of motor skills studies). The joint attention studies on healthy adults showed differences in activation as well as intensity and phase dependent connectivity in the frontal cortex during joint attention in comparison to rest. The joint attention studies on typically developing children showed differences in frontal cortical activation in comparison to that in children with autism. The planning and execution of motor skills studies on healthy adults and individuals with cerebral palsy (CP) showed difference in the frontal cortical dominance, that is, bilateral and ipsilateral dominance, respectively. The planning and execution of motor skills studies also demonstrated the plastic and learning behavior of brain wherein correlation was found between the relative change in total hemoglobin in the frontal cortex and the kinematics of the activity performed by the participants. Thus, during my dissertation the NIRS neuroimaging technique was successfully implemented to investigate the neurological response of language, joint attention and planning and execution of motor skills in healthy adults as well as preliminarily on children with autism and individuals with cerebral palsy. These NIRS studies have long-term potential for the design of early stage interventions in children with autism and customized rehabilitation in individuals with cerebral palsy.

Structured Approaches for Exploring Interpersonal Relationships in Natural Language Text

Human relationships have long been studied by scientists from domains like sociology, psychology, literature, etc. for understanding people's desires, goals, actions and expected behaviors. In this dissertation we study inter-personal relationships as expressed in natural language text. Modeling inter-personal relationships from text finds application in general natural language understanding, as well as real-world domains such as social networks, discussion forums, intelligent virtual agents, etc. We propose that the study of relationships should incorporate not only linguistic cues in text, but also the contexts in which these cues appear. Our investigations, backed by empirical evaluation, support this thesis, and demonstrate that the task benefits from using structured models that incorporate both types of information. We present such structured models to address the task of modeling the nature of relationships between any two given characters from a narrative. To begin with, we assume that relationships are of two types: cooperative and non-cooperative. We first describe an approach to jointly infer relationships between all characters in the narrative, and demonstrate how the task of characterizing the relationship between two characters can benefit from including information about their relationships with other characters in the narrative. We next formulate the relationship-modeling problem as a sequence prediction task to acknowledge the evolving nature of human relationships, and demonstrate the need to model the history of a relationship in predicting its evolution. Thereafter, we present a data-driven method to automatically discover various types of relationships such as familial, romantic, hostile, etc. Like before, we address the task of modeling evolving relationships but don't restrict ourselves to two types of relationships. We also demonstrate the need to incorporate not only local historical but also global context while solving this problem. Lastly, we demonstrate a practical application of modeling inter-personal relationships in the domain of online educational discussion forums. Such forums offer opportunities for its users to interact and form deeper relationships. With this view, we address the task of identifying initiation of such deeper relationships between a student and the instructor. Specifically, we analyze contents of the forums to automatically suggest threads to the instructors that require their intervention. By highlighting scenarios that need direct instructor-student interactions, we alleviate the need for the instructor to manually peruse all threads of the forum and also assist students who have limited avenues for communicating with instructors. We do this by incorporating the discourse structure of the thread through latent variables that abstractly represent contents of individual posts and model the flow of information in the thread. Such latent structured models that incorporate the linguistic cues without losing their context can be helpful in other related natural language understanding tasks as well. We demonstrate this by using the model for a very different task: identifying if a stated desire has been fulfilled by the end of a story.

Our own language: Scots verse translation and the second-generation Scottish renaissance

This thesis examines the role of Scots language verse translation in the second-generation or post-war Scottish Renaissance. The translation of European poetry into Scots was of central importance to the first-generation Scottish Renaissance of the nineteen twenties and thirties. As Margery Palmer McCulloch has shown, the wider cultural climate of Anglo-American modernism was key to MacDiarmid’s conception of the interwar Scottish Renaissance. What was the effect on second-generation poet-translators as the modernist moment passed? Are the many translations undertaken by the younger poets who emerged in the course of the nineteen forties and fifties a faithful reflection of this cultural inheritance? To what extent are they indicative of a new set of priorities and international influences? The five principal translators discussed in this thesis are Douglas Young (1913-1973), Sydney Goodsir Smith (1915-1975), Robert Garioch (1909-1981), Tom Scott (1918-1995) and William J. Tait (1918-1992). Each is the subject of a chapter, in many cases providing the first or most extensive treatment of particular translations. While the pioneering work of John Corbett, Bill Findlay and J. Derrick McClure, among other scholars, has drawn attention to the long history of literary translation into Scots, this thesis is the first extended critical work to take the verse translations of the post-MacDiarmid makars as its subject. The nature and extent of MacDiarmid’s influence is considered throughout, as are the wider discourses around language and translation in twentieth-century Scottish poetry. Critical engagement with a number of key insights from theoretical translation studies helps to situate these writers’ work in its global context. This thesis also explores the ways in which the specific context of Scots translation allows scholars to complicate or expand upon theories of translation developed in other cultural situations (notably Lawrence Venuti’s writing on domestication and foreignisation). The five writers upon whom this thesis concentrates were all highly individual, occasionally idiosyncratic personalities. Young’s polyglot ingenuity finds a foil in Garioch’s sharp, humane wit. Goodsir Smith’s romantic ironising meets its match in Scott’s radical certainty of cause. Tait’s use of the Shetlandic tongue sets him apart. Nonetheless, despite the great variety of style, form and tone shown by each of these translators, this thesis demonstrates that there are meaningful links to be made between them and that they form a unified, coherent group in the wider landscape of twentieth-century Scottish poetry. On the linguistic level, each engaged to some extent in the composition of a ‘synthetic’ or ‘plastic’ language deriving partly from literary sources, partly from the spoken language around them. On a more fundamental level, each was committed to enriching this language through translation, within which a number of key areas of interest emerge. One of the most important of these key areas is Gaelic – especially the poetry of Sorley MacLean, which Young, Garioch and Goodsir Smith all translated into Scots. This is to some extent an act of solidarity on the part of these Scots poets, acknowledging a shared history of marginalisation as well as expressing shared hopes for the future. The same is true of Goodsir Smith’s translations from a number of Eastern European poets (and Edwin Morgan’s own versions, slightly later in the century). The translation of verse drama by poets is another key theme sustained throughout the thesis, with Garioch and Young attempting to fill what they perceived as a gap in the Scots tradition through translation from other languages (another aspect of these writers’ legacy continued by Morgan). Beyond this, all of the writers discussed in this thesis translated extensively from European poetries from Ancient Greece to twentieth-century France. Their reasons for doing so were various, but a certain cosmopolitan idealism figures highly among them. So too does a desire to see Scotland interact with other European nations, thus escaping the potentially narrowing influence of post-war British culture. This thesis addresses the legacy of these writers’ translations, which, it argues, continue to exercise a perceptible influence on the course of poetry in Scotland. This work constitutes a significant contribution to a much-needed wider critical re-assessment of this pivotal period in modern Scottish writing, offering a fresh perspective on the formal and linguistic merits of these poets’ verse translations. Drawing upon frequently obscure book, pamphlet and periodical sources, as well as unpublished manuscripts in the National Library of Scotland and the Shetland Archives, this thesis breaks new ground in its investigation of the role of Scots verse translation in the second-generation Scottish Renaissance.

SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING

Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.

Funtional Near Infrared Spectroscopy Study of Language, Joint Attention and Motor Skills

Near infrared spectroscopy (NIRS) is an emerging non-invasive optical neuro imaging technique that monitors the hemodynamic response to brain activation with ms-scale temporal resolution and sub-cm spatial resolution. The overall goal of my dissertation was to develop and apply NIRS towards investigation of neurological response to language, joint attention and planning and execution of motor skills in healthy adults. Language studies were performed to investigate the hemodynamic response, synchrony and dominance feature of the frontal and fronto-temporal cortex of healthy adults in response to language reception and expression. The mathematical model developed based on granger causality explicated the directional flow of information during the processing of language stimuli by the fronto-temporal cortex. Joint attention and planning/ execution of motor skill studies were performed to investigate the hemodynamic response, synchrony and dominance feature of the frontal cortex of healthy adults and in children (5-8 years old) with autism (for joint attention studies) and individuals with cerebral palsy (for planning/execution of motor skills studies). The joint attention studies on healthy adults showed differences in activation as well as intensity and phase dependent connectivity in the frontal cortex during joint attention in comparison to rest. The joint attention studies on typically developing children showed differences in frontal cortical activation in comparison to that in children with autism. The planning and execution of motor skills studies on healthy adults and individuals with cerebral palsy (CP) showed difference in the frontal cortical dominance, that is, bilateral and ipsilateral dominance, respectively. The planning and execution of motor skills studies also demonstrated the plastic and learning behavior of brain wherein correlation was found between the relative change in total hemoglobin in the frontal cortex and the kinematics of the activity performed by the participants. Thus, during my dissertation the NIRS neuroimaging technique was successfully implemented to investigate the neurological response of language, joint attention and planning and execution of motor skills in healthy adults as well as preliminarily on children with autism and individuals with cerebral palsy. These NIRS studies have long-term potential for the design of early stage interventions in children with autism and customized rehabilitation in individuals with cerebral palsy.

Predire la borsa valori con language model neurali

Nonostante lo scetticismo di molti studiosi circa la possibilità di prevedere l'andamento della borsa valori, esistono svariate teorie ipotizzanti la possibilità di utilizzare le informazioni conosciute per predirne i movimenti futuri. L’avvento dell’intelligenza artificiale nella seconda parte dello scorso secolo ha permesso di ottenere risultati rivoluzionari in svariati ambiti, tanto che oggi tale disciplina trova ampio impiego nella nostra vita quotidiana in molteplici forme. In particolare, grazie al machine learning, è stato possibile sviluppare sistemi intelligenti che apprendono grazie ai dati, riuscendo a modellare problemi complessi. Visto il successo di questi sistemi, essi sono stati applicati anche all’arduo compito di predire la borsa valori, dapprima utilizzando i dati storici finanziari della borsa come fonte di conoscenza, e poi, con la messa a punto di tecniche di elaborazione del linguaggio naturale umano (NLP), anche utilizzando dati in linguaggio naturale, come il testo di notizie finanziarie o l’opinione degli investitori. Questo elaborato ha l’obiettivo di fornire una panoramica sull’utilizzo delle tecniche di machine learning nel campo della predizione del mercato azionario, partendo dalle tecniche più elementari per arrivare ai complessi modelli neurali che oggi rappresentano lo stato dell’arte. Vengono inoltre formalizzati il funzionamento e le tecniche che si utilizzano per addestrare e valutare i modelli di machine learning, per poi effettuare un esperimento in cui a partire da dati finanziari e soprattutto testuali si tenterà di predire correttamente la variazione del valore dell’indice di borsa S&P 500 utilizzando un language model basato su una rete neurale.

Bio-QA-GNN: Reasoning with Language Models and Knowledge Graphs for Interpretable Biomedical Question Answering

Nowadays the idea of injecting world or domain-specific structured knowledge into pre-trained language models (PLMs) is becoming an increasingly popular approach for solving problems such as biases, hallucinations, huge architectural sizes, and explainability lack—critical for real-world natural language processing applications in sensitive fields like bioinformatics. One recent work that has garnered much attention in Neuro-symbolic AI is QA-GNN, an end-to-end model for multiple-choice open-domain question answering (MCOQA) tasks via interpretable text-graph reasoning. Unlike previous publications, QA-GNN mutually informs PLMs and graph neural networks (GNNs) on top of relevant facts retrieved from knowledge graphs (KGs). However, taking a more holistic view, existing PLM+KG contributions mainly consider commonsense benchmarks and ignore or shallowly analyze performances on biomedical datasets. This thesis start from a propose of a deep investigation of QA-GNN for biomedicine, comparing existing or brand-new PLMs, KGs, edge-aware GNNs, preprocessing techniques, and initialization strategies. By combining the insights emerged in DISI's research, we introduce Bio-QA-GNN that include a KG. Working with this part has led to an improvement in state-of-the-art of MCOQA model on biomedical/clinical text, largely outperforming the original one (+3.63\% accuracy on MedQA). Our findings also contribute to a better understanding of the explanation degree allowed by joint text-graph reasoning architectures and their effectiveness on different medical subjects and reasoning types. Codes, models, datasets, and demos to reproduce the results are freely available at: \url{https://github.com/disi-unibo-nlp/bio-qagnn}.

The invisible factors that can make the difference. Language management and knowledge transfer in multinational sites

The knowledge-based society we live in has stressed the importance of human capital and brought talent to the top of most wanted skills, especially to companies who want to succeed in turbulent environments worldwide. In fact, streams, sequences of decisions and resource commitments characterize the day-to-day of multinational companies (MNCs). Such decision-making activities encompass major strategic moves like internationalization and new market entries or diversification and acquisitions. In most companies, these strategic decisions are extensively discussed and debated and are generally framed, formulated, and articulated in specialized language often developed by the best minds in the company. Yet the language used in such deliberations, in detailing and enacting the implementation strategy is usually taken for granted and receives little if any explicit attention (Brannen & Doz, 2012) an can still be a “forgotten factor” (Marschan et al. 1997). Literature on language management and international business refers to lack of awareness of business managers of the impact that language can have not only in communication effectiveness but especially in knowledge transfer and knowledge management in business environments. In the context of MNCs, management is, for many different reasons, more complex and demanding than that of a national company, mainly because of diversity factors inherent to internationalization, namely geographical and cultural spaces, i.e, varied mindsets. Moreover, the way of functioning, and managing language, of the MNC depends on its vision, its values and its internationalization model, i.e on in the way the MNE adapts to and controls the new markets, which can vary essentially from a more ethnocentric to a more pluricentric focus. Regardless of the internationalization model followed by the MNC, communication between different business units is essential to achieve unity in diversity and business sustainability. For the business flow and prosperity, inter-subsidiary, intra-company and company-client (customers, suppliers, governments, municipalities, etc..) communication must work in various directions and levels of the organization. If not well managed, this diversity can be a barrier to global coordination and create turbulent environments, even if a good technological support is available (Feely et al., 2002: 4). According to Marchan-Piekkari (1999) the tongue can be both (i) a barrier, (ii) a facilitator and (iii) a source of power. Moreover, the lack of preparation for the barriers of linguistic diversity can lead to various costs, including negotiations’ failure and failure on internationalization.. On the other hand, communication and language fluency is not just a message transfer procedure, but above all a knowledge transfer process, which requires extra-linguistic skills (persuasion, assertiveness …) in order to promote credibility of both parties. For this reason, MNCs need a common code to communicate and trade information inside and outside the company, which will require one or more strategies, in order to overcome possible barriers and organization distortions.

Terminology and knowledge engineering conference. New frontiers in the constructive symbiosis of terminology and knowledge engineering

To meet the increasing demands of the complex inter-organizational processes and the demand for continuous innovation and internationalization, it is evident that new forms of organisation are being adopted, fostering more intensive collaboration processes and sharing of resources, in what can be called collaborative networks (Camarinha-Matos, 2006:03). Information and knowledge are crucial resources in collaborative networks, being their management fundamental processes to optimize. Knowledge organisation and collaboration systems are thus important instruments for the success of collaborative networks of organisations having been researched in the last decade in the areas of computer science, information science, management sciences, terminology and linguistics. Nevertheless, research in this area didn’t give much attention to multilingual contexts of collaboration, which pose specific and challenging problems. It is then clear that access to and representation of knowledge will happen more and more on a multilingual setting which implies the overcoming of difficulties inherent to the presence of multiple languages, through the use of processes like localization of ontologies. Although localization, like other processes that involve multilingualism, is a rather well-developed practice and its methodologies and tools fruitfully employed by the language industry in the development and adaptation of multilingual content, it has not yet been sufficiently explored as an element of support to the development of knowledge representations - in particular ontologies - expressed in more than one language. Multilingual knowledge representation is then an open research area calling for cross-contributions from knowledge engineering, terminology, ontology engineering, cognitive sciences, computational linguistics, natural language processing, and management sciences. This workshop joined researchers interested in multilingual knowledge representation, in a multidisciplinary environment to debate the possibilities of cross-fertilization between knowledge engineering, terminology, ontology engineering, cognitive sciences, computational linguistics, natural language processing, and management sciences applied to contexts where multilingualism continuously creates new and demanding challenges to current knowledge representation methods and techniques. In this workshop six papers dealing with different approaches to multilingual knowledge representation are presented, most of them describing tools, approaches and results obtained in the development of ongoing projects. In the first case, Andrés Domínguez Burgos, Koen Kerremansa and Rita Temmerman present a software module that is part of a workbench for terminological and ontological mining, Termontospider, a wiki crawler that aims at optimally traverse Wikipedia in search of domainspecific texts for extracting terminological and ontological information. The crawler is part of a tool suite for automatically developing multilingual termontological databases, i.e. ontologicallyunderpinned multilingual terminological databases. In this paper the authors describe the basic principles behind the crawler and summarized the research setting in which the tool is currently tested. In the second paper, Fumiko Kano presents a work comparing four feature-based similarity measures derived from cognitive sciences. The purpose of the comparative analysis presented by the author is to verify the potentially most effective model that can be applied for mapping independent ontologies in a culturally influenced domain. For that, datasets based on standardized pre-defined feature dimensions and values, which are obtainable from the UNESCO Institute for Statistics (UIS) have been used for the comparative analysis of the similarity measures. The purpose of the comparison is to verify the similarity measures based on the objectively developed datasets. According to the author the results demonstrate that the Bayesian Model of Generalization provides for the most effective cognitive model for identifying the most similar corresponding concepts existing for a targeted socio-cultural community. In another presentation, Thierry Declerck, Hans-Ulrich Krieger and Dagmar Gromann present an ongoing work and propose an approach to automatic extraction of information from multilingual financial Web resources, to provide candidate terms for building ontology elements or instances of ontology concepts. The authors present a complementary approach to the direct localization/translation of ontology labels, by acquiring terminologies through the access and harvesting of multilingual Web presences of structured information providers in the field of finance, leading to both the detection of candidate terms in various multilingual sources in the financial domain that can be used not only as labels of ontology classes and properties but also for the possible generation of (multilingual) domain ontologies themselves. In the next paper, Manuel Silva, António Lucas Soares and Rute Costa claim that despite the availability of tools, resources and techniques aimed at the construction of ontological artifacts, developing a shared conceptualization of a given reality still raises questions about the principles and methods that support the initial phases of conceptualization. These questions become, according to the authors, more complex when the conceptualization occurs in a multilingual setting. To tackle these issues the authors present a collaborative platform – conceptME - where terminological and knowledge representation processes support domain experts throughout a conceptualization framework, allowing the inclusion of multilingual data as a way to promote knowledge sharing and enhance conceptualization and support a multilingual ontology specification. In another presentation Frieda Steurs and Hendrik J. Kockaert present us TermWise, a large project dealing with legal terminology and phraseology for the Belgian public services, i.e. the translation office of the ministry of justice, a project which aims at developing an advanced tool including expert knowledge in the algorithms that extract specialized language from textual data (legal documents) and whose outcome is a knowledge database including Dutch/French equivalents for legal concepts, enriched with the phraseology related to the terms under discussion. Finally, Deborah Grbac, Luca Losito, Andrea Sada and Paolo Sirito report on the preliminary results of a pilot project currently ongoing at UCSC Central Library, where they propose to adapt to subject librarians, employed in large and multilingual Academic Institutions, the model used by translators working within European Union Institutions. The authors are using User Experience (UX) Analysis in order to provide subject librarians with a visual support, by means of “ontology tables” depicting conceptual linking and connections of words with concepts presented according to their semantic and linguistic meaning. The organizers hope that the selection of papers presented here will be of interest to a broad audience, and will be a starting point for further discussion and cooperation.

Model morphisms (MoMo) to enable language independent information models and interoperable business networks

MSc. Dissertation presented at Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa to obtain the Master degree in Electrical and Computer Engineering

Formal Contracts for Runtime Verification Support in the Ada Programming Language

Poster presented in The 28th GI/ITG International Conference on Architecture of Computing Systems (ARCS 2015). 24 to 26, Mar, 2015. Porto, Portugal.