SMArc: a proposal for a smart, semantic middleware architecture focused on Smart City energy management

Among the main features that are intended to become part of what can be expected from the Smart City, one of them should be an improved energy management system, in order to benefit from a healthier relation with the environment, minimize energy expenses, and offer dynamic market opportunities. A Smart Grid seems like a very suitable infrastructure for this objective, as it guarantees a two-way information flow that will provide the means for energy management enhancement. However, to obtain all the required information, another entity must care about all the devices required to gather the data. What is more, this entity must consider the lifespan of the devices within the Smart Grid—when they are turned on and off or when new appliances are added—along with the services that devices are able to provide. This paper puts forward SMArc—an acronym for semantic middleware architecture—as a middleware proposal for the Smart Grid, so as to process the collected data and use it to insulate applications from the complexity of the metering facilities and guarantee that any change that may happen at these lower levels will be updated for future actions in the system.

A semantic middleware architecture focused on data and heterogeneity management within the Smart Grid

There is an increasing tendency of turning the current power grid, essentially unaware of variations in electricity demand and scattered energy sources, into something capable of bringing a degree of intelligence by using tools strongly related to information and communication technologies, thus turning into the so-called Smart Grid. In fact, it could be considered that the Smart Grid is an extensive smart system that spreads throughout any area where power is required, providing a significant optimization in energy generation, storage and consumption. However, the information that must be treated to accomplish these tasks is challenging both in terms of complexity (semantic features, distributed systems, suitable hardware) and quantity (consumption data, generation data, forecasting functionalities, service reporting), since the different energy beneficiaries are prone to be heterogeneous, as the nature of their own activities is. This paper presents a proposal on how to deal with these issues by using a semantic middleware architecture that integrates different components focused on specific tasks, and how it is used to handle information at every level and satisfy end user requests.

Generating Linked-Data based Domain-Specific Sentiment Lexicons from Legacy Language and Semantic Resources

We present a methodology for legacy language resource adaptation that generates domain-specific sentiment lexicons organized around domain entities described with lexical information and sentiment words described in the context of these entities. We explain the steps of the methodology and we give a working example of our initial results. The resulting lexicons are modelled as Linked Data resources by use of established formats for Linguistic Linked Data (lemon, NIF) and for linked sentiment expressions (Marl), thereby contributing and linking to existing Language Resources in the Linguistic Linked Open Data cloud.

Analysis of the suitability of existing medical ontologies for building a scalable semantic interoperability solution supporting multi-site collaboration in oncology

Semantic interoperability is essential to facilitate efficient collaboration in heterogeneous multi-site healthcare environments. The deployment of a semantic interoperability solution has the potential to enable a wide range of informatics supported applications in clinical care and research both within as ingle healthcare organization and in a network of organizations. At the same time, building and deploying a semantic interoperability solution may require significant effort to carryout data transformation and to harmonize the semantics of the information in the different systems. Our approach to semantic interoperability leverages existing healthcare standards and ontologies, focusing first on specific clinical domains and key applications, and gradually expanding the solution when needed. An important objective of this work is to create a semantic link between clinical research and care environments to enable applications such as streamlining the execution of multi-centric clinical trials, including the identification of eligible patients for the trials. This paper presents an analysis of the suitability of several widely-used medical ontologies in the clinical domain: SNOMED-CT, LOINC, MedDRA, to capture the semantics of the clinical trial eligibility criteria, of the clinical trial data (e.g., Clinical Report Forms), and of the corresponding patient record data that would enable the automatic identification of eligible patients. Next to the coverage provided by the ontologies we evaluate and compare the sizes of the sets of relevant concepts and their relative frequency to estimate the cost of data transformation, of building the necessary semantic mappings, and of extending the solution to new domains. This analysis shows that our approach is both feasible and scalable.

A Semantic Approach to Problem-Based Learning with Conceptual Models

El aprendizaje basado en problemas se lleva aplicando con éxito durante las últimas tres décadas en un amplio rango de entornos de aprendizaje. Este enfoque educacional consiste en proponer problemas a los estudiantes de forma que puedan aprender sobre un dominio particular mediante el desarrollo de soluciones a dichos problemas. Si esto se aplica al modelado de conocimiento, y en particular al basado en Razonamiento Cualitativo, las soluciones a los problemas pasan a ser modelos que representan el compotamiento del sistema dinámico propuesto. Por lo tanto, la tarea del estudiante en este caso es acercar su modelo inicial (su primer intento de representar el sistema) a los modelos objetivo que proporcionan soluciones al problema, a la vez que adquieren conocimiento sobre el dominio durante el proceso. En esta tesis proponemos KaiSem, un método que usa tecnologías y recursos semánticos para guiar a los estudiantes durante el proceso de modelado, ayudándoles a adquirir tanto conocimiento como sea posible sin la directa supervisión de un profesor. Dado que tanto estudiantes como profesores crean sus modelos de forma independiente, estos tendrán diferentes terminologías y estructuras, dando lugar a un conjunto de modelos altamente heterogéneo. Para lidiar con tal heterogeneidad, proporcionamos una técnica de anclaje semántico para determinar, de forma automática, enlaces entre la terminología libre usada por los estudiantes y algunos vocabularios disponibles en la Web de Datos, facilitando con ello la interoperabilidad y posterior alineación de modelos. Por último, proporcionamos una técnica de feedback semántico para comparar los modelos ya alineados y generar feedback basado en las posibles discrepancias entre ellos. Este feedback es comunicado en forma de sugerencias individualizadas que el estudiante puede utilizar para acercar su modelo a los modelos objetivos en cuanto a su terminología y estructura se refiere. ABSTRACT Problem-based learning has been successfully applied over the last three decades to a diverse range of learning environments. This educational approach consists of posing problems to learners, so they can learn about a particular domain by developing solutions to them. When applied to conceptual modeling, and particularly to Qualitative Reasoning, the solutions to problems are models that represent the behavior of a dynamic system. Therefore, the learner's task is to move from their initial model, as their first attempt to represent the system, to the target models that provide solutions to that problem while acquiring domain knowledge in the process. In this thesis we propose KaiSem, a method for using semantic technologies and resources to scaffold the modeling process, helping the learners to acquire as much domain knowledge as possible without direct supervision from the teacher. Since learners and experts create their models independently, these will have different terminologies and structure, giving rise to a pool of models highly heterogeneous. To deal with such heterogeneity, we provide a semantic grounding technique to automatically determine links between the unrestricted terminology used by learners and some online vocabularies of the Web of Data, thus facilitating the interoperability and later alignment of the models. Lastly, we provide a semantic-based feedback technique to compare the aligned models and generate feedback based on the possible discrepancies. This feedback is communicated in the form of individualized suggestions, which can be used by the learner to bring their model closer in terminology and structure to the target models.

Role of left inferior prefrontal cortex in retrieval of semantic knowledge: A reevaluation

A number of neuroimaging findings have been interpreted as evidence that the left inferior frontal gyrus (IFG) subserves retrieval of semantic knowledge. We provide a fundamentally different interpretation, that it is not retrieval of semantic knowledge per se that is associated with left IFG activity but rather selection of information among competing alternatives from semantic memory. Selection demands were varied across three semantic tasks in a single group of subjects. Functional magnetic resonance imaging signal in overlapping regions of left IFG was dependent on selection demands in all three tasks. In addition, the degree of semantic processing was varied independently of selection demands in one of the tasks. The absence of left IFG activity for this comparison counters the argument that the effects of selection can be attributed solely to variations in degree of semantic retrieval. Our findings suggest that it is selection, not retrieval, of semantic knowledge that drives activity in the left IFG.

Developing Semantic Rich Internet Applications with the Sm4RIA Extension for OIDE

This paper introduces the Sm4RIA Extension for OIDE, which implements the Sm4RIA approach in OIDE (OOH4RIA Integrated Development Environment). The application, based on the Eclipse framework, supports the design of the Sm4RIA models as well as the model-to-model and model-to-text transformation processes that facilitate the generation of Semantic Rich Internet Applications, i.e., RIA applications capable of sharing data as Linked data and consuming external data from other sources in the same manner. Moreover, the application implements mechanisms for the creation of RIA interfaces from ontologies and the automatic generation of administration interfaces for a previously design application.

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.

A semantic framework for textual data enrichment

In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.

The role of semantic context and attentional resource distribution in semantic comprehension in Swedish pre-schoolers

Research on semantic processing focused mainly on isolated units in language, which does not reflect the complexity of language. In order to understand how semantic information is processed in a wider context, the first goal of this thesis was to determine whether Swedish pre-school children are able to comprehend semantic context and if that context is semantically built up over time. The second goal was to investigate how the brain distributes attentional resources by means of brain activation amplitude and processing type. Swedish preschool children were tested in a dichotic listening task with longer children’s narratives. The development of event-related potential N400 component and its amplitude were used to investigate both goals. The decrease of the N400 in the attended and unattended channel indicated semantic comprehension and that semantic context was built up over time. The attended stimulus received more resources, processed the stimuli in more of a top-down manner and displayed prominent N400 amplitude in contrast to the unattended stimulus. The N400 and the late positivity were more complex than expected since endings of utterances longer than nine words were not accounted for. More research on wider linguistic context is needed in order to understand how the human brain comprehends natural language. 

Maintaining and updating semantic context in schizophrenia: an investigation of the effects of multiple remote primes

Conflicting findings regarding the ability of people with schizophrenia to maintain and update semantic contexts have been due, arguably, to vagaries within the experimental design employed (e.g. whether strongly or remotely associated prime-target pairs have been used, what delay between the prime and the target was employed, and what proportion of related prime-target pairs appeared) or to characteristics of the participant cohort (e.g. medication status, chronicity of illness). The aim of the present study was to examine how people with schizophrenia maintain and update contextual information over an extended temporal window by using multiple primes that were either remotely associated or unrelated to the target. Fourteen participants with schizophrenia and 12 healthy matched controls were compared across two stimulus onset asynchronies (SOAs) (short and long) and two relatedness proportions (RP) (high and low) in a crossed design. Analysis of variance statistics revealed significant two- and three-way interactions between Group and SOA, Group and Condition, SOA and RP, and Group, SOA and RP. The participants with schizophrenia showed evidence of enhanced remote priming at the short SOA and low RP, combined with a reduction in the time course over which context could be maintained. There was some sensitivity to biasing contextual information at the short SOA, although the mechanism over which context served to update information appeared to be different from that in the controls. The participants with schizophrenia showed marked performance decrements at the long SOA (both low and high RP). Indices of remote priming at the short (but not the long) SOA correlated with both clinical ratings of thought disorder and with increasing length of illness. The results support and extend the hypothesis that schizophrenia is associated with concurrent increases in tonic dopamine activity and decreases in phasic dopamine activity. (C) 2004 Elsevier Ireland Ltd. All rights reserved.

Summation of semantic priming and complex sentence comprehension in Parkinson's disease

Research has suggested that the integrity of semantic processing may be compromised in Parkinson's disease (PD), which may account for difficulties in complex sentence comprehension. In order to investigate the time course and integrity of semantic activation in PD, 20 patients with PD and 23 healthy controls performed a lexical decision task based on the multi-priming paradigm. Semantic priming effects were measured across stimulus onset asynchronies of 250 ms, 600 ms, and 1200 ms. Further, PD participants performed an auditory comprehension task. The results revealed significantly different patterns of semantic priming for the PD group at the 250-ms and 1200-ms SOAs. In addition, a delayed time course of semantic activation was evident for PD patients with poor comprehension of complex sentences. These results provide further support to suggest that both automatic and controlled aspects of semantic activation may be compromised in PD. Furthermore, the results also suggest that some sentence comprehension deficits in PD may be related to a reduction in information processing speed.

ICICLE: A semantic-based retrieval system for WWW images

In this paper, we present ICICLE (Image ChainNet and Incremental Clustering Engine), a prototype system that we have developed to efficiently and effectively retrieve WWW images based on image semantics. ICICLE has two distinguishing features. First, it employs a novel image representation model called Weight ChainNet to capture the semantics of the image content. A new formula, called list space model, for computing semantic similarities is also introduced. Second, to speed up retrieval, ICICLE employs an incremental clustering mechanism, ICC (Incremental Clustering on ChainNet), to cluster images with similar semantics into the same partition. Each cluster has a summary representative and all clusters' representatives are further summarized into a balanced and full binary tree structure. We conducted an extensive performance study to evaluate ICICLE. Compared with some recently proposed methods, our results show that ICICLE provides better recall and precision. Our clustering technique ICC facilitates speedy retrieval of images without sacrificing recall and precision significantly.

Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping

The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.

A Correlation Analysis on LSA and HAL Semantic Space Models

In this paper, we compare a well-known semantic spacemodel, Latent Semantic Analysis (LSA) with another model, Hyperspace Analogue to Language (HAL) which is widely used in different area, especially in automatic query refinement. We conduct this comparative analysis to prove our hypothesis that with respect to ability of extracting the lexical information from a corpus of text, LSA is quite similar to HAL. We regard HAL and LSA as black boxes. Through a Pearsonrsquos correlation analysis to the outputs of these two black boxes, we conclude that LSA highly co-relates with HAL and thus there is a justification that LSA and HAL can potentially play a similar role in the area of facilitating automatic query refinement. This paper evaluates LSA in a new application area and contributes an effective way to compare different semantic space models.