Automatic Classification of Bulgarian Adjectives on Emotional Semantic Axes

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Narrative comprehension: Semantic and syntactic summary production

The purpose of this research was to examine from a syntactic and narrative structure perspective two narrative summary types: a summary with a length constraint and an unconstrained summary. In addition, this research served to develop a multidimensional theory of narrative comprehension.^ College freshmen read two short stories written by written by Sake and were asked to write a constrained summary for one text and an unconstrained summary for the other text. Following this the subjects completed a metacognitive questionnaire. The summaries were analyzed to examine transitivity features and narrative structure features. The metacognitive questionnaires were examined to extract information about plot structure, differences between one and two episode stories, and to gain insight into the strategies used by subjects in producing both summary types.^ A Paired t-test conducted on the data found that there was a significant transitivity feature mean difference between a constrained summary and an unconstrained summary indicating that the number of transitivity features produced from each summary type were task dependent.^ Chi-square tests conducted on the data found that there were proportional differences in usage between plot features and thematic abstract units in an unconstrained summary and a constrained summary indicating that plot features and thematic abstract units produced from each summary type were task dependent.^ Qualitative analyses indicated that setting, goal, and resolution are typical within plot organization, there are summary production differences between one and two episode narratives, and subjects do not seem to be aware of summary production strategies.^ The results of this research have implications for comprehension and writing instruction. ^

A semantic paradigm for intelligent data access

An implementation of Sem-ODB—a database management system based on the Semantic Binary Model is presented. A metaschema of Sem-ODB database as well as the top-level architecture of the database engine is defined. A new benchmarking technique is proposed which allows databases built on different database models to compete fairly. This technique is applied to show that Sem-ODB has excellent efficiency comparing to a relational database on a certain class of database applications. A new semantic benchmark is designed which allows evaluation of the performance of the features characteristic of semantic database applications. An application used in the benchmark represents a class of problems requiring databases with sparse data, complex inheritances and many-to-many relations. Such databases can be naturally accommodated by semantic model. A fixed predefined implementation is not enforced allowing the database designer to choose the most efficient structures available in the DBMS tested. The results of the benchmark are analyzed. ^ A new high-level querying model for semantic databases is defined. It is proven adequate to serve as an efficient native semantic database interface, and has several advantages over the existing interfaces. It is optimizable and parallelizable, supports the definition of semantic userviews and the interoperability of semantic databases with other data sources such as World Wide Web, relational, and object-oriented databases. The query is structured as a semantic database schema graph with interlinking conditionals. The query result is a mini-database, accessible in the same way as the original database. The paradigm supports and utilizes the rich semantics and inherent ergonomics of semantic databases. ^ The analysis and high-level design of a system that exploits the superiority of the Semantic Database Model to other data models in expressive power and ease of use to allow uniform access to heterogeneous data sources such as semantic databases, relational databases, web sites, ASCII files, and others via a common query interface is presented. The Sem-ODB engine is used to control all the data sources combined under a unified semantic schema. A particular application of the system to provide an ODBC interface to the WWW as a data source is discussed. ^

Semantic wrapping for heterogeneous database management

Query processing is a commonly performed procedure and a vital and integral part of information processing. It is therefore important and necessary for information processing applications to continuously improve the accessibility of data sources as well as the ability to perform queries on those data sources. ^ It is well known that the relational database model and the Structured Query Language (SQL) are currently the most popular tools to implement and query databases. However, a certain level of expertise is needed to use SQL and to access relational databases. This study presents a semantic modeling approach that enables the average user to access and query existing relational databases without the concern of the database's structure or technicalities. This method includes an algorithm to represent relational database schemas in a more semantically rich way. The result of which is a semantic view of the relational database. The user performs queries using an adapted version of SQL, namely Semantic SQL. This method substantially reduces the size and complexity of queries. Additionally, it shortens the database application development cycle and improves maintenance and reliability by reducing the size of application programs. Furthermore, a Semantic Wrapper tool illustrating the semantic wrapping method is presented. ^ I further extend the use of this semantic wrapping method to heterogeneous database management. Relational, object-oriented databases and the Internet data sources are considered to be part of the heterogeneous database environment. Semantic schemas resulting from the algorithm presented in the method were employed to describe the structure of these data sources in a uniform way. Semantic SQL was utilized to query various data sources. As a result, this method provides users with the ability to access and perform queries on heterogeneous database systems in a more innate way. ^

Design principles of semantic binary database management systems

The Semantic Binary Data Model (SBM) is a viable alternative to the now-dominant relational data model. SBM would be especially advantageous for applications dealing with complex interrelated networks of objects provided that a robust efficient implementation can be achieved. This dissertation presents an implementation design method for SBM, algorithms, and their analytical and empirical evaluation. Our method allows building a robust and flexible database engine with a wider applicability range and improved performance. ^ Extensions to SBM are introduced and an implementation of these extensions is proposed that allows the database engine to efficiently support applications with a predefined set of queries. A New Record data structure is proposed. Trade-offs of employing Fact, Record and Bitmap Data structures for storing information in a semantic database are analyzed. ^ A clustering ID distribution algorithm and an efficient algorithm for object ID encoding are proposed. Mapping to an XML data model is analyzed and a new XML-based XSDL language facilitating interoperability of the system is defined. Solutions to issues associated with making the database engine multi-platform are presented. An improvement to the atomic update algorithm suitable for certain scenarios of database recovery is proposed. ^ Specific guidelines are devised for implementing a robust and well-performing database engine based on the extended Semantic Data Model. ^

Semantic Mining based on graph theory and ontologies. Case Study: Cell Signaling Pathways

In this paper we use concepts from graph theory and cellular biology represented as ontologies, to carry out semantic mining tasks on signaling pathway networks. Specifically, the paper describes the semantic enrichment of signaling pathway networks. A cell signaling network describes the basic cellular activities and their interactions. The main contribution of this paper is in the signaling pathway research area, it proposes a new technique to analyze and understand how changes in these networks may affect the transmission and flow of information, which produce diseases such as cancer and diabetes. Our approach is based on three concepts from graph theory (modularity, clustering and centrality) frequently used on social networks analysis. Our approach consists into two phases: the first uses the graph theory concepts to determine the cellular groups in the network, which we will call them communities; the second uses ontologies for the semantic enrichment of the cellular communities. The measures used from the graph theory allow us to determine the set of cells that are close (for example, in a disease), and the main cells in each community. We analyze our approach in two cases: TGF-β and the Alzheimer Disease.

Scalable semantic aware context storage

The number of connected devices collecting and distributing real-world information through various systems, is expected to soar in the coming years. As the number of such connected devices grows, it becomes increasingly difficult to store and share all these new sources of information. Several context representation schemes try to standardize this information, but none of them have been widely adopted. In previous work we addressed this challenge, however our solution had some drawbacks: poor semantic extraction and scalability. In this paper we discuss ways to efficiently deal with representation schemes' diversity and propose a novel d-dimension organization model. Our evaluation shows that d-dimension model improves scalability and semantic extraction.

The influence of surface and deep cues on primary and secondary school students’ assessment of relevance in Web menus

International audience

Deep convolutional neural networks for semantic video object segmentation

In this thesis, we propose to infer pixel-level labelling in video by utilising only object category information, exploiting the intrinsic structure of video data. Our motivation is the observation that image-level labels are much more easily to be acquired than pixel-level labels, and it is natural to find a link between the image level recognition and pixel level classification in video data, which would transfer learned recognition models from one domain to the other one. To this end, this thesis proposes two domain adaptation approaches to adapt the deep convolutional neural network (CNN) image recognition model trained from labelled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of unlabelled video data. Our proposed approaches explicitly model and compensate for the domain adaptation from the source domain to the target domain which in turn underpins a robust semantic object segmentation method for natural videos. We demonstrate the superior performance of our methods by presenting extensive evaluations on challenging datasets comparing with the state-of-the-art methods.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.

Usefulness of social tagging in organizing and providing access to the web: An analysis of indexing consistency and quality

This dissertation research points out major challenging problems with current Knowledge Organization (KO) systems, such as subject gateways or web directories: (1) the current systems use traditional knowledge organization systems based on controlled vocabulary which is not very well suited to web resources, and (2) information is organized by professionals not by users, which means it does not reflect intuitively and instantaneously expressed users’ current needs. In order to explore users’ needs, I examined social tags which are user-generated uncontrolled vocabulary. As investment in professionally-developed subject gateways and web directories diminishes (support for both BUBL and Intute, examined in this study, is being discontinued), understanding characteristics of social tagging becomes even more critical. Several researchers have discussed social tagging behavior and its usefulness for classification or retrieval; however, further research is needed to qualitatively and quantitatively investigate social tagging in order to verify its quality and benefit. This research particularly examined the indexing consistency of social tagging in comparison to professional indexing to examine the quality and efficacy of tagging. The data analysis was divided into three phases: analysis of indexing consistency, analysis of tagging effectiveness, and analysis of tag attributes. Most indexing consistency studies have been conducted with a small number of professional indexers, and they tended to exclude users. Furthermore, the studies mainly have focused on physical library collections. This dissertation research bridged these gaps by (1) extending the scope of resources to various web documents indexed by users and (2) employing the Information Retrieval (IR) Vector Space Model (VSM) - based indexing consistency method since it is suitable for dealing with a large number of indexers. As a second phase, an analysis of tagging effectiveness with tagging exhaustivity and tag specificity was conducted to ameliorate the drawbacks of consistency analysis based on only the quantitative measures of vocabulary matching. Finally, to investigate tagging pattern and behaviors, a content analysis on tag attributes was conducted based on the FRBR model. The findings revealed that there was greater consistency over all subjects among taggers compared to that for two groups of professionals. The analysis of tagging exhaustivity and tag specificity in relation to tagging effectiveness was conducted to ameliorate difficulties associated with limitations in the analysis of indexing consistency based on only the quantitative measures of vocabulary matching. Examination of exhaustivity and specificity of social tags provided insights into particular characteristics of tagging behavior and its variation across subjects. To further investigate the quality of tags, a Latent Semantic Analysis (LSA) was conducted to determine to what extent tags are conceptually related to professionals’ keywords and it was found that tags of higher specificity tended to have a higher semantic relatedness to professionals’ keywords. This leads to the conclusion that the term’s power as a differentiator is related to its semantic relatedness to documents. The findings on tag attributes identified the important bibliographic attributes of tags beyond describing subjects or topics of a document. The findings also showed that tags have essential attributes matching those defined in FRBR. Furthermore, in terms of specific subject areas, the findings originally identified that taggers exhibited different tagging behaviors representing distinctive features and tendencies on web documents characterizing digital heterogeneous media resources. These results have led to the conclusion that there should be an increased awareness of diverse user needs by subject in order to improve metadata in practical applications. This dissertation research is the first necessary step to utilize social tagging in digital information organization by verifying the quality and efficacy of social tagging. This dissertation research combined both quantitative (statistics) and qualitative (content analysis using FRBR) approaches to vocabulary analysis of tags which provided a more complete examination of the quality of tags. Through the detailed analysis of tag properties undertaken in this dissertation, we have a clearer understanding of the extent to which social tagging can be used to replace (and in some cases to improve upon) professional indexing.

Mejorando la visibilidad de sitios web usando tecnología semántica

La visibilidad de una página Web involucra el proceso de mejora de la posición del sitio en los resultados devueltos por motores de búsqueda como Google. Hay muchas empresas que compiten agresivamente para conseguir la primera posición en los motores de búsqueda más populares. Como regla general, los sitios que aparecen más arriba en los resultados suelen obtener más tráfico a sus páginas, y de esta forma, potencialmente más negocios. En este artículo se describe los principales modelos para enriquecer los resultados de las búsquedas con información tales como fechas o localidades; información de tipo clave-valor que permite al usuario interactuar con el contenido de una página Web directamente desde el sitio de resultados de la búsqueda. El aporte fundamental del artículo es mostrar la utilidad de diferentes formatos de marcado para enriquecer fragmentos de una página Web con el fin de ayudar a las empresas que están planeando implementar métodos de enriquecimiento semánticos en la estructuración de sus sitios Web.

A Retrospective Socio-Semantic Analysis of the PRO-VE Conferences

Part 19: Knowledge Management in Networks

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

Approche basée sur des patrons pour concevoir des logiciels d’enseignement adaptés aux technologies du Web

Les applications Web en général ont connu d’importantes évolutions technologiques au cours des deux dernières décennies et avec elles les habitudes et les attentes de la génération de femmes et d’hommes dite numérique. Paradoxalement à ces bouleversements technologiques et comportementaux, les logiciels d’enseignement et d’apprentissage (LEA) n’ont pas tout à fait suivi la même courbe d’évolution technologique. En effet, leur modèle de conception est demeuré si statique que leur utilité pédagogique est remise en cause par les experts en pédagogie selon lesquels les LEA actuels ne tiennent pas suffisamment compte des aspects théoriques pédagogiques. Mais comment améliorer la prise en compte de ces aspects dans le processus de conception des LEA? Plusieurs approches permettent de concevoir des LEA robustes. Cependant, un intérêt particulier existe pour l’utilisation du concept patron dans ce processus de conception tant par les experts en pédagogie que par les experts en génie logiciel. En effet, ce concept permet de capitaliser l’expérience des experts et permet aussi de simplifier de belle manière le processus de conception et de ce fait son coût. Une comparaison des travaux utilisant des patrons pour concevoir des LEA a montré qu’il n’existe pas de cadre de synergie entre les différents acteurs de l’équipe de conception, les experts en pédagogie d’un côté et les experts en génie logiciel de l’autre. De plus, les cycles de vie proposés dans ces travaux ne sont pas complets, ni rigoureusement décrits afin de permettre de développer des LEA efficients. Enfin, les travaux comparés ne montrent pas comment faire coexister les exigences pédagogiques avec les exigences logicielles. Le concept patron peut-il aider à construire des LEA robustes satisfaisant aux exigences pédagogiques ? Comme solution, cette thèse propose une approche de conception basée sur des patrons pour concevoir des LEA adaptés aux technologies du Web. Plus spécifiquement, l’approche méthodique proposée montre quelles doivent être les étapes séquentielles à prévoir pour concevoir un LEA répondant aux exigences pédagogiques. De plus, un répertoire est présenté et contient 110 patrons recensés et organisés en paquetages. Ces patrons peuvent être facilement retrouvés à l’aide du guide de recherche décrit pour être utilisés dans le processus de conception. L’approche de conception a été validée avec deux exemples d’application, permettant de conclure d’une part que l’approche de conception des LEA est réaliste et d’autre part que les patrons sont bien valides et fonctionnels. L’approche de conception de LEA proposée est originale et se démarque de celles que l’on trouve dans la littérature car elle est entièrement basée sur le concept patron. L’approche permet également de prendre en compte les exigences pédagogiques. Elle est générique car indépendante de toute plateforme logicielle ou matérielle. Toutefois, le processus de traduction des exigences pédagogiques n’est pas encore très intuitif, ni très linéaire. D’autres travaux doivent être réalisés pour compléter les résultats obtenus afin de pouvoir traduire en artéfacts exploitables par les ingénieurs logiciels les exigences pédagogiques les plus complexes et les plus abstraites. Pour la suite de cette thèse, une instanciation des patrons proposés serait intéressante ainsi que la définition d’un métamodèle basé sur des patrons qui pourrait permettre la spécification d’un langage de modélisation typique des LEA. L’ajout de patrons permettant d’ajouter une couche sémantique au niveau des LEA pourrait être envisagée. Cette couche sémantique permettra non seulement d’adapter les scénarios pédagogiques, mais aussi d’automatiser le processus d’adaptation au besoin d’un apprenant en particulier. Il peut être aussi envisagé la transformation des patrons proposés en ontologies pouvant permettre de faciliter l’évaluation des connaissances de l’apprenant, de lui communiquer des informations structurées et utiles pour son apprentissage et correspondant à son besoin d’apprentissage.