877 resultados para Semantic Annotation
Resumo:
This project is a step forward in the study of text mining where enhanced text representation with semantic information plays a significant role. It develops effective methods of entity-oriented retrieval, semantic relation identification and text clustering utilizing semantically annotated data. These methods are based on enriched text representation generated by introducing semantic information extracted from Wikipedia into the input text data. The proposed methods are evaluated against several start-of-art benchmarking methods on real-life data-sets. In particular, this thesis improves the performance of entity-oriented retrieval, identifies different lexical forms for an entity relation and handles clustering documents with multiple feature spaces.
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Actualmente, la Web provee un inmenso conjunto de servicios (WS-*, RESTful, OGC WFS), los cuales están normalmente expuestos a través de diferentes estándares que permiten localizar e invocar a estos servicios. Estos servicios están, generalmente, descritos utilizando información textual, sin una descripción formal, es decir, la descripción de los servicios es únicamente sintáctica. Para facilitar el uso y entendimiento de estos servicios, es necesario anotarlos de manera formal a través de la descripción de los metadatos. El objetivo de esta tesis es proponer un enfoque para la anotación semántica de servicios Web en el dominio geoespacial. Este enfoque permite automatizar algunas de las etapas del proceso de anotación, mediante el uso combinado de recursos ontológicos y servicios externos. Este proceso ha sido evaluado satisfactoriamente con un conjunto de servicios en el dominio geoespacial. La contribución principal de este trabajo es la automatización parcial del proceso de anotación semántica de los servicios RESTful y WFS, lo cual mejora el estado del arte en esta área. Una lista detallada de las contribuciones son: • Un modelo para representar servicios Web desde el punto de vista sintáctico y semántico, teniendo en cuenta el esquema y las instancias. • Un método para anotar servicios Web utilizando ontologías y recursos externos. • Un sistema que implementa el proceso de anotación propuesto. • Un banco de pruebas para la anotación semántica de servicios RESTful y OGC WFS. Abstract The Web contains an immense collection of Web services (WS-*, RESTful, OGC WFS), normally exposed through standards that tell us how to locate and invocate them. These services are usually described using mostly textual information and without proper formal descriptions, that is, existing service descriptions mostly stay on a syntactic level. If we want to make such services potentially easier to understand and use, we may want to annotate them formally, by means of descriptive metadata. The objective of this thesis is to propose an approach for the semantic annotation of services in the geospatial domain. Our approach automates some stages of the annotation process, by using a combination of thirdparty resources and services. It has been successfully evaluated with a set of geospatial services. The main contribution of this work is the partial automation of the process of RESTful and WFS semantic annotation services, what improves the current state of the art in this area. The more detailed list of contributions are: • A model for representing Web services. • A method for annotating Web services using ontological and external resources. • A system that implements the proposed annotation process. • A gold standard for the semantic annotation of RESTful and OGC WFS services, and algorithms for evaluating the annotations.
Resumo:
While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents. In the emerging Semantic Web, search, interpretation and aggregation can be addressed by ontology-based semantic mark-up. In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems. This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress.
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We are interested in the annotation of knowledge which does not necessarily require a consensus. Scholarly debate is an example of such a category of knowledge where disagreement and contest are widespread and desirable, and unlike many Semantic Web approaches, we are interested in the capture and the compilation of these conflicting viewpoints and perspectives. The Scholarly Ontologies project provides the underlying formalism to represent this meta-knowledge, and we will look at ways to lighten the burden of its creation. After having described some particularities of this kind of knowledge, we introduce ClaimSpotter, our approach to support its ‘capture’, based on the elicitation of a number of recommendations which are presented for consideration to our annotators (or analysts), and give some elements of evaluation.
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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
Resumo:
The Semantic Annotation component is a software application that provides support for automated text classification, a process grounded in a cohesion-centered representation of discourse that facilitates topic extraction. The component enables the semantic meta-annotation of text resources, including automated classification, thus facilitating information retrieval within the RAGE ecosystem. It is available in the ReaderBench framework (http://readerbench.com/) which integrates advanced Natural Language Processing (NLP) techniques. The component makes use of Cohesion Network Analysis (CNA) in order to ensure an in-depth representation of discourse, useful for mining keywords and performing automated text categorization. Our component automatically classifies documents into the categories provided by the ACM Computing Classification System (http://dl.acm.org/ccs_flat.cfm), but also into the categories from a high level serious games categorization provisionally developed by RAGE. English and French languages are already covered by the provided web service, whereas the entire framework can be extended in order to support additional languages.
Resumo:
With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.
Resumo:
To be presented at SIG/ISMB07 ontology workshop: http://bio-ontologies.org.uk/index.php To be published in BMC Bioinformatics. Sponsorship: JISC
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Cost-effective semantic description and annotation of shared knowledge resources has always been of great importance for digital libraries and large scale information systems in general. With the emergence of the Social Web and Web 2.0 technologies, a more effective semantic description and annotation, e.g., folksonomies, of digital library contents is envisioned to take place in collaborative and personalised environments. However, there is a lack of foundation and mathematical rigour for coping with contextualised management and retrieval of semantic annotations throughout their evolution as well as diversity in users and user communities. In this paper, we propose an ontological foundation for semantic annotations of digital libraries in terms of flexonomies. The proposed theoretical model relies on a high dimensional space with algebraic operators for contextualised access of semantic tags and annotations. The set of the proposed algebraic operators, however, is an adaptation of the set theoretic operators selection, projection, difference, intersection, union in database theory. To this extent, the proposed model is meant to lay the ontological foundation for a Digital Library 2.0 project in terms of geometric spaces rather than logic (description) based formalisms as a more efficient and scalable solution to the semantic annotation problem in large scale.
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In this paper the authors present an approach for the semantic annotation of RESTful services in the geospatial domain. Their approach automates some stages of the annotation process, by using a combination of resources and services: a cross-domain knowledge base like DBpedia, two domain ontologies like GeoNames and the WGS84 vocabulary, and suggestion and synonym services. The authors’ approach has been successfully evaluated with a set of geospatial RESTful services obtained from ProgrammableWeb.com, where geospatial services account for a third of the total amount of services available in this registry.
Resumo:
Le dictionnaire LVF (Les Verbes Français) de J. Dubois et F. Dubois-Charlier représente une des ressources lexicales les plus importantes dans la langue française qui est caractérisée par une description sémantique et syntaxique très pertinente. Le LVF a été mis disponible sous un format XML pour rendre l’accès aux informations plus commode pour les applications informatiques telles que les applications de traitement automatique de la langue française. Avec l’émergence du web sémantique et la diffusion rapide de ses technologies et standards tels que XML, RDF/RDFS et OWL, il serait intéressant de représenter LVF en un langage plus formalisé afin de mieux l’exploiter par les applications du traitement automatique de la langue ou du web sémantique. Nous en présentons dans ce mémoire une version ontologique OWL en détaillant le processus de transformation de la version XML à OWL et nous en démontrons son utilisation dans le domaine du traitement automatique de la langue avec une application d’annotation sémantique développée dans GATE.
Resumo:
La présente étude s’inscrit dans une lignée de travaux de recherche en traductologie réalisés dans un cadre de sémantique cognitive et visant à dégager les modes de conceptualisation métaphorique dans les domaines de spécialité, et plus précisément dans les sciences biomédicales. Notre étude se concentre sur les modes de conceptualisation métaphorique utilisés en neuroanatomie en français, en anglais et en allemand, dans une perspective d’application à la traduction. Nous nous penchons plus spécifiquement sur la description anatomique de deux structures du système nerveux central : la moelle spinale et le cervelet. Notre objectif est de repérer et de caractériser les indices de conceptualisation métaphorique (ICM). Notre méthode s'appuie sur un corpus trilingue de textes de référence traitant de ces structures et fait appel à une annotation sémantique en langage XML, ce qui autorise une interrogation des corpus annotés au moyen du langage XQuery. Nous mettons en évidence que les ICM jouent un rôle prédominant dans la phraséologie et les dénominations propres à la description anatomique du système nerveux, comme c'est le cas en biologie cellulaire et en anatomie des muscles, des nerfs périphériques et des vaisseaux sanguins. Sous l’angle lexical, il faut distinguer les ICM prédicatifs, les ICM non prédicatifs ainsi que les ICM quasi prédicatifs. La plupart des modes de conceptualisation métaphorique préalablement repérés en biologie cellulaire et en anatomie sont également présents dans le domaine plus spécifique de la neuroanatomie. Certains ICM et modes de conceptualisation sont toutefois spécifiques à des éléments des régions étudiées. Par ailleurs, les modes de conceptualisation métaphorique en français, en anglais et en allemand sont semblables, mais sont exprimés par des réseaux lexicaux d'ICM dont la richesse varie. De plus, la composition nominale étant une des caractéristiques de l'allemand, la forme linguistique des ICM présente des caractéristiques spécifiques. Nos résultats mettent en évidence la richesse métaphorique de la neuroanatomie. Cohérents avec les résultats des études antérieures, ils enrichissent cependant la typologie des ICM et soulignent la complexité, sur les plans lexical et cognitif, de la métaphore conceptuelle.