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Mu Dong-me - One of the best experts on this subject based on the ideXlab platform.
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Comparative Study on Semantic Annotation Platforms
Information Sciences, 2008Co-Authors: Mu Dong-meAbstract:This paper made a summary on current representative Semantic Annotation platforms in brief,compared and analysed them in terms of Annotation method,possibility of extension and Annotation precision,discussed the trend of Semantic Annotation platform.
Siegfried Handschuh - One of the best experts on this subject based on the ideXlab platform.
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Towards controlled natural language for Semantic Annotation
International Journal on Semantic Web and Information Systems, 2010Co-Authors: B. Davis, Pradeep Dantuluri, Siegfried Handschuh, Hamish CunninghamAbstract:Richly interlinked metadata constitute the foundation of the Semantic Web. Manual Semantic Annotation is a labor intensive task requiring training in formal ontological descriptions for the otherwise non-expert user. Although automatic Annotation tools attempt to ease this knowledge acquisition barrier, their development often requires access to specialists in Natural Language Processing (NLP). This challenges researchers to develop user-friendly Annotation environments. Controlled Natural Languages (CNLs) offer an incentive to the novice user to annotate, while simultaneously authoring his/her respective documents in a user-friendly manner. CNLs have been successfully applied to ontology authoring, but little research has focused on their application to Semantic Annotation. This paper describes two novel approaches to Semantic Annotation, which permit non-expert users to simultaneously author and annotate meeting minutes using CNL. Finally, this work provides empirical evidence that for certain scenarios applying CNLs for Semantic Annotation can be more user friendly than a standard manual Semantic Annotation tool. Copyright © 2010, IGI Global.
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on designing controlled natural languages for Semantic Annotation
Controlled Natural Language, 2009Co-Authors: B. Davis, Pradeep Dantuluri, Siegfried Handschuh, Laura Dragan, Hamish CunninghamAbstract:Manual Semantic Annotation is a complex and arduous task both time-consuming and costly often requiring specialist annotators. (Semi)-automatic Annotation tools attempt to ease this process by detecting instances of classes within text and relationships between instances, however their usage often requires knowledge of Natural Language Processing( NLP) or formal ontological descriptions. This challenges researchers to develop user-friendly Annotation environments within the knowledge acquisition process. Controlled Natural Languages (CNL)s offer an incentive to the novice user to annotate, while simultaneously authoring, his/her respective documents in a user-friendly manner, yet shielding him/her from the underlying complex knowledge representation formalisms. CNLs have already been successfully applied within the context of ontology authoring, yet very little research has focused on CNLs for Semantic Annotation. We describe the design and implementation of two approaches to user friendly Semantic Annotation, based on Controlled Language for Information Extraction tools, which permit nonexpert users to semi-automatically both author and annotate meeting minutes and status reports using controlled natural language.
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knowledge representation and Semantic Annotation of multimedia content
IEE Proceedings - Vision Image and Signal Processing, 2006Co-Authors: Kosmas Petridis, Siegfried Handschuh, Stephan Bloehdorn, Carsten Saathoff, Nikos Simou, Stamatia Dasiopoulou, Vassilis Tzouvaras, Yannis Avrithis, Yiannis Kompatsiaris, Steffen StaabAbstract:Knowledge representation and Semantic Annotation of multimedia documents typically have been pursued in two different directions. Previous approaches have focused either on low-level descriptors, such as dominant colour, or on the Semantic content dimension and corresponding manual Annotations, such as person or vehicle. Here, a knowledge infrastructure and an experimentation platform for Semantic Annotation to bridge the two directions are presented. Ontologies are being extended and enriched to include low-level audiovisual features and descriptors. Additionally, a tool that allows for linking low-level MPEG-7 visual descriptions to ontologies and Annotations is presented. Thus, ontologies that include prototypical instances of high-level domain concepts together with a formal specification of the corresponding visual descriptors are constructed. This infrastructure is exploited by a knowledge-assisted analysis framework that may handle problems such as segmentation, tracking, feature extraction and matching in order to classify scenes, identify and label objects and thus automatically create the associated Semantic metadata.
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Semantic Annotation for knowledge management: Requirements and a survey of the state of the art
Web Semantics: Science, Services and Agents on the World Wide Web, 2006Co-Authors: Fabio Ciravegna, Enrico Motta, Siegfried Handschuh, Maria Vargas-vera, José Iria, Philipp CimianoAbstract: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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Semantic Annotation of Resources in the Semantic Web
Semantic Web Services, 1Co-Authors: Siegfried HandschuhAbstract:In this chapter, we give a brief introduction into the main idea of the Semantic Web, namely making better use and enabling more intelligent applications for Web-accessible information by accompanying them with machine-understandable, Semantic meta data; we sketch the major methodological framework behind, consisting of two intertwined, orthogonal processes, the knowledge process and the knowledge meta process–the latter is concerned with ontology engineering, the former uses ontologies for ontology-based meta-data assignment to Web resources, i.e. for Semantic Annotation. The major part of the chapter is devoted to the idea of Semantic Annotation, requirements and functionalities of Annotation tools, an example implementation and an overview of the state of research and practice in Semantic Annotation.
Nacer Boudjlida - One of the best experts on this subject based on the ideXlab platform.
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Semantic Annotation for knowledge explicitation in a product lifecycle management context
Computers in Industry, 2015Co-Authors: Yongxin Liao, Mario Lezoche, Hervé Panetto, Nacer Boudjlida, Eduardo De Freitas Rocha LouresAbstract:Investigating some existing surveys about Semantic Annotation researches.Collecting and classifying Semantic Annotation researches based on Zachman framework.Analysing those collected literatures, especially from formalization aspect.Identifying the existing drawbacks and pointing out the possible research directions. Nowadays, the need for systems interoperability in or across enterprises has become more and more ubiquitous. Many research works have been carried out in the fields of information exchange, transformation, discovery and reuse. One of the main challenges in these researches is to overcome the Semantic heterogeneity between enterprise applications along the life cycle of a product. As a possible solution to assist the Semantic interoperability, the Semantic Annotation has gained many attentions and widely used in different domains. We collect a number of literature that applied Semantic Annotations on different objects, and classify them according to the subject being described in an enterprise architecture framework. A detailed survey, especially from the formalization aspect, is presented to identify the existing drawbacks and to point out the possible research directions.
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Semantic Annotation Model Definition for Systems Interoperability
2011Co-Authors: Yongxin Liao, Mario Lezoche, Hervé Panetto, Nacer BoudjlidaAbstract:Semantic Annotation is one of the useful solutions to enrich target's (systems, models, meta-models, etc.) information. There are some papers which use Semantic enrichment for different purposes (integration, composition, sharing and reuse, etc.) in several domains, but none of them provides a complete process of how to use Semantic Annotations. This paper identifies three main components of Semantic Annotation, proposes for it a formal definition and presents a survey of current Semantic Annotation methods. At the end, we present a simple case study to explain how our Semantic Annotation proposition can be applied.The survey presented in this paper will be the basis of our future research on models, Semantics and architecture for enterprises systems interoperability during the product lifecycle.
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Why, Where and How to use Semantic Annotation for Systems Interoperability
2011Co-Authors: Yongxin Liao, Mario Lezoche, Hervé Panetto, Nacer BoudjlidaAbstract:Semantic Annotation is one of the useful solutions to enrich target's (systems, models, meta-models, etc.) information. There are some papers which use Semantic enrichment for different purposes (integration, composition, sharing and reuse, etc.) in several domains, but none of them provides a complete process of how to use Semantic Annotations. This paper identifies three main components of Semantic Annotation, gives a formal definition of Semantic Annotation method and presents a survey of current Semantic Annotation methods which include: languages and tools that can be used to develop ontology, the design of Semantic Annotation structure models and the corresponding applications. The survey presented in this paper will be the basis of our future research on models, Semantics and architecture for systems interoperability.
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Why, Where and How to use Semantic Annotation for Systems Interoperability
2011Co-Authors: Yongxin Liao, Mario Lezoche, Hervé Panetto, Nacer BoudjlidaAbstract:LORIA, Nancy -University, CNRS, BP 70239, 54506 Vandoeuvre -les-Nancy Cedex, France, Nacer.Boudjlida@loria.fr Abstract: 6HPDQWLFDQQRWDWLRQLVRQHRIWKHXVHIXOVROXWLRQVWRHQULFKWDUJHW¶V V\VWHPVPRGHOVPHWD -models, etc.) information. There are some papers which use Semantic enrichment for different purposes (integratio n, composition, sharing and reuse, etc.) in several domains, but none of them provides a complete process of how to use Semantic Annotation s. This paper identifies three main components of Semantic Annotation, gives a formal definition of Semantic annotati on method and presents a survey of current Semantic Annotation methods which include: languages and tools that can be used to develop ontology, the design of Semantic Annotation structure models and the corresponding applications. Th e survey presented in this paper will be the basis of our future research on models, Semantics and architecture for systems interoperability. Keywords: Semantic Annotation, Models, Ontology, Systems Interoperability. 1. INTRODUCTION Nowadays, the need of systems collaboration across enterprises and through different domains has become more and more ubiquitous. But because the lack of standardized models or schemas, as well as Semantic differenc es and inconsistencies problems, a series of research for data/model exchange, transformation, discovery and reuse are carried out in recent years. One of the main challenges in these researches is to overcome the gap among different data/model structures. Semantic Annotation is not only just XVHGIRUHQULFKLQJWKHGDWDPRGHO¶VLQIRUPDWLRQEXWDOVR it can be one of the useful solutions for helping semi -automatic or even automatic systems interoperability. Semantically annotating data/model s can help to bridge the different knowledge representations. It can be used to discover matching between models elements , which help s information system s integration (Agt , et al., 2010) . It can Semantically enhance XML -Schem DV¶ LQIRUPDWLRQ , which supports XML documents transformation (Kopke and Eder, 2010) . It can describe web services in a Semantic network , which is used for further discovery and composition (Talantikite , et al., 2009) . It can support system modellers in reusing process models, detecting c ross -process relations, facilitating change management and knowledge transfer (Bron , et al., 2007) . Semantic Annotation can be wid ely used in many fields . It can link specific resources according to its domain ontologies . The main contribution of this paper is identifying three main components of Semantic Annotation , gives a formal definition of Semantic Annotation and presenting a survey, based on the literature, of current Semantic Annotation methods that are applied for different purposes and domains . Th ese Annotation methods vary in their ontology (languages, tools and design), models and corresponding applications . The remaining of this paper is organized as follows: Section 2 describes the definition of Annotation and gives a formal definition of s emantic Annotation s. Section 3 provides the answers to why and where to use Semantic Annotation. Section 4 fi rst presents an introduction to ontolog ies and Semantic annoation structure models, and then discusses the usage of Semantic Annotations. Section 5 concludes this paper, together with some related work and potential extensions . 2. W HAT IS Semantic Annotation ? In this section, we first illustrate the types of Annotations from different papers (section 2.1), and then propose a formal definition of Semantic Annotation together with its three main components (section 2.2) . 2.1 Definition and Types of Annotation In Oxford Dictionary Online
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OTM Workshops - Semantic Annotation model definition for systems interoperability
On the Move to Meaningful Internet Systems: OTM 2011 Workshops, 2011Co-Authors: Yongxin Liao, Mario Lezoche, Hervé Panetto, Nacer BoudjlidaAbstract:Semantic Annotation is one of the useful solutions to enrich target's (systems, models, meta-models, etc.) information. There are some papers which use Semantic enrichment for different purposes (integration, composition, sharing and reuse, etc.) in several domains, but none of them provides a complete process of how to use Semantic Annotations. This paper identifies three main components of Semantic Annotation, proposes for it a formal definition and presents a survey of current Semantic Annotation methods. At the end, we present a simple case study to explain how our Semantic Annotation proposition can be applied. The survey presented in this paper will be the basis of our future research on models, Semantics and architecture for enterprises systems interoperability during the product lifecycle.
Damyan Ognyanoff - One of the best experts on this subject based on the ideXlab platform.
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Semantic Annotation, indexing, and retrieval
Web Semantics: Science Services and Agents on the World Wide Web, 2004Co-Authors: Atanas Kiryakov, Borislav Popov, I. Terziev, Dimitar Manov, Damyan OgnyanoffAbstract:The Semantic Web realization depends on the availability of a critical mass of metadata for the web content, associated with the respective formal knowledge about the world. We claim that the Semantic Web, at its current stage of development, is in a state of a critically need of metadata generation and usage schemata that are specific, well-defined and easy to understand. This paper introduces our vision for a holistic architecture for Semantic Annotation, indexing, and retrieval of documents with regard to extensive Semantic repositories. A system (called KIM), implementing this concept, is presented in brief and it is used for the purposes of evaluation and demonstration. A particular schema for Semantic Annotation with respect to real-world entities is proposed. The underlying philosophy is that a practical Semantic Annotation is impossible without some particular knowledge modelling commitments. Our understanding is that a system for such Semantic Annotation should be based upon a simple model of real-world entity classes, complemented with extensive instance knowledge. To ensure the efficiency, ease of sharing, and reusability of the metadata, we introduce an upper-level ontology (of about 250 classes and 100 properties), which starts with some basic philosophical distinctions and then goes down to the most common entity types (people, companies, cities, etc.). Thus it encodes many of the domain-independent commonsense concepts and allows straightforward domain- specific extensions. On the basis of the ontology, a large-scale knowledge base of entity descriptions is bootstrapped, and further extended and maintained. Currently, the knowledge bases usually scales between 105 and 106 descriptions. Finally, this paper presents a Semantically enhanced information extraction system, which provides automatic Semantic Annotation with references to classes in the ontology and to instances. The system has been running over a continuously growing document collection (currently about 0.5 million news articles), so it has been under constant testing and evaluation for some time now. On the basis of these Semantic Annotations, we perform Semantic based indexing and retrieval where users can mix traditional IR (information retrieval) queries and ontology-based ones. We argue that such large-scale, fully automatic methods are essential for the transformation of the current largely textual web into a Semantic web.
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KIM-Semantic Annotation Platform
The Semantic Web - ISWC 2003, 2003Co-Authors: Borislav Popov, Angel Kirilov, Damyan Ognyanoff, Atanas Kiryakov, Dimitar Manov, Miroslav GoranovAbstract:Abstract. The KIM platform provides a novel Knowledge and Information\r\nManagement infrastructure and services for automatic Semantic Annotation,\r\nindexing, and retrieval of documents. It provides mature infrastructure for\r\nscaleable and customizable information extraction (IE1) as well as Annotation\r\nand document management, based on GATE2. In order to provide basic level of\r\nperformance and allow easy bootstrapping of applications, KIM is equipped\r\nwith an upper-level ontology and a knowledge base providing extensive\r\ncoverage of entities of general importance. The ontologies and knowledge bases\r\ninvolved are handled using cutting edge Semantic Web technology and\r\nstandards, including RDF(S) repositories, ontology middleware and reasoning.\r\nFrom technical point of view, the platform allows KIM-based applications to\r\nuse it for automatic Semantic Annotation, content retrieval based on Semantic\r\nrestrictions, and querying and modifying the underlying ontologies and\r\nknowledge bases. This paper presents the KIM platform, with emphasize on its\r\narchitecture, interfaces, tools, and other technical issues.
Borislav Popov - One of the best experts on this subject based on the ideXlab platform.
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Semantic Annotation, indexing, and retrieval
Web Semantics: Science Services and Agents on the World Wide Web, 2004Co-Authors: Atanas Kiryakov, Borislav Popov, I. Terziev, Dimitar Manov, Damyan OgnyanoffAbstract:The Semantic Web realization depends on the availability of a critical mass of metadata for the web content, associated with the respective formal knowledge about the world. We claim that the Semantic Web, at its current stage of development, is in a state of a critically need of metadata generation and usage schemata that are specific, well-defined and easy to understand. This paper introduces our vision for a holistic architecture for Semantic Annotation, indexing, and retrieval of documents with regard to extensive Semantic repositories. A system (called KIM), implementing this concept, is presented in brief and it is used for the purposes of evaluation and demonstration. A particular schema for Semantic Annotation with respect to real-world entities is proposed. The underlying philosophy is that a practical Semantic Annotation is impossible without some particular knowledge modelling commitments. Our understanding is that a system for such Semantic Annotation should be based upon a simple model of real-world entity classes, complemented with extensive instance knowledge. To ensure the efficiency, ease of sharing, and reusability of the metadata, we introduce an upper-level ontology (of about 250 classes and 100 properties), which starts with some basic philosophical distinctions and then goes down to the most common entity types (people, companies, cities, etc.). Thus it encodes many of the domain-independent commonsense concepts and allows straightforward domain- specific extensions. On the basis of the ontology, a large-scale knowledge base of entity descriptions is bootstrapped, and further extended and maintained. Currently, the knowledge bases usually scales between 105 and 106 descriptions. Finally, this paper presents a Semantically enhanced information extraction system, which provides automatic Semantic Annotation with references to classes in the ontology and to instances. The system has been running over a continuously growing document collection (currently about 0.5 million news articles), so it has been under constant testing and evaluation for some time now. On the basis of these Semantic Annotations, we perform Semantic based indexing and retrieval where users can mix traditional IR (information retrieval) queries and ontology-based ones. We argue that such large-scale, fully automatic methods are essential for the transformation of the current largely textual web into a Semantic web.
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KIM-Semantic Annotation Platform
The Semantic Web - ISWC 2003, 2003Co-Authors: Borislav Popov, Angel Kirilov, Damyan Ognyanoff, Atanas Kiryakov, Dimitar Manov, Miroslav GoranovAbstract:Abstract. The KIM platform provides a novel Knowledge and Information\r\nManagement infrastructure and services for automatic Semantic Annotation,\r\nindexing, and retrieval of documents. It provides mature infrastructure for\r\nscaleable and customizable information extraction (IE1) as well as Annotation\r\nand document management, based on GATE2. In order to provide basic level of\r\nperformance and allow easy bootstrapping of applications, KIM is equipped\r\nwith an upper-level ontology and a knowledge base providing extensive\r\ncoverage of entities of general importance. The ontologies and knowledge bases\r\ninvolved are handled using cutting edge Semantic Web technology and\r\nstandards, including RDF(S) repositories, ontology middleware and reasoning.\r\nFrom technical point of view, the platform allows KIM-based applications to\r\nuse it for automatic Semantic Annotation, content retrieval based on Semantic\r\nrestrictions, and querying and modifying the underlying ontologies and\r\nknowledge bases. This paper presents the KIM platform, with emphasize on its\r\narchitecture, interfaces, tools, and other technical issues.