Survey of semantic annotation platforms
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paper_title: Methodology for Reliable Schema Development and Evaluation of Manual Annotations

paper_content:

The quality of manual annotations of linguistic data depends on the use of reliable coding schemas as well as on the ability of human annotators to handle them appropriately. As is well known from a wide range of previous experiences annotations using highly complex coding schemas often lead to unacceptable annotation quality. Reducing complexity might make schemas easier to handle, but in this way valuable information needed for more sophisticated applications is excluded as well. In order to deal with this problem, we developed a systematic approach to schema development, which allows for developing coding schemas for fine-grained semantic annotations while systematically securing the quality of such annotations. For illustration, we present examples from two projects where text and speech data are annotated.
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paper_title: Semantic Word Processing for Content Authors

paper_content:

Document authors cannot routinely afford the overhead imposed by current semantic annotation tools. Some characteristics of their task can be exploited to provide them with a tool that will reduce the effort required to create both the document content and their accompanying semantic annotations. SemanticWord is such a semantic annotation tool. SemanticWord is an environment based in MS Word that integrates content and markup authoring, providing customizable tools that allow simultaneous generation of content and semantic annotations, an annotation scheme that allows annotations to be reused when content is reused, a customizable library of templates containing partially annotated text, and an automatic information extraction system with the tools for refining and augmenting its output.
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paper_title: Ontology Based Semantic Annotation for Enhancing Mobility Support for Visually Impaired Web Users

paper_content:

1. ABSTRACT We have previously shown that the mobility, or ease of travel, of visually impaired Web users is reduced since most Web pages are usually designed for visual interaction[7]. Therefore, in a visually impaired person’s environment objects that support travel are missing or inaccessible altogether. We aim to enhance the experience of visually impaired Web travellers by using annotation and Semantic Web technologies. In [17], we have proposed a semi-automated tool which encodes techniques for the support of travel upon the Web. The main goal of this tool is to analyse Web pages to identify objects that support mobility and travel, discover their roles, annotate them and transform pages based on these annotations to enhance the provided mobility support. This paper mainly presents the annotation part of the tool and provides some transformation examples which are based on the annotations. The main message of this paper is that visually impaired Web users could also benefit from the Semantic Webtechnologies and here we demonstrate a possible approach to achieve that.
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paper_title: MnM: Ontology Driven Semi-Automatic and Automatic Support for Semantic Markup

paper_content:

An important precondition for realizing the goal of a semantic web is the ability to annotate web resources with semantic information. In order to carry out this task, users need appropriate representation languages, ontologies, and support tools. In this paper we present MnM, an annotation tool which provides both automated and semi-automated support for annotating web pages with semantic contents. MnM integrates a web browser with an ontology editor and provides open APIs to link to ontology servers and for integrating information extraction tools. MnM can be seen as an early example of the next generation of ontology editors, being web-based, oriented to semantic markup and providing mechanisms for large-scale automatic markup of web pages.
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paper_title: Automatic Semantic Annotation using Unsupervised Information Extraction and Integration

paper_content:

In this paper we propose a methodology to learn to automatically annotate domain-specific information from large repositories (e.g. Web sites) with minimum user intervention. The methodology is based on a combination of information extraction, information integration and machine learning techniques. Learning is seeded by extracting information from structured sources (e.g. databases and digital libraries). Retrieved information is then used to partially annotate documents. These annotated documents are used to bootstrap learning for simple Information Extraction (IE) methodologies, which in turn will produce more annotations used to annotate more documents. It will be used to train more complex IE engines and the cycle will keep on repeating itself until the required information is obtained. The user intervention is limited to providing an initial URL and to correct information if it is the case when the computation is finished. The revised annotation can then be reused to provide further training and therefore getting more information and/or more precision.
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paper_title: Extracting Patterns and Relations from the World Wide Web

paper_content:

The World Wide Web is a vast resource for information. At the same time it is extremely distributed. A particular type of data such as restaurant lists may be scattered across thousands of independent information sources in many different formats. In this paper, we consider the problem of extracting a relation for such a data type from all of these sources automatically. We present a technique which exploits the duality between sets of patterns and relations to grow the target relation starting from a small sample. To test our technique we use it to extract a relation of (author,title) pairs from the World Wide Web.
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paper_title: WordNet: An Electronic Lexical Database

paper_content:

A teaching device to acquaint dental students and also patients with endodontic root canal techniques performed by dentists and utilizing an electronic oscillator having a scale reading in electric current measurement and a pair of electrical circuit conductors being connected at one end to the terminals of the oscillator and the opposite ends thereof respectively being connectable to one or more small diameter metal wires which simulate dental reamers and files which are movable in root canal-simulating passages of uniform diameter complementary to that of said wires and formed in a transparent model of a human tooth including a root and cusp thereon and mounted in a transparent enclosure in which the root portion of the tooth extends with the cusp of the model extending above the upper end of the enclosure.
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paper_title: Automatic Semantic Annotation using Unsupervised Information Extraction and Integration

paper_content:

In this paper we propose a methodology to learn to automatically annotate domain-specific information from large repositories (e.g. Web sites) with minimum user intervention. The methodology is based on a combination of information extraction, information integration and machine learning techniques. Learning is seeded by extracting information from structured sources (e.g. databases and digital libraries). Retrieved information is then used to partially annotate documents. These annotated documents are used to bootstrap learning for simple Information Extraction (IE) methodologies, which in turn will produce more annotations used to annotate more documents. It will be used to train more complex IE engines and the cycle will keep on repeating itself until the required information is obtained. The user intervention is limited to providing an initial URL and to correct information if it is the case when the computation is finished. The revised annotation can then be reused to provide further training and therefore getting more information and/or more precision.
---
paper_title: Extracting Patterns and Relations from the World Wide Web

paper_content:

The World Wide Web is a vast resource for information. At the same time it is extremely distributed. A particular type of data such as restaurant lists may be scattered across thousands of independent information sources in many different formats. In this paper, we consider the problem of extracting a relation for such a data type from all of these sources automatically. We present a technique which exploits the duality between sets of patterns and relations to grow the target relation starting from a small sample. To test our technique we use it to extract a relation of (author,title) pairs from the World Wide Web.
---
paper_title: MnM: Ontology Driven Semi-Automatic and Automatic Support for Semantic Markup

paper_content:

An important precondition for realizing the goal of a semantic web is the ability to annotate web resources with semantic information. In order to carry out this task, users need appropriate representation languages, ontologies, and support tools. In this paper we present MnM, an annotation tool which provides both automated and semi-automated support for annotating web pages with semantic contents. MnM integrates a web browser with an ontology editor and provides open APIs to link to ontology servers and for integrating information extraction tools. MnM can be seen as an early example of the next generation of ontology editors, being web-based, oriented to semantic markup and providing mechanisms for large-scale automatic markup of web pages.
---
paper_title: MnM: Ontology Driven Semi-Automatic and Automatic Support for Semantic Markup

paper_content:

An important precondition for realizing the goal of a semantic web is the ability to annotate web resources with semantic information. In order to carry out this task, users need appropriate representation languages, ontologies, and support tools. In this paper we present MnM, an annotation tool which provides both automated and semi-automated support for annotating web pages with semantic contents. MnM integrates a web browser with an ontology editor and provides open APIs to link to ontology servers and for integrating information extraction tools. MnM can be seen as an early example of the next generation of ontology editors, being web-based, oriented to semantic markup and providing mechanisms for large-scale automatic markup of web pages.
---
paper_title: Automatic Semantic Annotation using Unsupervised Information Extraction and Integration

paper_content:

In this paper we propose a methodology to learn to automatically annotate domain-specific information from large repositories (e.g. Web sites) with minimum user intervention. The methodology is based on a combination of information extraction, information integration and machine learning techniques. Learning is seeded by extracting information from structured sources (e.g. databases and digital libraries). Retrieved information is then used to partially annotate documents. These annotated documents are used to bootstrap learning for simple Information Extraction (IE) methodologies, which in turn will produce more annotations used to annotate more documents. It will be used to train more complex IE engines and the cycle will keep on repeating itself until the required information is obtained. The user intervention is limited to providing an initial URL and to correct information if it is the case when the computation is finished. The revised annotation can then be reused to provide further training and therefore getting more information and/or more precision.
---
paper_title: SemTag and seeker: bootstrapping the semantic web via automated semantic annotation

paper_content:

This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date.We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.
---
paper_title: MnM: Ontology Driven Semi-Automatic and Automatic Support for Semantic Markup

paper_content:

An important precondition for realizing the goal of a semantic web is the ability to annotate web resources with semantic information. In order to carry out this task, users need appropriate representation languages, ontologies, and support tools. In this paper we present MnM, an annotation tool which provides both automated and semi-automated support for annotating web pages with semantic contents. MnM integrates a web browser with an ontology editor and provides open APIs to link to ontology servers and for integrating information extraction tools. MnM can be seen as an early example of the next generation of ontology editors, being web-based, oriented to semantic markup and providing mechanisms for large-scale automatic markup of web pages.
---
paper_title: SemTag and seeker: bootstrapping the semantic web via automated semantic annotation

paper_content:

This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date.We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.
---
paper_title: Automatic Semantic Annotation using Unsupervised Information Extraction and Integration

paper_content:

In this paper we propose a methodology to learn to automatically annotate domain-specific information from large repositories (e.g. Web sites) with minimum user intervention. The methodology is based on a combination of information extraction, information integration and machine learning techniques. Learning is seeded by extracting information from structured sources (e.g. databases and digital libraries). Retrieved information is then used to partially annotate documents. These annotated documents are used to bootstrap learning for simple Information Extraction (IE) methodologies, which in turn will produce more annotations used to annotate more documents. It will be used to train more complex IE engines and the cycle will keep on repeating itself until the required information is obtained. The user intervention is limited to providing an initial URL and to correct information if it is the case when the computation is finished. The revised annotation can then be reused to provide further training and therefore getting more information and/or more precision.
---

Title: Survey of Semantic Annotation Platforms

Section 1: Introduction
Description 1: Present the challenges and motivations for semantic annotations and provide an overview of manual and semi-automatic annotations.

Section 2: Platform Classification
Description 2: Describe the classification of semantic annotation platforms based on their annotation methods, including Pattern-based, Machine Learning-based, and Multistrategy approaches.

Section 3: Platform Overview
Description 3: Provide an overview of selected semantic annotation platforms, including their architecture, information extraction tools and methods, initial ontology, and other features.

Section 4: Evaluation
Description 4: Report the performance metrics of the various semantic annotation platforms discussed, using measures such as Precision, Recall, and F-measure.

Section 5: Platform Summary
Description 5: Summarize the distinguishing attributes of the platforms, discussing the impact of different entity detection methods and the necessity of extensible architectures.

Section 6: Conclusion
Description 6: Conclude the survey by summarizing the main findings and emphasizing the importance of evolving semantic annotation platforms.