A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized. In this paper we present an inclusive layered classification of Semantic Annotation challenges and discuss the most important issues in this field. Also, we review and analyze machine learning applications for solving semantic annotation problems. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation challenges and requirements

Automatic semantic annotation using unsupervised information extraction and integration

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.peer-reviewe

EliXR-TIME: A Temporal Knowledge Representation for Clinical Research Eligibility Criteria.

Effective clinical text processing requires accurate extraction and representation of temporal expressions. Multiple temporal information extraction models were developed but a similar need for extracting temporal expressions in eligibility criteria (e.g., for eligibility determination) remains. We identified the temporal knowledge representation requirements of eligibility criteria by reviewing 100 temporal criteria. We developed EliXR-TIME, a frame-based representation designed to support semantic annotation for temporal expressions in eligibility criteria by reusing applicable classes from well-known clinical temporal knowledge representations. We used EliXR-TIME to analyze a training set of 50 new temporal eligibility criteria. We evaluated EliXR-TIME using an additional random sample of 20 eligibility criteria with temporal expressions that have no overlap with the training data, yielding 92.7% (76 / 82) inter-coder agreement on sentence chunking and 72% (72 / 100) agreement on semantic annotation. We conclude that this knowledge representation can facilitate semantic annotation of the temporal expressions in eligibility criteria

Semantic annotation in ubiquitous healthcare skills-based learning environments

This paper describes initial work on developing a semantic annotation system for the augmentation of skills-based learning for Healthcare. Scenario driven skills-based learning takes place in an augmented hospital ward simulation involving a patient simulator known as SimMan. The semantic annotation software enables real-time annotations of these simulations for debriefing of the students, student self study and better analysis of the learning approaches of mentors. A description of the developed system is provided with initial findings and future directions for the work.<br/

The development of non-coding RNA ontology

Identification of non-coding RNAs (ncRNAs) has been significantly improved over the past decade. On the other hand, semantic annotation of ncRNA data is facing critical challenges due to the lack of a comprehensive ontology to serve as common data elements and data exchange standards in the field. We developed the Non-Coding RNA Ontology (NCRO) to handle this situation. By providing a formally defined ncRNA controlled vocabulary, the NCRO aims to fill a specific and highly needed niche in semantic annotation of large amounts of ncRNA biological and clinical data

Doc2RDFa: Semantic Annotation for Web Documents

Ever since its conception, the amount of data published on the worldwide web has been rapidly growing to the point where it has become an important source of both general and domain specific information. However, the majority of documents published online are not machine readable by default. Many researchers believe that the answer to this problem is to semantically annotate these documents, and thereby contribute to the linked "Web of Data". Yet, the process of annotating web documents remains an open challenge. While some efforts towards simplifying this process have been made in the recent years, there is still a lack of semantic content creation tools that integrate well with information worker toolsets. Towards this end, we introduce Doc2RDFa, an HTML rich text processor with the ability to automatically and manually annotate domain-specific Content

A HUMAN RESOURCE ONTOLOGY FOR RECRUITMENT PROCESS

In this paper we propose a model of ontology for the human resource domain. We emphasize the benefits resulting from the application of Semantic Web technologies in the recruitment process. We use currently available standards and classifications to develop a human resource ontology which gives us means for semantic annotation of job postings and applications. Furthermore, we outline the process of semantic matching which improves the quality of query results. Finally, we propose the architecture of an evaluation system based on Semantic Web technologies.human resource ontology, HR-XML, e-recruitment, semantic annotation.

Semantic annotation of Web APIs with SWEET

Recently technology developments in the area of services on the Web are marked by the proliferation of Web applications and APIs. The development and evolution of applications based on Web APIs is, however, hampered by the lack of automation that can be achieved with current technologies. In this paper we present SWEET - Semantic Web sErvices Editing Tool - a lightweight Web application for creating semantic descriptions of Web APIs. SWEET directly supports the creation of mashups by enabling the semantic annotation of Web APIs, thus contributing to the automation of the discovery, composition and invocation service tasks. Furthermore, it enables the development of composite SWS based applications on top of Linked Data

Living with the Semantic Gap: Experiences and remedies in the context of medical imaging

Semantic annotation of images is a key concern for the newly emerged applications of semantic multimedia. Machine processable descriptions of images make it possible to automate a variety of tasks from search and discovery to composition and collage of image data bases. However, the ever occurring problem of the semantic gap between the low level descriptors and the high level interpretation of an image poses new challenges and needs to be addressed before the full potential of semantic multimedia can be realised. We explore the possibilities and lessons learnt with applied semantic multimedia from our engagement with medical imaging where we deployed ontologies and a novel distributed architecture to provide semantic annotation, decision support and methods for tackling the semantic gap problem

Text-based Semantic Annotation Service for Multimedia Content in the Esperonto project

Within the Esperonto project, an integration of NLP, ontologies and other knowledge bases, is being performed with the goal to implement a semantic annotation service that upgrades the actual Web towards the emerging Semantic Web. Research is being currently conducted on how to apply the Esperonto semantic annotation service to text material associated with still images in web pages. In doing so, the project will allow for semantic querying of still images in the web, but also (automatically) create a large set of text-based semantic annotations of still images, which can be used by the Multimedia community in order to support the task of content indexing of image material, possibly combining the Esperonto type of annotations with the annotations resulting from image analysis