The semantic web and language technology, its potential and practicalities: EUROLAN-2003

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

The Ontology is increasingly important for many information systems and Semantic Web, while the cost of constructing ontology is too much. In this paper, we propose an automatic approach for ontology construction to assist the knowledge engineers to construct the specific domain ontology from the documents, which are gathered from Internet. We hope to raise the automation level to make correctly and efficiently build the ontology while applying the method to different domains. For different domains, we propose an approach to extract new Chinese terms from the specific corpus automatically. And we use information retrieval, natural language processing, and soft computing techniques to find out the concepts of the ontology. In addition, we extend the concept of episode to construct an Episode Net. Using Episode Net, we can find out the static attributes, dynamic operations, and the associations between concepts of the ontology. Finally, we use object-oriented model to represent the ontology and then construct the ontology with four-layer object-oriented structure. The experimental results show that our approach can effectively assist ontology engineers to construct the domain ontology.

This paper reports on the early-stage development of an analytics framework to support the semantic integration of dynamic surveillance data across multiple scales to inform decision making for malaria eradication. We propose using the Semantic Web of Things (SWoT), a combination of Internet of Things (IoT) and semantic web technologies, to support the evolution and integration of dynamic malaria data sources and improve interoperability between different datasets generated through relevant IoT assets (e.g. computers, sensors, persons, and other smart objects and devices).

PurposeThe purpose of this paper is to explore useful Semantic Web technologies and ontology-based applications for digital libraries. It also investigates the perceptions of university librarians and academicians in Pakistan about Semantic Web technologies and their use in digital libraries.Design/methodology/approachAn exploratory research design based on Delphi research strategy was conducted to answer the research questions. Interviews were conducted with a purposive sample of 50 key informants including university librarians and academicians to explore their perceptions about Semantic Web technologies and their use in digital libraries. Thematic analysis of interview data was conducted to obtain results.FindingsThe results of this paper showed that DuraCloud, Semantic information mashup, OntoEdit and resource description framework (RDF) are the various Semantic Web applications which are useful for digital libraries to develop semantic relationships among digital contents and increase their accessibility in the web environment. Findings revealed that Semantic Web provides precise results and meets user information needs in an effective way. Results also showed that next-generation digital libraries use context-awareness technology, intelligent agent software and detecting sensors to analyze user information needs and provide dynamic information services. This paper recommended that librarians should embrace the use of emerging web technologies in libraries and offer library services through the medium of the web.Practical implicationsThis paper envisaged the future of digital library services and Semantic Web applications that can be used to re-structure metadata of digital library. This paper has practical implications for librarians to consider the useful applications of Semantic Web for digital library and enhance the interoperability of metadata among heterogeneous information systems. Practically, results obtained from this paper are highly useful for library schools and LIS teachers to up-date their curriculum by incorporating new contents related to web languages and Semantic Web applications for digital libraries.Originality/valueThis paper identifies various Semantic Web applications which are useful for developing Semantic Digital Libraries.

Advent of technologies like semantic web, multi-agent systems, web mining has changed the internet as knowledge provider. Web personalization offers a solution to the information overload problem in current web by providing users a personalized experience, considering their interest, behavior, context and emotions. Semantic web technology is based on use of software agents, ontologies and reasoning to add meaning to web information. An important technology for achieving personalization is the use of independent intelligent software agents. This work reviews, web personalization in the light of semantic web and software agent technology. A comparative study of recent works in the domain of web personalization has been carried out for this purpose. This review highlights ample scope for application of intelligent agents in the web personalization domain for solving many existing issues like personalized content management, user profile learning, modeling and adaptive interactions with users.

Exploiting tourism destinations’ knowledge in an RDF-based P2P network

Since archaeology in particular and humanities in general are interdisciplinary disciplines, there is an imperious need to enhance the accessibility and harmonise data integration, given their varied resource types (books, archives, scientific data, GIS, 3D models etc) and their different conceptual and technological structures and standards. A factor that further reduces accessibility and query performances is related to storing, such as physical location of data, language, and interface. The advent of Semantic Web technology represents an important advance in creating networks of archaeological knowledge based on various resources available on-line. While the valuable use of CIDOC-CRM (Comite International pour la Documentation -Conceptual Reference Model) ontology for specific CH (Cultural Heritage) domains (e.g. museums, libraries, etc) has been partially demonstrated, the interdisciplinary implementations are limited. In this article, we explore the potential of the CIDOC-CRM for a cross-domain implementation of CH data integration. We conducted tha mappings of different on-line resources related to Stonehenge to CIDOC-CRM, and evaluated them in the MAD database, a web-based application that manages natively XML-based datasets. The result ensures the validity of mapping mechanism and the semantic integration of CH data from different sources.

Semantic mashup of biomedical data

Universidade Federal de Sao Carlos (UFSCar), Dept Letras, BR-13565905 Sao Carlos, SP, Brazil

Semantic Web for Health Care and Life Sciences: a review of the state of the art

<div><p><em> Machine Translation has been an area of linguistic research for almost more than two decades now. But it still remains a very challenging task for devising an automated system which will deliver accurate translations of the natural languages. However, great strides have been made in this field with more success owing to the development of technologies of the web and off late there is a renewed interest in this area of research. </em></p><p><em> Technological advancements in the preceding two decades have influenced Machine Translation in a considerable way. Several MT approaches including Statistical Machine Translation greatly benefitted from these advancements, basically making use of the availability of extensive corpora. Web technology web3.0 uses the semantic web technology which represents any object or resource in the web both syntactically and semantically. This type of representation is very much useful for the computing systems to search any content on the internet similar to lexical search and improve the internet based translations making it more effective and efficient.</em></p><p><em> In this paper we propose a technique to improve existing statistical Machine Translation methods by making use of semantic web technology. Our focus will be on Tamil and Tamil to English MT. The proposed method could successfully integrate a semantic web technique in the process of WSD which forms part of the MT system. The integration is accomplished by using the capabilities of RDFS and OWL into the WSD component of the MT model. The contribution of this work lies in showing that integrating a Semantic web technique in the WSD system significantly improves the performance of a statistical MT system for a translation from Tamil to English.</em></p></div><em> In this paper we assume the availability of large corpora in Tamil language and specific domain based ontologies with Tamil semantic web technology using web3.0. We are positive on the expansion and development of Tamil semantic web and subsequently infer that Tamil to English MT will greatly improve the disambiguation concept apart from other related benefits. This method could enable the enhancement of translation quality by improving on word sense disambiguation process while text is translated from Tamil to English language. This method can also be extended to other languages such as Hindi and Indian Languages.</em>

Question-Answering (QA) system is improved version of search engines or can be stated as specific Search engines because search engines deliver data in the form of URLs whereas Question-Answering system produces direct answer to the query asked. That's why more focus has been put on developing better Question Answering systems that could respond to large variety of user query. Semantic Web Technology has aided to the development of such systems from past many years because it could add meaning to the query being asked. Such systems are more efficient than the systems developed in other languages. In this paper, we have proposed a QA system of JAVA Programming Language, where knowledge will be represented in the form of Ontology and based on relations derived from domain knowledge to obtained final answer. Information present in Ontology is represented in the form of tables to feed in knowledge base. It combines the techniques of Natural Language Processing and Semantic Web.

Because the ultimate purpose of the Semantic Web is to help users locate, organize, and process information, we strongly believe that it should be grounded in the information access method humans are most comfortable with—natural language. However, the Resource Description Framework (RDF), the foundation of the Semantic Web, was designed to be easily processed by computers, not humans. To render RDF friendlier to humans, we propose to augment it with natural language annotations, or metadata written in everyday language. We argue that natural language annotations are not only intuitive and effective, but can also accelerate the pace with which the Semantic Web is being adopted. We demonstrate the use of natural language annotations from within Haystack, an end user Semantic Web platform that also serves as a testbed for our ideas. In addition to a prototype SemanticWeb question answering system, we describe other opportunities for marrying natural language and Semantic Web technology.

It's easy to see how, in post-Soviet Eastern Europe, a five-day discussion of connectivity and information sharing could be read as a metaphor for a new era of social openness and understanding. That said, the Twelfth International World Wide Web Conference held in May in Budapest, Hungary nonetheless focused largely on nuts and bolts, with tracks devoted to rising trends in electronic security, data mining, and e-commerce. But the most intriguing of the tracks might well have been those about the Semantic Web, Semantic Web services, and the connections between them. The Semantic Web remains in its infancy, but the technologies behind it could still promise to dramatically rewrite the way the Internet operates in the coming years.

Constitute is a web application developed for the analysis of the text of written constitutions. At the heart of the application is a set of constitutional excerpts that are encoded with tags derived from a conceptual inventory used in the Comparative Constitutions Project (CCP), a related dataset that records the characteristics of national constitutions since 1789. The data collection deposited with QDR is a complete set of excerpts, together with topical tags, from in-force constitutions, as well as a few draft and historical constitutions, appearing on the site as of December 26, 2017. Data Abstract The data include the constitutions of nearly all independent states, as identified by Ward and Gledistch, in force as of December 26, 2017. Constitute’s conceptual organization was informed by its intended use as a public-facing data product. Since Constitute is intended to be usable for members of the public as well as real-life constitution-writers and academics, the project’s texts, content tags, and conceptual inventory are all organized with an eye towards readability and parsimony. This guiding principle is most noticeable in the dataset’s conceptual inventory, which covers roughly half as many items as the original Comparative Constitutions Project (CCP) variable set. Constitute’s hierarchical variable organization — enabled by the Semantic Web technology underlying the project — is informed by similar concerns. To enhance usability (both visually on the project’s website and in downstream statistical applications), Constitute’s conceptual hierarchy is oriented in a shallow but wide fashion, with few hierarchical layers and many sub-categories at each layer. In compiling data for QDR, we included all information which is available on the Constitute website at the time of submission. These data consist of Semantic Web (.nt) files that include excerpts from nearly all in-force constitutions as of 2017, with accompanying content tags. The data follow a conceptual framework developed by the Principal Investigators in the Comparative Constitutions Project (CCP), a related dataset that records some 600 characteristics of national constitutions enacted since 1789. Documentation and data for that project, including data and texts for older constitutions, are available on the project website, at https://comparativeconstitutionsproject.org . Data Model The textual excerpts are derived from the CCP’s core dataset, which records a comprehensive set of characteristics of historical constitutions. The Constitute dataset consists of two main pieces: (1) a set of conceptual tags and a related conceptual ontology; and (2) a collection of cleaned and tagged constitutional texts. Constitute tracks some 300 unique conceptual tags at the paragraph level of each individual constitution, which are organized into a hierarchical, extensible ontology. Example tags might include “Free Expression,” “Head of State Powers,” or “National Motto.” The Constitute conceptual inventory is available in QDR as an OWL file, a graph-based Semantic Web format that allows for formal descriptions of hierarchical classification schemes. In accordance with the general Semantic Web framework, OWL files can be easily expanded to include new categories and relationships, allowing researchers to extend the Constitute coding scheme for their own purposes. Each constitutional text in Constitute is represented in a related hierarchical format. Currently, the QDR sample consists of nearly all in-force national constitutions as of 2017, as well as a few draft and historical texts. Files Description The data collection deposited with QDR is a complete set of excerpts -- together with topical tags – as they appeared on the site as of December 26, 2017. Metadata about each constitution is also provided (as an .nt file), along with the project’s conceptual inventory (as an .owl file) and copyright information (as a spreadsheet).