Content Ontology Design Patterns Qualities, Methods, and Tools
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Realising the Potential of Algal Biomass Production Through Semantic Web and Linked Data
LEAPS: Realising the Potential of Algal Biomass Production through Semantic Web and Linked data ∗ Monika Solanki Johannes Skarka Craig Chapman KBE Lab Karlsruhe Institute of KBE Lab Birmingham City University Technology (ITAS) Birmingham City University [email protected] [email protected] [email protected] ABSTRACT In order to derive fuels from biomass, algal operation plant Recently algal biomass has been identified as a potential sites are setup that facilitate biomass cultivation and con- source of large scale production of biofuels. Governments, version of the biomass into end use products, some of which environmental research councils and special interests groups are biofuels. Microalgal biomass production in Europe is are funding several efforts that investigate renewable energy seen as a promising option for biofuels production regarding production opportunities in this sector. However so far there energy security and sustainability. Since microalgae can be has been no systematic study that analyses algal biomass cultivated in photobioreactors on non-arable land this tech- potential especially in North-Western Europe. In this paper nology could significantly reduce the food vs. fuel dilemma. we present a spatial data integration and analysis frame- However, until now there has been no systematic analysis work whereby rich datasets from the algal biomass domain of the algae biomass potential for North-Western Europe. that include data about algal operation sites and CO2 source In [20], the authors assessed the resource potential for mi- sites amongst others are semantically enriched with ontolo- croalgal biomass but excluded all areas not between 37◦N gies specifically designed for the domain and made available and 37◦S, thus most of Europe. -
From Cataloguing Cards to Semantic Web 1
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Scientific Open-access Literature Archive and Repository Archeologia e Calcolatori 20, 2009, 111-128 REPRESENTING KNOWLEDGE IN ARCHAEOLOGY: FROM CATALOGUING CARDS TO SEMANTIC WEB 1. Introduction Representing knowledge is the basis of any catalogue. The Italian Ca- talogue was based on very valuable ideas, developed in the late 1880s, with the basic concept of putting objects in their context. Initially cataloguing was done manually, with typewritten cards. The advent of computers led to some early experimentations, and subsequently to the de�nition of a more formalized representation schema. The web produced a cultural revolution, and made the need for technological and semantic interoperability more evi- dent. The Semantic Web scenario promises to allow a sharing of knowledge, which will make the knowledge that remained unexpressed in the traditional environments available to any user, and allow objects to be placed in their cultural context. In this paper we will brie�y recall the principles of cataloguing and lessons learned by early computer experiences. Subsequently we will descri- be the object model for archaeological items, discussing its strong and weak points. In section 5 we present the web scenario and approaches to represent knowledge. 2. Cataloguing: history and principles The Italian Catalogue of cultural heritage has its roots in the experiences and concepts, developed in the late 1880s and early 1900s, by the famous art historian Adolfo Venturi, who was probably one of the �rst scholars to think explicitly in terms of having a frame of reference to describe works of art, emphasizing as the main issue the context in which the work had been produced. -
Automated Ontology Creation Using XML Schema Elements
Automated Ontology Creation using XML Schema Elements Samuel Suhas Singapogu, Paulo C. G. Costa, J. Mark Pullen C4I center, George Mason University Fairfax, VA, USA [ssingapo,pcosta,mpullen]@c4i.gmu.edu Abstract—Ontologies are commonly used to represent formal semantics in a computer system, usually capturing them in the Although ontologies are valuable assets in system model- form of concepts, relationships and axioms. Axioms convey ing, testing and analysis, the process of manually creating an asserted knowledge and support inferring new knowledge ontology for a complex system is inherently tedious and through logical reasoning. For complex systems, the process of error prone. Existing methods of knowledge discovery and creating ontologies manually can be tedious and error-prone. ontology creation usually are based on text mining of a data Many automated methods of knowledge discovery are based on mining domain text corpus, but current state-of-the-art meth- corpus for that domain. XML-based systems capture the ods using this approach fail to consider properly semantic data structure and syntax of all necessary and meaningful ele- embedded in XML schemata in complex systems. This paper ments in a XML schema, which therefore is a useful starting proposes a mapping method for identifying relevant semantic point for semantic analysis. In such systems, XML schemata data in XML schemata, automatically structuring and repre- have been shown to be a valuable resource of semantic data senting it in the form of a draft ontology. Concepts, concept [2]. Command and Control systems in the military context hierarchy and domain relationships from XML schema are support various functions, including commanding of forces mapped to relevant parts of an OWL ontology. -
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a platform for all that we know savas parastatidis http://savas.me savasp transition from web to apps increasing focus on information (& knowledge) rise of personal digital assistants importance of near-real time processing http://aptito.com/blog/wp-content/uploads/2012/05/smartphone-apps.jpg today... storing computing computers are huge amounts great tools for of data managing indexing example google and microsoft both have copies of the entire web (and more) for indexing purposes tomorrow... storing computing computers are huge amounts great tools for of data managing indexing acquisition discovery aggregation organization we would like computers to of the world’s information also help with the automatic correlation analysis and knowledge interpretation inference data information knowledge intelligence wisdom expert systems watson freebase wolframalpha rdbms google now web indexing data is symbols (bits, numbers, characters) information adds meaning to data through the introduction of relationship - it answers questions such as “who”, “what”, “where”, and “when” knowledge is a description of how the world works - it’s the application of data and information in order to answer “how” questions G. Bellinger, D. Castro, and A. Mills, “Data, Information, Knowledge, and Wisdom,” Inform. pp. 1–4, 2004 web – the data platform web – the information platform web – the knowledge platform foundation for new experiences “wisdom is not a product of schooling but of the lifelong attempt to acquire it” representative examples wolframalpha watson source: -
Knowledge Extraction for Hybrid Question Answering
KNOWLEDGEEXTRACTIONFORHYBRID QUESTIONANSWERING Von der Fakultät für Mathematik und Informatik der Universität Leipzig angenommene DISSERTATION zur Erlangung des akademischen Grades Doctor rerum naturalium (Dr. rer. nat.) im Fachgebiet Informatik vorgelegt von Ricardo Usbeck, M.Sc. geboren am 01.04.1988 in Halle (Saale), Deutschland Die Annahme der Dissertation wurde empfohlen von: 1. Professor Dr. Klaus-Peter Fähnrich (Leipzig) 2. Professor Dr. Philipp Cimiano (Bielefeld) Die Verleihung des akademischen Grades erfolgt mit Bestehen der Verteidigung am 17. Mai 2017 mit dem Gesamtprädikat magna cum laude. Leipzig, den 17. Mai 2017 bibliographic data title: Knowledge Extraction for Hybrid Question Answering author: Ricardo Usbeck statistical information: 10 chapters, 169 pages, 28 figures, 32 tables, 8 listings, 5 algorithms, 178 literature references, 1 appendix part supervisors: Prof. Dr.-Ing. habil. Klaus-Peter Fähnrich Dr. Axel-Cyrille Ngonga Ngomo institution: Leipzig University, Faculty for Mathematics and Computer Science time frame: January 2013 - March 2016 ABSTRACT Over the last decades, several billion Web pages have been made available on the Web. The growing amount of Web data provides the world’s largest collection of knowledge.1 Most of this full-text data like blogs, news or encyclopaedic informa- tion is textual in nature. However, the increasing amount of structured respectively semantic data2 available on the Web fosters new search paradigms. These novel paradigms ease the development of natural language interfaces which enable end- users to easily access and benefit from large amounts of data without the need to understand the underlying structures or algorithms. Building a natural language Question Answering (QA) system over heteroge- neous, Web-based knowledge sources requires various building blocks. -
Datatone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3
DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3 1University of Michigan, 2Adobe Research 3University of Illinois, Ann Arbor, MI San Francisco, CA Urbana Champaign, IL fgaotong,[email protected] fmirad,[email protected] [email protected] ABSTRACT to be both flexible and easy to use. General purpose spread- Answering questions with data is a difficult and time- sheet tools, such as Microsoft Excel, focus largely on offer- consuming process. Visual dashboards and templates make ing rich data transformation operations. Visualizations are it easy to get started, but asking more sophisticated questions merely output to the calculations in the spreadsheet. Asking often requires learning a tool designed for expert analysts. a “visual question” requires users to translate their questions Natural language interaction allows users to ask questions di- into operations on the spreadsheet rather than operations on rectly in complex programs without having to learn how to the visualization. In contrast, visual analysis tools, such as use an interface. However, natural language is often ambigu- Tableau,1 creates visualizations automatically based on vari- ous. In this work we propose a mixed-initiative approach to ables of interest, allowing users to ask questions interactively managing ambiguity in natural language interfaces for data through the visualizations. However, because these tools are visualization. We model ambiguity throughout the process of often intended for domain specialists, they have complex in- turning a natural language query into a visualization and use terfaces and a steep learning curve. algorithmic disambiguation coupled with interactive ambigu- Natural language interaction offers a compelling complement ity widgets. -
Directions in AI Research and Applications at Siemens Corporate
AI Magazine Volume 11 Number 1 (1991)(1990) (© AAAI) Research in Progress of linguistic phenomena. The com- Directions in AI Research putational work concerns questions of adequate processing models and algorithms, as embodied in the and Applications at actual interfaces being developed. These topics are explored in the Siemens Corporate Research framework of three projects: The nat- ural language consulting dialogue and Development project (Wisber) takes up research- oriented topics (descriptive grammar formalisms and linguistically adequate grammar specification, handling of Wolfram Buettner, Klaus Estenfeld, discourse, and so on), the data-access Hans Haugeneder, and Peter Struss project (Sepp) focuses on the practi- cal application of state-of-the-art technology, and the work on gram- mar-development environments ■ Many barriers exist today that prevent particular, Prolog extensions; and (4) (Ape) centers on the problem of ade- effective industrial exploitation of current design and analysis of neural networks. quate tools for specifying linguistic and future AI research. These barriers can The lab’s 26 researchers are orga- knowledge sources and efficient pro- only be removed by people who are work- nized into four groups corresponding cessing methods. ing at the scientific forefront in AI and to these areas. Together, they provide Wisber is jointly funded by the know potential industrial needs. Siemens with innovative software The Knowledge Processing Laboratory’s German government and several research and development concentrates in technologies, appropriate applications, industrial and academic partners. the following areas: (1) natural language prototypical implementations of AI The goal of the project is to develop interfaces to knowledge-based systems and systems, and evaluations of new a knowledge-based advice-giving databases; (2) theoretical and experimen- techniques and trends. -
A Framework for Ontology-Based Library Data Generation, Access and Exploitation
Universidad Politécnica de Madrid Departamento de Inteligencia Artificial DOCTORADO EN INTELIGENCIA ARTIFICIAL A framework for ontology-based library data generation, access and exploitation Doctoral Dissertation of: Daniel Vila-Suero Advisors: Prof. Asunción Gómez-Pérez Dr. Jorge Gracia 2 i To Adelina, Gustavo, Pablo and Amélie Madrid, July 2016 ii Abstract Historically, libraries have been responsible for storing, preserving, cata- loguing and making available to the public large collections of information re- sources. In order to classify and organize these collections, the library commu- nity has developed several standards for the production, storage and communica- tion of data describing different aspects of library knowledge assets. However, as we will argue in this thesis, most of the current practices and standards available are limited in their ability to integrate library data within the largest information network ever created: the World Wide Web (WWW). This thesis aims at providing theoretical foundations and technical solutions to tackle some of the challenges in bridging the gap between these two areas: library science and technologies, and the Web of Data. The investigation of these aspects has been tackled with a combination of theoretical, technological and empirical approaches. Moreover, the research presented in this thesis has been largely applied and deployed to sustain a large online data service of the National Library of Spain: datos.bne.es. Specifically, this thesis proposes and eval- uates several constructs, languages, models and methods with the objective of transforming and publishing library catalogue data using semantic technologies and ontologies. In this thesis, we introduce marimba-framework, an ontology- based library data framework, that encompasses these constructs, languages, mod- els and methods. -
Using a Natural Language Understanding System to Generate Semantic Web Content
Final version to appear, International Journal on Semantic Web and Information Systems, 3(4), 2007. (http://www.igi-pub.com/) 1 Using a Natural Language Understanding System to Generate Semantic Web Content Akshay Java, Sergei Nirneburg, Marjorie McShane, Timothy Finin, Jesse English, Anupam Joshi University of Maryland, Baltimore County, Baltimore MD 21250 {aks1,sergei,marge,finin,english1,joshi}@umbc.edu Abstract We describe our research on automatically generating rich semantic annotations of text and making it available on the Semantic Web. In particular, we discuss the challenges involved in adapting the OntoSem natural language processing system for this purpose. OntoSem, an implementation of the theory of ontological semantics under continuous development for over fifteen years, uses a specially constructed NLP-oriented ontology and an ontological- semantic lexicon to translate English text into a custom ontology-motivated knowledge representation language, the language of text meaning representations (TMRs). OntoSem concentrates on a variety of ambiguity resolution tasks as well as processing unexpected input and reference. To adapt OntoSem’s representation to the Semantic Web, we developed a translation system, OntoSem2OWL, between the TMR language into the Semantic Web language OWL. We next used OntoSem and OntoSem2OWL to support SemNews, an experimental web service that monitors RSS news sources, processes the summaries of the news stories and publishes a structured representation of the meaning of the text in the news story. Index Terms semantic web, OWL, RDF, natural language processing, information extraction I. INTRODUCTION A core goal of the development of the Semantic Web is to bring progressively more meaning to the information published on the Web. -
Transforming Recursive Relationships to OWL2 Components
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 10, 2019 Ontology Learning from Relational Databases: Transforming Recursive Relationships to OWL2 Components 1 Mohammed Reda CHBIHI LOUHDI Hicham BEHJA2 Research Laboratory on computer science innovation (LRII) LRI - Laboratory Faculty of Science Aïn Chock Casablanca ENSEM, Hassan II University Hassan II University, Casablanca, Morocco Casablanca, Morocco Abstract—Relational databases (RDB) are widely used as a ontologies provide classes, properties, individuals, and data backend for information systems, and contain interesting values and are stored as Semantic Web documents [3]. structured data (schema and data). In the case of ontology learning, RDB can be used as knowledge source. Multiple There are many approaches that transform a RDB to an approaches exist for building ontologies from RDB. They mainly ontology. Three main techniques are used: (1) Reverse use schema mapping to transform RDB components to Engineering, to convert the relational model to the conceptual ontologies. Most existing approaches do not deal with recursive model (which is considered as semantically richer than the relationships that can encapsulate good semantics. In this paper, relational model) or to retrieve lost information during the two technics are proposed for transforming recursive transformation of the conceptual model to the relational model, relationships to OWL2 components: (1) Transitivity mechanism (2) Schema Mapping, to convert relational model components and (2) Concept Hierarchy. The main objective of this work is to to ontology components, through the use of transformation build richer ontologies with deep taxonomies from RDB. rules and (3) Data Mining to analyze stored data in order to enrich the ontology. -
Ontology Life Cycle: a Survey on the Ontology and Its Development Steps
International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2015): 78.96 | Impact Factor (2015): 6.391 Ontology Life Cycle: A Survey on the Ontology and its Development Steps Eiman Alsiddig Altayeb Ibrahim1, Mohammed Awad Mohammed Ataelfadiel2 1Sudan University of Science and Technology, College of Computer Science and Information Technology, Khartoum – Sudan 2Computer Science dept. AL-Imam MuhommedIbn Saud Islamic University, AL-Ahsa, Kingdom of Saudi Arabia Abstract: In the last decades, the turnout has become great for the use of ontology in various fields, such as web technologies, database integration, multi agent systems, natural language processing, etc... The main goal of this paper is to answer generic questions about ontologies, such as: What is the ontology? Which are the different Types of ontologies? What is the purpose of the use of ontologies in an application? Which methods can I use to build ontology? During the paper we discussed the definition of Ontology, types of ontologies (Formal and Informal ontology) and its Component.Also, highlighted some of the languages used in the construction of the Ontologies beside the ontology Building Tools. In addition, we listed the agreed steps for building the Ontology. In conclusion, we did not overlook to mention some of the Ontology projects specifically in Holy Quran Domain. Keywords: Ontology Definition, Ontology Types, Ontology Building Tools, Ontology languages, Ontology components, Ontology Engineering 1. Introduction 2. Ontology Definition Historically, ontologies arise out of the branch of philosophy There are many interpretations about what ontology is. In fact, known as metaphysics, which deals with the nature of reality – hot discussions are often done in many meetings on ontology. -
An Approach to Formalizing Ontology Driven Semantic Integration: Concepts, Dimensions and Framework Wenlong Gao
Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2012 An Approach to Formalizing Ontology Driven Semantic Integration: Concepts, Dimensions and Framework Wenlong Gao Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY COLLEGE OF COMMUNICATION AND INFORMATION AN APPROACH TO FORMALIZING ONTOLOGY DRIVEN SEMANTIC INTEGRATION: CONCEPTS, DIMENSIONS AND FRAMEWORK By WENLONG GAO A Dissertation submitted to the School of Library and Information Studies In partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree Awarded: Spring Semester, 2012 Wenlong Gao defended this dissertation on December 9, 2011 The members of the supervisory committee were: Corinne Jörgensen Professor Directing Dissertation Daniel Schwartz University Representative Ian Douglas Committee Member Besiki Stvilia Committee Member The Graduate School has verified and approved the above-named committee members and certifies that the dissertation has been approved in accordance with university requirements. ii For my parents, I could not have done this without you. iii ACKNOWLEDGEMENTS This dissertation could not have been completed without the tremendous support and help of my dissertation committee, friends and family. First, I would like to thank Dr. Corinne Jörgensen, my dissertation chair and mentor. You supported me through this entire process, and encouraged me whenever I encountered challenges. I will always appreciate the trust and confidence you placed in me. It has been an honor and privilege to be your doctoral student. Second, I am thankful for other committee members, Dr. Ian Douglas, Dr. Besiki Stvilia, Dr. Daniel Schwartz, who inspired me not only throughout the dissertation development but also though my personal academic development.