DOCSLIB.ORG

Intuitive Ontology Authoring Using Controlled Natural Language

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Intuitive Ontology Authoring Using Controlled Natural Language Intuitive Ontology Authoring using Controlled Natural Language Ronald Denaux Submitted in accordance with the requirements for the degree of Doctor of Philosophy University of Leeds School of Computing February 2013 The candidate confirms that the work submitted is his/her own, except where work which has formed part of jointly authored publications has been included. The contribution of the candidate and the other au- thors to this work has been explicitly indicated below. The candidate confirms that appropriate credit has been given within the thesis where reference has been made to the work of others. Chapter 2, the background literature, is based on: R. Denaux, C. Dolbear, G. Hart, V. Dimitrova, and A. G. Cohn, Supporting domain experts to construct conceptual ontologies: A holistic approach, Web Semantics: Science, Services and Agents on the World Wide Web, vol. 9, no. 2, pp. 113-127, Jul. 2011. The original background literature for this paper was based on my own work and the Chapter in the thesis updates and goes into more detail about the related work. The technical description of ROOin Chapter 3 is based on: R. Denaux, V. Dimitrova, A. Cohn, C. Dolbear, and G. Hart, Rabbit to OWL: Ontology Au- thoring with a CNL-Based Tool, in Controlled Natural Language, vol. 5972, N. Fuchs, Ed. Springer Berlin / Heidelberg, 2010, pp. 246-264. This paper was mostly written by me, and reviewed and improved by the other authors. The design of the Rabbit controlled natural language and the Kanga methodology was done by the Ordnance Survey (C. Dolbear and G. Hart). An initial Rab- bit BNF was also done by the Ordnance Survey. I implemented the Rabbit parser on my own. During the implementation of the parser I updated the Rabbit BNF where necessary in collaboration with G. Hart. The initial design of ROO and the guide dog functionality was done in collaboration with Ordnance Survey and V. Dimitrova. The implementation of ROOand the guide dog is my own work. The evaluation of ROOin Chapter 3 is based on: V. Dimitrova, R. Denaux, G. Hart, C. Dolbear, I. Holt, and A. G. Cohn, Involving Domain Experts in Authoring OWL Ontologies, in Proceedings of the 7th International Semantic Web Conference, ISWC, 2008, pp. 116. The evaluation discussed in this work was done by me and V. Dimitrova with suppervision by the other authors. G. Hart also contributed to the analysis of the results together with V. Dimitrova. Parts of Chapter 4 were published as: R. Denaux, D. Thakker, V. Dimitrova, and A. G. Cohn, Interactive Semantic Feedback for Intuitive Ontology Authoring, in 7th International Conference on Formal Ontology in Information Systems, 2012. This research was driven by me with guidance from my supervisors. V. Dimitrova and D. Thakker helped to design and perform the evaluation study, while A. G. Cohn supervised the work. This copy has been supplied on the understanding that it is copyright material and that no quotation from the thesis may be published with- out proper acknowledgement. ©2013 University of Leeds and Ronald Denaux to... domain experts, I suppose. Acknowledgements My immediate thanks to both of my supervisors for their valuable comments and nudges in the right direction. Another thanks to them for their dedication, while at the same time giving me the space to go in my own direction. More thanks go to Glen Hart for his trust in me and for supporting the work on ROO. I am also grateful to the other members of the Ordnance Survey {Cathy Dolbear, Ian Holt, John Goodwin, Paula Engelbrecht{ team with whom I collaborated and helped me to get ROO started in the right direction. Thanks to the members of the reading group through the years for keeping things lively and interesting and for providing feedback on my work. Special thanks to Dhaval Thacker for helping out with the Entendre evaluation. Thanks as well to Lora Aroyo, who first brought me in contact with this research area and with Leeds. Finally, also a big thanks to all those who have provided valuable software that I could reuse and build on for my research. Special thanks to Brian Davis and Kaarel Kaljurand for providing their tools. Thanks go, of course, to my parents, brother, close friends and good teachers who have all contributed in one way or another to this thesis being created. A final huge thanks goes to Clare for enduring me during those days before a big deadline and for helping me put research aside once in a while. Abstract Ontologies have been proposed and studied in the last couple of decades as a way to capture and share people's knowledge about the world in a way that is processable by computer systems. Ontologies have the potential to serve as a bridge between the human conceptual understanding of the world and the data produced, processed and stored in computer systems. However, ontologies so far have failed to gather widespread adoption, failing to realise the original vision of the semantic web as a next generation of the world wide web: where everyone would be able to contribute and interlink their data and knowledge as easily as they can contribute and interlink their websites. One of the main reasons for this lack of widespread adoption of ontologies is the steep learning curve for authoring them: most people find it too difficult to learn the syntax and formal semantics of ontology languages. Most research has tried to alleviate this problem by finding ways to help people to collaborate with knowledge engineers when building ontologies; this approach however, requires the wide availability of knowledge engineers, who in practice are scarce. In the context of the semantic web, recent research has started looking at ways to di- rectly capture knowledge from domain experts as ontologies. One such approach advocates the use of Controlled Natural Languages (CNL) as a promising way to alleviate the syntactical impediment to writing ontological constructs. However, not much is yet known about the capabilities and limitations of CNL-based ontol- ogy authoring by domain experts. It is also unknown what type of automatic tool support can and should be provided to novice ontology authors, although such intelligent tool support is becoming possible due to advances in reasoning with existing ontologies and other related areas such as natural language processing. This PhD investigates how CNL-based ontology authoring systems can make ontology authoring more accessible to domain experts by providing intelligent tool support. In particular, this thesis iteratively investigates the impact of pro- viding various types of intelligent tool support for authoring ontologies using the Web Ontology Language (OWL) and a controlled natural language called Rabbit. After each iteration of added tool support, we evaluate how it impacts the ontol- ogy authoring process and what are the main limitations of the resulting ontology authoring system. Based on the found limitations, we decide which further tool support would be most beneficial to novice ontology authors. This methodol- ogy resulted in iteratively providing support for (i) understanding the syntactic capabilities and limitations of the chosen controlled natural language; (ii) fol- lowing appropriate ontology engineering methodologies; (iii) fostering awareness about the logical consequences of adding new knowledge to an ontology and (iv) interacting with the ontology authoring system via dialogues. The main contributions of this PhD are (i) showing that domain experts benefit from guidance about the ontology authoring process and understandable syntax error messages for finding the correct CNL syntax; (ii) the definition of a framework to integrate the syntactical and semantic analyses of ontology authors' inputs; (iii) showing that intuitive feedback about the integration of ontology authors' inputs into an existing ontology benefits ontology authors as they become aware of potential ontology defects; (iv) the definition of a framework to analyse and describe ontology authoring in terms of dialogue moves and their discourse structure. Abbreviations ACE Attempto Controlled English AG Annotation Graph BNF Backus Naur Form cf communicative function CL Controlled Language CNL Controlled Natural Language DE Domain Expert DL Description Logic dm dialogue move fs functional segment fundep functional dependency GUI Graphical User Interface IC Informational Component ISU Information-State-Update KE Knowledge Engineer NL Natural Language NLP Natural Language Processing O Ontology OA Ontology Authoring or Ontology Author OAs Ontology Authors POI Point of Interest POS Part of Speech sc semantic content SW Semantic Web Contents 1 Introduction 1 2 Intuitive Ontology Authoring 6 2.1 Ontology Authoring . .6 2.1.1 Preliminaries . .7 2.1.2 Involving Domain Experts . 10 2.2 CNL-Based Ontology Authoring . 16 2.2.1 CNLs for Knowledge Formulation . 17 2.2.2 Syntactic Support . 22 2.3 Discussion and Open Issues . 23 3 ROO: CNL-Based Ontology Authoring 27 3.1 An Existing Methodology for Involving Domain Experts in Ontol- ogy Authoring . 28 3.1.1 The Rabbit Controlled Natural Language . 29 3.2 ROO: Rabbit to OWL Ontology Authoring Tool . 32 3.2.1 Architectural Overview . 33 3.3 Providing Domain Expert-Specific Tool Support for Rabbit .... 35 3.3.1 RabbitParser . 35 3.3.2 Handling Ambiguity in Rabbit sentences . 39 3.3.3 Support for Editing Rabbit .................. 45 3.3.4 Support for Following Ontology Engineering Methodology 47 3.4 Evaluation . 50 3.4.1 Preliminary User Studies . 50 3.4.2 Experimental Design . 51 9 CONTENTS 3.4.3 Results . 54 3.4.4 Benefits and Limitations of CNL-based Interaction for On- tology Authoring . 63 3.5 Practical Experience with ROO ................... 67 3.6 Conclusion . 70 4 Entendre: Understanding Ontology Authors' Inputs 72 4.1 Analysing Ontology Authors' Inputs . 74 4.2 Approaches to Understanding Ontology Authors .
Load more

Recommended publications
  • A Data-Driven Framework for Assisting Geo-Ontology Engineering Using a Discrepancy Index
    University of California Santa Barbara A Data-Driven Framework for Assisting Geo-Ontology Engineering Using a Discrepancy Index A Thesis submitted in partial satisfaction of the requirements for the degree Master of Arts in Geography by Bo Yan Committee in charge: Professor Krzysztof Janowicz, Chair Professor Werner Kuhn Professor Emerita Helen Couclelis June 2016 The Thesis of Bo Yan is approved. Professor Werner Kuhn Professor Emerita Helen Couclelis Professor Krzysztof Janowicz, Committee Chair May 2016 A Data-Driven Framework for Assisting Geo-Ontology Engineering Using a Discrepancy Index Copyright c 2016 by Bo Yan iii Acknowledgements I would like to thank the members of my committee for their guidance and patience in the face of obstacles over the course of my research. I would like to thank my advisor, Krzysztof Janowicz, for his invaluable input on my work. Without his help and encour- agement, I would not have been able to find the light at the end of the tunnel during the last stage of the work. Because he provided insight that helped me think out of the box. There is no better advisor. I would like to thank Yingjie Hu who has offered me numer- ous feedback, suggestions and inspirations on my thesis topic. I would like to thank all my other intelligent colleagues in the STKO lab and the Geography Department { those who have moved on and started anew, those who are still in the quagmire, and those who have just begun { for their support and friendship. Last, but most importantly, I would like to thank my parents for their unconditional love.
    [Show full text]
  • Semi-Automated Ontology Based Question Answering System Open Access
    Journal of Advanced Research in Computing and Applications 17, Issue 1 (2019) 1-5 Journal of Advanced Research in Computing and Applications Journal homepage: www.akademiabaru.com/arca.html ISSN: 2462-1927 Open Semi-automated Ontology based Question Answering System Access 1, Khairul Nurmazianna Ismail 1 Faculty of Computer Science, Universiti Teknologi MARA Melaka, Malaysia ABSTRACT Question answering system enable users to retrieve exact answer for questions submit using natural language. The demand of this system increases since it able to deliver precise answer instead of list of links. This study proposes ontology-based question answering system. Research consist explanation of question answering system architecture. This question answering system used semi-automatic ontology development (Ontology Learning) approach to develop its ontology. Keywords: Question answering system; knowledge Copyright © 2019 PENERBIT AKADEMIA BARU - All rights reserved base; ontology; online learning 1. Introduction In the era of WWW, people need to search and get information fast online or offline which make people rely on Question Answering System (QAS). One of the common and traditionally use QAS is Frequently Ask Question site which contains common and straight forward answer. Currently, QAS have emerge as powerful platform using various techniques such as information retrieval, Knowledge Base, Natural Language Processing and Hybrid Based which enable user to retrieve exact answer for questions posed in natural language using either pre-structured database or a collection of natural language documents [1,4]. The demand of QAS increases day by day since it delivers short, precise and question-specific answer [10]. QAS with using knowledge base paradigm are better in Restricted- domain QA system since it ability to focus [12].
    [Show full text]
  • Artificial Consciousness and the Consciousness-Attention Dissociation
    Consciousness and Cognition 45 (2016) 210–225 Contents lists available at ScienceDirect Consciousness and Cognition journal homepage: www.elsevier.com/locate/concog Review article Artificial consciousness and the consciousness-attention dissociation ⇑ Harry Haroutioun Haladjian a, , Carlos Montemayor b a Laboratoire Psychologie de la Perception, CNRS (UMR 8242), Université Paris Descartes, Centre Biomédical des Saints-Pères, 45 rue des Saints-Pères, 75006 Paris, France b San Francisco State University, Philosophy Department, 1600 Holloway Avenue, San Francisco, CA 94132 USA article info abstract Article history: Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to Received 6 July 2016 implement sophisticated forms of human intelligence in machines. This research attempts Accepted 12 August 2016 to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable Keywords: once we overcome short-term engineering challenges. We believe, however, that phenom- Artificial intelligence enal consciousness cannot be implemented in machines. This becomes clear when consid- Artificial consciousness ering emotions and examining the dissociation between consciousness and attention in Consciousness Visual attention humans. While we may be able to program ethical behavior based on rules and machine Phenomenology learning, we will never be able to reproduce emotions or empathy by programming such Emotions control systems—these will be merely simulations. Arguments in favor of this claim include Empathy considerations about evolution, the neuropsychological aspects of emotions, and the disso- ciation between attention and consciousness found in humans.
    [Show full text]
  • Computability and Complexity
    Computability and Complexity Lecture Notes Herbert Jaeger, Jacobs University Bremen Version history Jan 30, 2018: created as copy of CC lecture notes of Spring 2017 Feb 16, 2018: cleaned up font conversion hickups up to Section 5 Feb 23, 2018: cleaned up font conversion hickups up to Section 6 Mar 15, 2018: cleaned up font conversion hickups up to Section 7 Apr 5, 2018: cleaned up font conversion hickups up to the end of these lecture notes 1 1 Introduction 1.1 Motivation This lecture will introduce you to the theory of computation and the theory of computational complexity. The theory of computation offers full answers to the questions, • what problems can in principle be solved by computer programs? • what functions can in principle be computed by computer programs? • what formal languages can in principle be decided by computer programs? Full answers to these questions have been found in the last 70 years or so, and we will learn about them. (And it turns out that these questions are all the same question). The theory of computation is well-established, transparent, and basically simple (you might disagree at first). The theory of complexity offers many insights to questions like • for a given problem / function / language that has to be solved / computed / decided by a computer program, how long does the fastest program actually run? • how much memory space has to be used at least? • can you speed up computations by using different computer architectures or different programming approaches? The theory of complexity is historically younger than the theory of computation – the first surge of results came in the 60ties of last century.
    [Show full text]
  • Bi-Directional Transformation Between Normalized Systems Elements and Domain Ontologies in OWL
    Bi-directional Transformation between Normalized Systems Elements and Domain Ontologies in OWL Marek Suchanek´ 1 a, Herwig Mannaert2, Peter Uhnak´ 3 b and Robert Pergl1 c 1Faculty of Information Technology, Czech Technical University in Prague, Thakurova´ 9, Prague, Czech Republic 2Normalized Systems Institute, University of Antwerp, Prinsstraat 13, Antwerp, Belgium 3NSX bvba, Wetenschapspark Universiteit Antwerpen, Galileilaan 15, 2845 Niel, Belgium Keywords: Ontology, Normalized Systems, Transformation, Model-driven Development, Ontology Engineering, Software Modelling. Abstract: Knowledge representation in OWL ontologies gained a lot of popularity with the development of Big Data, Artificial Intelligence, Semantic Web, and Linked Open Data. OWL ontologies are very versatile, and there are many tools for analysis, design, documentation, and mapping. They can capture concepts and categories, their properties and relations. Normalized Systems (NS) provide a way of code generation from a model of so-called NS Elements resulting in an information system with proven evolvability. The model used in NS contains domain-specific knowledge that can be represented in an OWL ontology. This work clarifies the potential advantages of having OWL representation of the NS model, discusses the design of a bi-directional transformation between NS models and domain ontologies in OWL, and describes its implementation. It shows how the resulting ontology enables further work on the analytical level and leverages the system design. Moreover, due to the fact that NS metamodel is metacircular, the transformation can generate ontology of NS metamodel itself. It is expected that the results of this work will help with the design of larger real-world applications as well as the metamodel and that the transformation tool will be further extended with additional features which we proposed.
    [Show full text]
  • Inventing Computational Rhetoric
    INVENTING COMPUTATIONAL RHETORIC By Michael W. Wojcik A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Digital Rhetoric and Professional Writing — Master of Arts 2013 ABSTRACT INVENTING COMPUTATIONAL RHETORIC by Michael W. Wojcik Many disciplines in the humanities are developing “computational” branches which make use of information technology to process large amounts of data algorithmically. The field of computational rhetoric is in its infancy, but we are already seeing interesting results from applying the ideas and goals of rhetoric to text processing and related areas. After considering what computational rhetoric might be, three approaches to inventing computational rhetorics are presented: a structural schema, a review of extant work, and a theoretical exploration. Copyright by MICHAEL W. WOJCIK 2013 For Malea iv ACKNOWLEDGEMENTS Above all else I must thank my beloved wife, Malea Powell, without whose prompting this thesis would have remained forever incomplete. I am also grateful for the presence in my life of my terrific stepdaughter, Audrey Swartz, and wonderful granddaughter Lucille. My thesis committee, Dean Rehberger, Bill Hart-Davidson, and John Monberg, pro- vided me with generous guidance and inspiration. Other faculty members at Michigan State who helped me explore relevant ideas include Rochelle Harris, Mike McLeod, Joyce Chai, Danielle Devoss, and Bump Halbritter. My previous academic program at Miami University did not result in a degree, but faculty there also contributed greatly to my the- oretical understanding, particularly Susan Morgan, Mary-Jean Corbett, Brit Harwood, J. Edgar Tidwell, Lori Merish, Vicki Smith, Alice Adams, Fran Dolan, and Keith Tuma.
    [Show full text]
  • Semantically Enhanced and Minimally Supervised Models for Ontology Construction, Text Classification, and Document Recommendation Alkhatib, Wael (2020)
    Semantically Enhanced and Minimally Supervised Models for Ontology Construction, Text Classification, and Document Recommendation Alkhatib, Wael (2020) DOI (TUprints): https://doi.org/10.25534/tuprints-00011890 Lizenz: CC-BY-ND 4.0 International - Creative Commons, Namensnennung, keine Bearbei- tung Publikationstyp: Dissertation Fachbereich: 18 Fachbereich Elektrotechnik und Informationstechnik Quelle des Originals: https://tuprints.ulb.tu-darmstadt.de/11890 SEMANTICALLY ENHANCED AND MINIMALLY SUPERVISED MODELS for Ontology Construction, Text Classification, and Document Recommendation Dem Fachbereich Elektrotechnik und Informationstechnik der Technischen Universität Darmstadt zur Erlangung des akademischen Grades eines Doktor-Ingenieurs (Dr.-Ing.) genehmigte Dissertation von wael alkhatib, m.sc. Geboren am 15. October 1988 in Hama, Syrien Vorsitz: Prof. Dr.-Ing. Jutta Hanson Referent: Prof. Dr.-Ing. Ralf Steinmetz Korreferent: Prof. Dr.-Ing. Steffen Staab Tag der Einreichung: 28. January 2020 Tag der Disputation: 10. June 2020 Hochschulkennziffer D17 Darmstadt 2020 This document is provided by tuprints, e-publishing service of the Technical Univer- sity Darmstadt. http://tuprints.ulb.tu-darmstadt.de [email protected] Please cite this document as: URN:nbn:de:tuda-tuprints-118909 URL:https://tuprints.ulb.tu-darmstadt.de/id/eprint/11890 This publication is licensed under the following Creative Commons License: Attribution-No Derivatives 4.0 International https://creativecommons.org/licenses/by-nd/4.0/deed.en Wael Alkhatib, M.Sc.: Semantically Enhanced and Minimally Supervised Models, for Ontology Construction, Text Classification, and Document Recommendation © 28. January 2020 supervisors: Prof. Dr.-Ing. Ralf Steinmetz Prof. Dr.-Ing. Steffen Staab location: Darmstadt time frame: 28. January 2020 ABSTRACT The proliferation of deliverable knowledge on the web, along with the rapidly in- creasing number of accessible research publications, make researchers, students, and educators overwhelmed.
    [Show full text]
  • Download Special Issue
    Scientific Programming Scientific Programming Techniques and Algorithms for Data-Intensive Engineering Environments Lead Guest Editor: Giner Alor-Hernandez Guest Editors: Jezreel Mejia-Miranda and José María Álvarez-Rodríguez Scientific Programming Techniques and Algorithms for Data-Intensive Engineering Environments Scientific Programming Scientific Programming Techniques and Algorithms for Data-Intensive Engineering Environments Lead Guest Editor: Giner Alor-Hernández Guest Editors: Jezreel Mejia-Miranda and José María Álvarez-Rodríguez Copyright © 2018 Hindawi. All rights reserved. This is a special issue published in “Scientific Programming.” All articles are open access articles distributed under the Creative Com- mons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Editorial Board M. E. Acacio Sanchez, Spain Christoph Kessler, Sweden Danilo Pianini, Italy Marco Aldinucci, Italy Harald Köstler, Germany Fabrizio Riguzzi, Italy Davide Ancona, Italy José E. Labra, Spain Michele Risi, Italy Ferruccio Damiani, Italy Thomas Leich, Germany Damian Rouson, USA Sergio Di Martino, Italy Piotr Luszczek, USA Giuseppe Scanniello, Italy Basilio B. Fraguela, Spain Tomàs Margalef, Spain Ahmet Soylu, Norway Carmine Gravino, Italy Cristian Mateos, Argentina Emiliano Tramontana, Italy Gianluigi Greco, Italy Roberto Natella, Italy Autilia Vitiello, Italy Bormin Huang, USA Francisco Ortin, Spain Jan Weglarz, Poland Chin-Yu Huang, Taiwan Can Özturan, Turkey
    [Show full text]
  • Ontology Learning and Its Application to Automated Terminology Translation
    Natural Language Processing Ontology Learning and Its Application to Automated Terminology Translation Roberto Navigli and Paola Velardi, Università di Roma La Sapienza Aldo Gangemi, Institute of Cognitive Sciences and Technology lthough the IT community widely acknowledges the usefulness of domain ontolo- A gies, especially in relation to the Semantic Web,1,2 we must overcome several bar- The OntoLearn system riers before they become practical and useful tools. Thus far, only a few specific research for automated ontology environments have ontologies. (The “What Is an Ontology?” sidebar on page 24 provides learning extracts a definition and some background.) Many in the and is part of a more general ontology engineering computational-linguistics research community use architecture.4,5 Here, we describe the system and an relevant domain terms WordNet,3 but large-scale IT applications based on experiment in which we used a machine-learned it require heavy customization. tourism ontology to automatically translate multi- from a corpus of text, Thus, a critical issue is ontology construction— word terms from English to Italian. The method can identifying, defining, and entering concept defini- apply to other domains without manual adaptation. relates them to tions. In large, complex application domains, this task can be lengthy, costly, and controversial, because peo- OntoLearn architecture appropriate concepts in ple can have different points of view about the same Figure 1 shows the elements of the architecture. concept. Two main approaches aid large-scale ontol- Using the Ariosto language processor,6 OntoLearn a general-purpose ogy construction. The first one facilitates manual extracts terminology from a corpus of domain text, ontology engineering by providing natural language such as specialized Web sites and warehouses or doc- ontology, and detects processing tools, including editors, consistency uments exchanged among members of a virtual com- checkers, mediators to support shared decisions, and munity.
    [Show full text]
  • Computability Theory
    CSC 438F/2404F Notes (S. Cook and T. Pitassi) Fall, 2019 Computability Theory This section is partly inspired by the material in \A Course in Mathematical Logic" by Bell and Machover, Chap 6, sections 1-10. Other references: \Introduction to the theory of computation" by Michael Sipser, and \Com- putability, Complexity, and Languages" by M. Davis and E. Weyuker. Our first goal is to give a formal definition for what it means for a function on N to be com- putable by an algorithm. Historically the first convincing such definition was given by Alan Turing in 1936, in his paper which introduced what we now call Turing machines. Slightly before Turing, Alonzo Church gave a definition based on his lambda calculus. About the same time G¨odel,Herbrand, and Kleene developed definitions based on recursion schemes. Fortunately all of these definitions are equivalent, and each of many other definitions pro- posed later are also equivalent to Turing's definition. This has lead to the general belief that these definitions have got it right, and this assertion is roughly what we now call \Church's Thesis". A natural definition of computable function f on N allows for the possibility that f(x) may not be defined for all x 2 N, because algorithms do not always halt. Thus we will use the symbol 1 to mean “undefined". Definition: A partial function is a function n f :(N [ f1g) ! N [ f1g; n ≥ 0 such that f(c1; :::; cn) = 1 if some ci = 1. In the context of computability theory, whenever we refer to a function on N, we mean a partial function in the above sense.
    [Show full text]
  • Semi-Automatic Terminology Ontology Learning Based on Topic Modeling
    The paper has been accepted at Engineering Applications of Artificial Intelligence on 9 May 2017. This paper can cite as: Monika Rani, Amit Kumar Dhar, O.P. Vyas, Semi-automatic terminology ontology learning based on topic modeling, Engineering Applications of Artificial Intelligence, Volume 63, August 2017, Pages 108-125,ISSN 0952- 1976,https://doi.org/10.1016/j.engappai.2017.05.006. (http://www.sciencedirect.com/science/article/pii/S0952197617300891. Semi-Automatic Terminology Ontology Learning Based on Topic Modeling Monika Rani*, Amit Kumar Dhar and O. P. Vyas Department of Information Technology, Indian Institute of Information Technology, Allahabad, India [email protected] Abstract- Ontologies provide features like a common vocabulary, reusability, machine-readable content, and also allows for semantic search, facilitate agent interaction and ordering & structuring of knowledge for the Semantic Web (Web 3.0) application. However, the challenge in ontology engineering is automatic learning, i.e., the there is still a lack of fully automatic approach from a text corpus or dataset of various topics to form ontology using machine learning techniques. In this paper, two topic modeling algorithms are explored, namely LSI & SVD and Mr.LDA for learning topic ontology. The objective is to determine the statistical relationship between document and terms to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and semantic retrieving corresponding topic ontology for the user‟s query demonstrating the effectiveness of the proposed approach. Keywords: Ontology Learning (OL), Latent Semantic Indexing (LSI), Singular Value Decomposition (SVD), Probabilistic Latent Semantic Indexing (pLSI), MapReduce Latent Dirichlet Allocation (Mr.LDA), Correlation Topic Modeling (CTM).
    [Show full text]
  • Data Models for Home Services
    __________________________________________PROCEEDING OF THE 13TH CONFERENCE OF FRUCT ASSOCIATION Data Models for Home Services Vadym Kramar, Markku Korhonen, Yury Sergeev Oulu University of Applied Sciences, School of Engineering Raahe, Finland {vadym.kramar, markku.korhonen, yury.sergeev}@oamk.fi Abstract An ultimate penetration of communication technologies allowing web access has enriched a conception of smart homes with new paradigms of home services. Modern home services range far beyond such notions as Home Automation or Use of Internet. The services expose their ubiquitous nature by being integrated into smart environments, and provisioned through a variety of end-user devices. Computational intelligence require a use of knowledge technologies, and within a given domain, such requirement as a compliance with modern web architecture is essential. This is where Semantic Web technologies excel. A given work presents an overview of important terms, vocabularies, and data models that may be utilised in data and knowledge engineering with respect to home services. Index Terms: Context, Data engineering, Data models, Knowledge engineering, Semantic Web, Smart homes, Ubiquitous computing. I. INTRODUCTION In recent years, a use of Semantic Web technologies to build a giant information space has shown certain benefits. Rapid development of Web 3.0 and a use of its principle in web applications is the best evidence of such benefits. A traditional database design in still and will be widely used in web applications. One of the most important reason for that is a vast number of databases developed over years and used in a variety of applications varying from simple web services to enterprise portals. In accordance to Forrester Research though a growing number of document, or knowledge bases, such as NoSQL is not a hype anymore [1].
    [Show full text]