Using Ontologies for Knowledge Management: an Information Systems Perspective
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A Framework for Comparing the Use of a Linguistic Ontology in an Application
A Framework for Comparing the use of a Linguistic Ontology in an Application Julieanne van Zyl1 and Dan Corbett2 Abstract. A framework recently developed for understanding and “a body of knowledge about the world (or a domain) that a) is classifying ontology applications provides opportunities to review a repository of primitive symbols used in meaning the state of the art, and to provide guidelines for application b) organises these symbols in a tangled subsumption developers from different communities [1]. The framework hierarchy; and identifies four main categories of ontology applications: neutral c) further interconnects these symbols using a rich system of authoring, ontology as specification, common access to semantic and discourse-pragmatic relations defined among information, and ontology-based search. Specific scenarios are the concepts” [2]. described for each category and a number of features have been identified to highlight the similarities and differences between There has been some work on comparing ontology applications, them. In this paper we identify additional scenarios, describing the which refer to “the application that makes use of or benefits from use of a linguistic ontology in an application. We populate the the ontology (possibly directly)” [1]. A framework [3] was scenarios with a number of prominent research and industrial developed for comparing various projects in ontology design, applications from diverse communities such as knowledge-based which considered the differences and similarities in the way the machine translation, medical databases, heterogenous information projects treat some basic knowledge representation aspects. The systems integration, multilingual text generation, Web-based projects compared include three natural language ontologies, two applications, and information retrieval. -
How to Keep a Knowledge Base Synchronized with Its Encyclopedia Source
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) How to Keep a Knowledge Base Synchronized with Its Encyclopedia Source Jiaqing Liang12, Sheng Zhang1, Yanghua Xiao134∗ 1School of Computer Science, Shanghai Key Laboratory of Data Science Fudan University, Shanghai, China 2Shuyan Technology, Shanghai, China 3Shanghai Internet Big Data Engineering Technology Research Center, China 4Xiaoi Research, Shanghai, China [email protected], fshengzhang16,[email protected] Abstract However, most of these knowledge bases tend to be out- dated, which limits their utility. For example, in many knowl- Knowledge bases are playing an increasingly im- edge bases, Donald Trump is only a business man even af- portant role in many real-world applications. How- ter the inauguration. Obviously, it is important to let ma- ever, most of these knowledge bases tend to be chines know that Donald Trump is the president of the United outdated, which limits the utility of these knowl- States so that they can understand that the topic of an arti- edge bases. In this paper, we investigate how to cle mentioning Donald Trump is probably related to politics. keep the freshness of the knowledge base by syn- Moreover, new entities are continuously emerging and most chronizing it with its data source (usually ency- of them are popular, such as iphone 8. However, it is hard for clopedia websites). A direct solution is revisiting a knowledge base to cover these entities in time even if the the whole encyclopedia periodically and rerun the encyclopedia websites have already covered them. entire pipeline of the construction of knowledge freshness base like most existing methods. -
Learning Ontologies from RDF Annotations
/HDUQLQJÃRQWRORJLHVÃIURPÃ5')ÃDQQRWDWLRQV $OH[DQGUHÃ'HOWHLOÃ&DWKHULQHÃ)DURQ=XFNHUÃ5RVHÃ'LHQJ ACACIA project, INRIA, 2004, route des Lucioles, B.P. 93, 06902 Sophia Antipolis, France {Alexandre.Delteil, Catherine.Faron, Rose.Dieng}@sophia.inria.fr $EVWUDFW objects, as in [Mineau, 1990; Carpineto and Romano, 1993; Bournaud HWÃDO., 2000]. In this paper, we present a method for learning Since all RDF annotations are gathered inside a common ontologies from RDF annotations of Web RDF graph, the problem which arises is the extraction of a resources by systematically generating the most description for a given resource from the whole RDF graph. specific generalization of all the possible sets of After a brief description of the RDF data model (Section 2) resources. The preliminary step of our method and of RDF Schema (Section 3), Section 4 presents several consists in extracting (partial) resource criteria for extracting partial resource descriptions. In order descriptions from the whole RDF graph gathering to deal with the intrinsic complexity of the building of a all the annotations. In order to deal with generalization hierarchy, we propose an incremental algorithmic complexity, we incrementally build approach by gradually increasing the size of the descriptions the ontology by gradually increasing the size of the resource descriptions we consider. we consider. The principle of the approach is explained in Section 5 and more deeply detailed in Section 6. Ã ,QWURGXFWLRQ Ã 7KHÃ5')ÃGDWDÃPRGHO The Semantic Web, expected to be the next step that will RDF is the emerging Web standard for annotating resources lead the Web to its full potential, will be based on semantic HWÃDO metadata describing all kinds of Web resources. -
A Translation Approach to Portable Ontology Specifications
Knowledge Systems Laboratory September 1992 Technical Report KSL 92-71 Revised April 1993 A Translation Approach to Portable Ontology Specifications by Thomas R. Gruber Appeared in Knowledge Acquisition, 5(2):199-220, 1993. KNOWLEDGE SYSTEMS LABORATORY Computer Science Department Stanford University Stanford, California 94305 A Translation Approach to Portable Ontology Specifications Thomas R. Gruber Knowledge System Laboratory Stanford University 701 Welch Road, Building C Palo Alto, CA 94304 [email protected] Abstract To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse — definitions of classes, relations, functions, and other objects — is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how -
Theory-Based Knowledge Acquisition for Ontology Development
UNIVERSITÄT HOHENHEIM Theory-based Knowledge Acquisition for Ontology Development A dissertation submitted to the Faculty of Business, Economics and Social Sciences of the University of Hohenheim in partial fulfillment of the requirements for the degree of Doctor of Economics (Dr. oec.) by Andreas Scheuermann July 2016 Principal advisor: Prof. Dr. Stefan Kirn Co-advisor: Prof. Dr. Ralph Bergmann Chair of the examination committee: Prof. Dr. Christian Ernst Dean: Prof. Dr. Dirk Hachmeister Date of oral examination: 7. July 2016 Abstract iii Abstract This thesis concerns the problem of knowledge acquisition in ontology development. Knowledge acquisition is essential for developing useful ontologies but it is a complex and error-prone task. When capturing specific knowledge about a particular domain of interest, the problem of knowledge acquisition occurs due to linguistic, cognitive, modelling, and methodical difficulties. For overcoming these four difficulties, this research proposes a theory-based knowledge acquisition method. By studying the knowledge base, basic terms and concepts in the areas of ontology, ontology development, and knowledge acquisition are defined. A theoretical analysis of knowledge acquisition identifies linguistic, cognitive, modelling, and methodical difficulties, for which a survey of 15 domain ontologies provides further empirical evidence. A review of existing knowledge acquisition approaches shows their insufficiencies for reducing the problem of knowledge acquisition. As the underpinning example, a description of the domain of transport chains is provided. Correspondingly, a theory in business economics, i.e. the Contingency Approach, is selected. This theory provides the key constructs, relationships, and dependencies that can guide knowledge acquisition in the business domain and, thus, theoretically substantiate knowledge acquisition. -
Implications of Big Data for Knowledge Organization. Fidelia Ibekwe-Sanjuan, Bowker Geoffrey
Implications of big data for knowledge organization. Fidelia Ibekwe-Sanjuan, Bowker Geoffrey To cite this version: Fidelia Ibekwe-Sanjuan, Bowker Geoffrey. Implications of big data for knowledge organization.. Knowledge Organization, Ergon Verlag, 2017, Special issue on New trends for Knowledge Organi- zation, Renato Rocha Souza (guest editor), 44 (3), pp.187-198. hal-01489030 HAL Id: hal-01489030 https://hal.archives-ouvertes.fr/hal-01489030 Submitted on 23 Apr 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives| 4.0 International License F. Ibekwe-SanJuan and G. C. Bowker (2017). Implications of Big Data for Knowledge Organization, Knowledge Organization 44(3), pp. 187-198 Implications of big data for knowledge organization Fidelia Ibekwe-SanJuan Geoffrey C. Bowker Aix Marseille Univ, IRSIC, Marseille, France University of California, Irvine, USA. [email protected] [email protected] Fidelia Ibekwe-SanJuan is Professor at the School of Journalism and Communication, University of Aix-Marseille in France. Her research interests span both empirical and theoretical issues. She has developed methodologies and tools for text mining and information retrieval. She is interested in the epistemology of science, in inter-disciplinarity issues and in the history of information and library science. -
Principles of Knowledge Organization: Analysis and Structures in the Networked Environment
Principles of knowledge organization: analysis and structures in the networked environment 0. Abstract 1. Context of discussion 2. Definition of principles Knowledge organization in the networked environment is guided by standards. Standards Principles are laws, assumptions standards, rules, in knowledge organization are built on principles. For example, NISO Z39.19-1993 Guide judgments, policy, modes of action, as essential or • Knowledge Organization in the networked environment is guided by standards to the Construction of Monolingual Thesauri (now undergoing revision) and NISO Z39.85- basic qualities. They can also be considered goals • Standards in knowledge organization are built on principles 2001 Dublin Core Metadata Element Set are tw o standards used in ma ny implementations. or values in some knowledge organization theories. Both of these standards were crafted with knowledge organization principles in mind. (Adapting the definition from the American Heritage Therefore it is standards work guided by knowledge organization principles which can Existing standards built on principles: Dictionary). affect design of information services and technologies. This poster outlines five threads of • NISO Z39.19-1993 Guide to the Construction of Monolingual Thesauri thought that inform knowledge organization principles in the networked environment. An • NISO Z39.85-2001 Dublin Core Metadata Element Set understanding of each of these five threads informs system evaluation. The evaluation of knowledge organization systems should be tightly -
Biases in Knowledge Representation: an Analysis of the Feminine Domain in Brazilian Indexing Languages
Suellen Oliveira Milani and José Augusto Chaves Guimarães. 2011. Biases in knowledge representation: an analysis of the feminine domain in Brazilian indexing languages. In Smiraglia, Richard P., ed. Proceedings from North American Symposium on Knowledge Organization, Vol. 3. Toronto, Canada, pp. 94-104. Suellen Oliveira Milani ([email protected]) and José Augusto Chaves Guimarães ([email protected]) Sao Paulo State University, Marília, SP, Brazil Biases in Knowledge Representation: an Analysis of the Feminine Domain in Brazilian Indexing Languages† Abstract: The process of knowledge representation, as well as its tools and resulting products are not neutral but permeated by moral values. This scenario gives rise to problems of biases in representation, such as gender issues, dichotomy categorizations and lack of cultural warrant and hospitality. References on women’s issues are still scarce in the literature, which makes it necessary to analyze to what extent the terms related to these particular issues are inserted in the tools in a biased way. This study aimed to verify the presence of the terms female, femininity, feminism, feminist, maternal, motherly, woman/women within the following Brazilian indexing languages: Subject Terminology of the National Library (STNL), University of Sao Paulo Subject Headings (USPSH), Brazilian Senate Subject Headings (BSSH) and Law Decimal Classification (LDC). Each term identified in the first three alphabetical languages generated a registration card containing both its descriptors and non-descriptors, as well as scope notes, USE/UF, RT, and BT/NT relationships. As for the analysis of LDC, the registration card was filled out by following the categories proposed by Olson (1998). -
The Linguistic Dimension of Terminology
1st Athens International Conference on Translation and Interpretation Translation: Between Art and Social Science, 13 -14 October 2006 THE LINGUISTIC DIMENSION OF TERMINOLOGY: PRINCIPLES AND METHODS OF TERM FORMATION Kostas Valeontis Elena Mantzari Physicist-Electronic Engineer, President of ΕLΕΤΟ1, Linguist-Researcher, Deputy Secretary General of ΕLΕΤΟ Abstract Terminology has a twofold meaning: 1. it is the discipline concerned with the principles and methods governing the study of concepts and their designations (terms, names, symbols) in any subject field, and the job of collecting, processing, and managing relevant data, and 2. the set of terms belonging to the special language of an individual subject field. In its study of concepts and their representations in special languages, terminology is multidisciplinary, since it borrows its fundamental tools and concepts from a number of disciplines (e.g. logic, ontology, linguistics, information science and other specific fields) and adapts them appropriately in order to cover particularities in its own area. The interdisciplinarity of terminology results from the multifaceted character of terminological units, as linguistic items (linguistics), as conceptual elements (logic, ontology, cognitive sciences) and as vehicles of communication in both scientific and generic language contexts. Accordingly, the theory of terminology can be identified as having three different dimensions: the cognitive, the linguistic, and the communicative dimension (Sager: 1990). The linguistic dimension of the theory of terminology can be detected mainly in the linguistic mechanisms that set the patterns for term formation and term forms. In this paper, we will focus on the presentation of general linguistic principles concerning term formation, during primary naming of an original concept in a source language and secondary term formation in a target language. -
Propositional Knowledge: Acquisition and Application to Syntactic and Semantic Parsing
Thesis for the Degree of Doctor of Philosophy 2017 Propositional Knowledge: Acquisition and Application to Syntactic and Semantic Parsing Bernardo Cabaleiro Barciela University Master's Degree in Languages and Computer Systems (National Distance Education University) Doctoral Programme in Intelligent Systems Academic advisor: Anselmo Peñas Padilla Associate Professor in the Languages and Computer Systems Department (National Distance Education University) Thesis for the Degree of Doctor of Philosophy 2017 Propositional Knowledge: Acquisition and Application to Syntactic and Semantic Parsing Bernardo Cabaleiro Barciela University Master's Degree in Languages and Computer Systems (National Distance Education University) Doctoral Programme in Intelligent Systems Academic advisor: Anselmo Peñas Padilla Associate Professor in the Languages and Computer Systems Department (National Distance Education University) A mi familia Acknowledgements Esta tesis pone punto final a mis estudios de doctorado, y, a la vez, supone el fin de una bonita etapa de mi vida. A lo largo de este tiempo me han acompañado muchas personas que han hecho que esta experiencia sea inolvidable. Estas líneas son para intentar agradecerles todo lo que han hecho por mí. En primer lugar, me gustaría mostrar mi agradecimiento a Anselmo Peñas por su dirección tanto del trabajo de fin de máster como de esta tesis doctoral. Durante todo este tiempo Anselmo ha puesto mucho empeño para formarme, no sólo en el campo del PLN sino también como investigador. Quiero agradecerle la libertad que me ha dado para trabajar en lo que me interesaba, y el especial cuidado que ha puesto en corregir y mejorar esos trabajos. Me gustaría dar las gracias también a mis compañeros en el departamento de lenguajes y sistemas, tanto a estuvieron como a los que están. -
File: Terminology.Pdf
Lowe I 2009, www.scientificlanguage.com/esp/terminology.pdf 1 A question of terminology ______________________________________________________________ This essay is copyright under the creative commons Attribution - Noncommercial - Share Alike 3.0 Unported licence. You are encouraged to share and copy this essay free of charge. See for details: http://creativecommons.org/licenses/by-nc-sa/3.0/ A question of terminology Updated 24 November 2009 24 November 2009 A framework for analysing sub/semi-technical words is presented which reconciles the two senses of these terms. Additional practical ideas for teaching them are provided. 0. Introduction The terminology for describing the language of science is in a state of confusion. There are several words, with differing definitions. There is even confusion over exactly what should be defined and labelled. In other words, there is a problem of classification. This paper attempts to sort out some of the confusion. 1. Differing understandings of what is meant by “general” English and “specialised” English (see also www.scientificlanguage.com/esp/audience.pdf ) Table to show the different senses of ‘general’ and ‘specialised’ Popular senses Technical senses 1. a wide range of general 2. Basic English, of all interest topics of general kinds, from pronunciation, “general” English knowledge, such as sport, through vocabulary, to hobbies etc discourse patterns such as Suggested term: those used in a newspaper. general topics English Suggested terms: foundational English basic English 3. a wide range of topics 4. Advanced English, the within the speciality of the fine points, and the “specialised” English student. vocabulary and discourse Suggested term: patterns specific to the specialised topics English discipline. -
Arxiv:1910.13561V1 [Cs.LG] 29 Oct 2019 E-Mail: [email protected] M
Noname manuscript No. (will be inserted by the editor) A Heuristically Modified FP-Tree for Ontology Learning with Applications in Education Safwan Shatnawi · Mohamed Medhat Gaber ∗ · Mihaela Cocea Received: date / Accepted: date Abstract We propose a heuristically modified FP-Tree for ontology learning from text. Unlike previous research, for concept extraction, we use a regular expression parser approach widely adopted in compiler construction, i.e., deterministic finite automata (DFA). Thus, the concepts are extracted from unstructured documents. For ontology learning, we use a frequent pattern mining approach and employ a rule mining heuristic function to enhance its quality. This process does not rely on predefined lexico-syntactic patterns, thus, it is applicable for different subjects. We employ the ontology in a question-answering system for students' content-related questions. For validation, we used textbook questions/answers and questions from online course forums. Subject experts rated the quality of the system's answers on a subset of questions and their ratings were used to identify the most appropriate automatic semantic text similarity metric to use as a validation metric for all answers. The Latent Semantic Analysis was identified as the closest to the experts' ratings. We compared the use of our ontology with the use of Text2Onto for the question-answering system and found that with our ontology 80% of the questions were answered, while with Text2Onto only 28.4% were answered, thanks to the finer grained hierarchy our approach is able to produce. Keywords Ontologies · Frequent pattern mining · Ontology learning · Question answering · MOOCs S. Shatnawi College of Applied Studies, University of Bahrain, Sakhair Campus, Zallaq, Bahrain E-mail: [email protected] M.