Arguments About the Nature of Concepts: Symbols, Embodiment, and Beyond
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Ontology and Information Systems
Ontology and Information Systems 1 Barry Smith Philosophical Ontology Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. ‘Ontology’ is often used by philosophers as a synonym for ‘metaphysics’ (literally: ‘what comes after the Physics’), a term which was used by early students of Aristotle to refer to what Aristotle himself called ‘first philosophy’.2 The term ‘ontology’ (or ontologia) was itself coined in 1613, independently, by two philosophers, Rudolf Göckel (Goclenius), in his Lexicon philosophicum and Jacob Lorhard (Lorhardus), in his Theatrum philosophicum. The first occurrence in English recorded by the OED appears in Bailey’s dictionary of 1721, which defines ontology as ‘an Account of being in the Abstract’. Methods and Goals of Philosophical Ontology The methods of philosophical ontology are the methods of philosophy in general. They include the development of theories of wider or narrower scope and the testing and refinement of such theories by measuring them up, either against difficult 1 This paper is based upon work supported by the National Science Foundation under Grant No. BCS-9975557 (“Ontology and Geographic Categories”) and by the Alexander von Humboldt Foundation under the auspices of its Wolfgang Paul Program. Thanks go to Thomas Bittner, Olivier Bodenreider, Anita Burgun, Charles Dement, Andrew Frank, Angelika Franzke, Wolfgang Grassl, Pierre Grenon, Nicola Guarino, Patrick Hayes, Kathleen Hornsby, Ingvar Johansson, Fritz Lehmann, Chris Menzel, Kevin Mulligan, Chris Partridge, David W. Smith, William Rapaport, Daniel von Wachter, Chris Welty and Graham White for helpful comments. -
Word Senses and Wordnet Lady Bracknell
Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright © 2021. All rights reserved. Draft of September 21, 2021. CHAPTER 18 Word Senses and WordNet Lady Bracknell. Are your parents living? Jack. I have lost both my parents. Lady Bracknell. To lose one parent, Mr. Worthing, may be regarded as a misfortune; to lose both looks like carelessness. Oscar Wilde, The Importance of Being Earnest ambiguous Words are ambiguous: the same word can be used to mean different things. In Chapter 6 we saw that the word “mouse” has (at least) two meanings: (1) a small rodent, or (2) a hand-operated device to control a cursor. The word “bank” can mean: (1) a financial institution or (2) a sloping mound. In the quote above from his play The Importance of Being Earnest, Oscar Wilde plays with two meanings of “lose” (to misplace an object, and to suffer the death of a close person). We say that the words ‘mouse’ or ‘bank’ are polysemous (from Greek ‘having word sense many senses’, poly- ‘many’ + sema, ‘sign, mark’).1 A sense (or word sense) is a discrete representation of one aspect of the meaning of a word. In this chapter WordNet we discuss word senses in more detail and introduce WordNet, a large online the- saurus —a database that represents word senses—with versions in many languages. WordNet also represents relations between senses. For example, there is an IS-A relation between dog and mammal (a dog is a kind of mammal) and a part-whole relation between engine and car (an engine is a part of a car). -
Using Latent Semantic Analysis to Identify Research Trends in Openstreetmap
Article Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap Sukhjit Singh Sehra 1,2,*, Jaiteg Singh 3 and Hardeep Singh Rai 4 1 Department of Research, Innovation & Consultancy, I.K. Gujral Punjab Technical University, Kapurthala, Punjab 144603, India 2 Department of Computer Science & Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab 141006, India 3 School of Computer Sciences, Chitkara University, Patiala, Punjab 140401, India; [email protected] 4 Department of Civil Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab 141006, India; [email protected] * Correspondence: [email protected]; Tel.: +91-985-595-9200 Academic Editors: Bernd Resch, Linda See and Wolfgang Kainz Received: 13 May 2017; Accepted: 28 June 2017; Published: 1 July 2017 Abstract: OpenStreetMap (OSM), based on collaborative mapping, has become a subject of great interest to the academic community, resulting in a considerable body of literature produced by many researchers. In this paper, we use Latent Semantic Analysis (LSA) to help identify the emerging research trends in OSM. An extensive corpus of 485 academic abstracts of papers published during the period 2007–2016 was used. Five core research areas and fifty research trends were identified in this study. In addition, potential future research directions have been provided to aid geospatial information scientists, technologists and researchers in undertaking future OSM research. Keywords: OpenStreetMap; research trends; latent semantic analysis; volunteered geographic information 1. Introduction OpenStreetMap (OSM), founded in 2004, provides a free editable guide to the world, available under an Open Database License (ODbL). The project is supported by Web 2.0 technologies, which enable more coordinated efforts among web clients, different users and content suppliers [1]. -
SEMANTIC ANALYSIS of NATURAL LANGUAGE and DEFINITE CLAUSE GRAMMAR USING STATISTICAL PARSING and THESAURI By
SEMANTIC ANALYSIS OF NATURAL LANGUAGE AND DEFINITE CLAUSE GRAMMAR USING STATISTICAL PARSING AND THESAURI by Björn Dagerman A thesis submitted in partial fulfillment of the requirements for the degree of BACHELOR OF SCIENCE in Computer Science Examiner: Baran Çürüklü Supervisor: Batu Akan MÄLARDALEN UNIVERSITY School of Innovation, Design and Engineering 2013 i ABSTRACT Services that rely on the semantic computations of users’ natural linguistic inputs are becoming more frequent. Computing semantic relatedness between texts is problematic due to the inherit ambiguity of natural language. The purpose of this thesis was to show how a sentence could be compared to a predefined semantic Definite Clause Grammar (DCG). Furthermore, it should show how a DCG-based system could benefit from such capabilities. Our approach combines openly available specialized NLP frameworks for statistical parsing, part-of-speech tagging and word-sense disambiguation. We compute the seman- tic relatedness using a large lexical and conceptual-semantic thesaurus. Also, we extend an existing programming language for multimodal interfaces, which uses static predefined DCGs: COactive Language Definition (COLD). That is, every word that should be accept- able by COLD needs to be explicitly defined. By applying our solution, we show how our approach can remove dependencies on word definitions and improve grammar definitions in DCG-based systems. (27 pages) ii SAMMANFATTNING Tjänster som beror på semantiska beräkningar av användares naturliga tal blir allt van- ligare. Beräkning av semantisk likhet mellan texter är problematiskt på grund av naturligt tals medförda tvetydighet. Syftet med det här examensarbetet är att visa hur en mening can jämföras med en fördefinierad semantisk Definite Clause Grammar (DCG). -
Ontology for Information Systems (O4IS) Design Methodology Conceptualizing, Designing and Representing Domain Ontologies
Ontology for Information Systems (O4IS) Design Methodology Conceptualizing, Designing and Representing Domain Ontologies Vandana Kabilan October 2007. A Dissertation submitted to The Royal Institute of Technology in partial fullfillment of the requirements for the degree of Doctor of Technology . The Royal Institute of Technology School of Information and Communication Technology Department of Computer and Systems Sciences IV DSV Report Series No. 07–013 ISBN 978–91–7178–752–1 ISSN 1101–8526 ISRN SU–KTH/DSV/R– –07/13– –SE V All knowledge that the world has ever received comes from the mind; the infinite library of the universe is in our own mind. – Swami Vivekananda. (1863 – 1902) Indian spiritual philosopher. The whole of science is nothing more than a refinement of everyday thinking. – Albert Einstein (1879 – 1955) German-Swiss-U.S. scientist. Science is a mechanism, a way of trying to improve your knowledge of na- ture. It’s a system for testing your thoughts against the universe, and seeing whether they match. – Isaac Asimov. (1920 – 1992) Russian-U.S. science-fiction author. VII Dedicated to the three KAs of my life: Kabilan, Rithika and Kavin. IX Abstract. Globalization has opened new frontiers for business enterprises and human com- munication. There is an information explosion that necessitates huge amounts of informa- tion to be speedily processed and acted upon. Information Systems aim to facilitate human decision-making by retrieving context-sensitive information, making implicit knowledge ex- plicit and to reuse the knowledge that has already been discovered. A possible answer to meet these goals is the use of Ontology. -
Semantics and Computational Semantics∗
Semantics and Computational Semantics∗ Matthew Stone Rutgers University Abstract Computational semanticists typically devise applied computer systems simply to work as well as possible, but their work enriches the study of linguistic meaning with important tools, resources and methods which I survey here. To get useful results requires systematizing models from semantics in computational terms. Key computational challenges involve the compositional analysis of sentence meaning; the formalization of lexical meaning and related world knowledge; and modeling context and conver- sation to connect semantics and pragmatics. Underpinning computational semanticists’ successes in all these areas, we find the distinctive roles of representation, problem solving and learning in computation. 1 Introduction Interdisciplinary investigations marry the methods and concerns of differ- ent fields. Computer science is the study of precise descriptions of finite processes; semantics is the study of meaning in language. Thus, computa- tional semantics embraces any project that approaches the phenomenon of meaning by way of tasks that can be performed by following definite sets of mechanical instructions. So understood, computational semantics revels in applying semantics, by creating intelligent devices whose broader behavior fits the meanings of utterances, and not just their form. IBM’s Watson (Fer- rucci, Brown, Chu-Carroll, Fan, Gondek, Kalyanpur, Lally, Murdock, Nyberg, Prager, Schlaefer & Welty 2010) is a harbinger of the excitement and potential of this technology. In applications, the key questions of meaning are the questions engineers must answer to make things work. How can we build a system that copes robustly with the richness and variety of meaning in language, and with its ambiguities and underspecification? The focus is on methods that give us clear problems we can state and solve. -
Low-Dimensional Embodied Semantics for Music and Language
Low-dimensional Embodied Semantics for Music and Language Francisco Afonso Raposoa,c, David Martins de Matosa,c, Ricardo Ribeirob,c aInstituto Superior T´ecnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal bInstituto Universit´ariode Lisboa (ISCTE-IUL), Av. das For¸casArmadas, 1649-026 Lisboa, Portugal cINESC-ID Lisboa, R. Alves Redol 9, 1000-029 Lisboa, Portugal Abstract Embodied cognition states that semantics is encoded in the brain as firing patterns of neural circuits, which are learned according to the statistical structure of human multimodal experience. However, each human brain is idiosyncratically biased, according to its subjective experience history, making this biological semantic machinery noisy with respect to the overall semantics inherent to media artifacts, such as music and language excerpts. We propose to represent shared semantics using low-dimensional vector embeddings by jointly modeling several brains from human subjects. We show these unsupervised efficient representations outperform the original high-dimensional fMRI voxel spaces in proxy music genre and language topic classification tasks. We further show that joint modeling of several subjects increases the semantic richness of the learned latent vector spaces. Keywords: Semantics, Embodied Cognition, fMRI, Music, Natural Language, Machine Learning, Cross-modal Retrieval, Classification 1. Introduction scriptions can be captured by jointly modeling the statis- tics of neural firing of several similarly stimulated brains. The current consensus in cognitive science defends that In this work, we propose modeling shared semantics conceptual knowledge is encoded in the brain as firing pat- as low-dimensional encodings via statistical inter-subject terns of neural populations [1]. Correlated patterns of ex- brain agreement using Generalized Canonical Correlation perience trigger the firing of neural populations, creating Analysis (GCCA). -
6.863J Natural Language Processing Lecture 15: Word Semantics – Working with Wordnet
6.863J Natural Language Processing Lecture 15: Word semantics – Working with Wordnet Robert C. Berwick [email protected] The Menu Bar • Administrivia: • Lab 4 due April 9 (Weds.); • Start w/ final projects, unless there are objections • Agenda: • Working with Wordnet • What’s Wordnet • What can we do with it? • Solving some reasoning problems: • Mending a torn dress • Enjoying a movie; What’s a shelf? • Implementing EVCA and Wordnet together 6.863J/9.611J Lecture 15 Sp03 Wordnet motivation But people have persistent problem. When they look up a word, especially a commonly used word, they often find a dozen or more different meanings. What the dictionary does not make clear are the contexts in which each of these different meanings would be understood. So we know what kind of information is required, but we have not yet learned how to provide it to a computer. (G. Miller, U.S./Japan Joint Workshop on Electronic Dictionaries and Language Technologies January 23--25, 1993.) 6.863J/9.611J Lecture 15 Sp03 What’s Wordnet? • Psychological motivation • Nouns, verbs, adjectives organized into (fairly) distinct networks of • Synonym Sets (synsets) • Each synset = 1 concept • Supposedly intersubstitutable within synset (“synonomy”) 6.863J/9.611J Lecture 15 Sp03 Practical motivation • What’s not in a dictionary? • Take example, like tree – “large, woody perennial plant with a distinct trunk” • What info is missing? 6.863J/9.611J Lecture 15 Sp03 Psychological motivation • Why these categories? • Words association: first word thought of drawn from -
Semantic Issues in Integrating Data from Different Models to Achieve Data Interoperability
MEDINFO 2007 K. Kuhn et al. (Eds) IOS Press, 2007 © 2007 The authors. All rights reserved. Semantic Issues in Integrating Data from Different Models to Achieve Data Interoperability Rahil Qamara, Alan Rectora a Medical Informatics Group, University of Manchester, Manchester, U.K. Abstract Background Matching clinical data to codes in controlled terminolo- Archetype models gies is the first step towards achieving standardisation of In this paper, archetypes refer to the openEHR archetypes. data for safe and accurate data interoperability. The MoST Archetypes are being put forward by the openEHR organi- automated system was used to generate a list of candidate sation as a method for modeling clinical concepts. The SNOMED CT code mappings. The paper discusses the models conform to the openEHR Reference Model (RM). semantic issues which arose when generating lexical and Archetypes are computable expressions of a domain content semantic matches of terms from the archetype model to rel- model of medical records. The expression is in the form of evant SNOMED codes. It also discusses some of the structured constraint statements, inherited from the RM [1]. solutions that were developed to address the issues. The The intended purpose of archetypes is to empower clinicians aim of the paper is to highlight the need to be flexible when to define the content, semantics and data-entry interfaces of integrating data from two separate models. However, the systems independently from the information systems [1]. paper also stresses that the context and semantics of the Archetypes were selected because of their feature to sepa- data in either model should be taken into consideration at rate the internal model data from formal terminologies. -
Semantic Networks John F
Semantic Networks John F. Sowa This is an updated version of an article in the Encyclopedia of Artificial Intelligence, Wiley, 1987; second edition, 1992. Most of the text from 1992 is unchanged, but more recent material and references have been added. A semantic network or net is a graph structure for representing knowledge in patterns of interconnected nodes and arcs. Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics. The Giant Global Graph of the Semantic Web is a large semantic network (Berners-Lee et al. 2001; Hendler & van Harmelen 2008). What is common to all semantic networks is a declarative graphic representation that can be used to represent knowledge and support automated systems for reasoning about the knowledge. Some versions are highly informal, but others are formally defined systems of logic. Following are six of the most common kinds of semantic networks: 1. Definitional networks emphasize the subtype or is-a relation between a concept type and a newly defined subtype. The resulting network, also called a generalization or subsumption hierarchy, supports the rule of inheritance for copying properties defined for a supertype to all of its subtypes. Since definitions are true by definition, the information in these networks is often assumed to be necessarily true. 2. Assertional networks are designed to assert propositions. Unlike definitional networks, the information in an assertional network is assumed to be contingently true, unless it is explicitly marked with a modal operator. Some assertional networks have been proposed as models of the conceptual structures underlying natural language semantics.