Search (63 results, page 1 of 4)

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Abstract

In order to discover design principles for a large memory that can enable it to serve as the base of knowledge underlying human-like language behavior, experiments with a model memory are being performed. This model is built up within a computer by "recoding" a body of information from an ordinary dictionary into a complex network of elements and associations interconnecting them. Then, the ability of a program to use the resulting model memory effectively for simulating human performance provides a test of its design. One simulation program, now running, is given the model memory and is required to compare and contrast the meanings of arbitrary pairs of English words. For each pair, the program locates any relevant semantic information within the model memory, draws inferences on the basis of this, and thereby discovers various relationships between the meanings of the two words. Finally, it creates English text to express its conclusions. The design principles embodied in the memory model, together with some of the methods used by the program, constitute a theory of how human memory for semantic and other conceptual material may be formatted, organized, and used.

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Content

G. Gainotti, M.C. Silveri, A. Daniele, L. Giustolisi, Neuroanatomical Correlates of Category-specific Semantic Disorders: A Critical Survey. J. S. Snowden, H. L. Griffiths, D. Neary, Autobiographical Experience and Word Meaning. L. Cipolotti, E.K. Warrington, Towards a Unitary Account of Access Dysphasia: A Single Case Study. E. Forde, G.W. Humphreys, Refractory Semantics in Global Aphasia: On Semantic Organisation and the Access-Storage Distinction in Neuropsychology. A. E. Hillis, A. Caramazza, The Compositionality of Lexical Semantic Representations: Clues from Semantic Errors in Object Naming. H.E. Moss, L.K. Tyler, Investigating Semantic Memory Impairments: The Contribution of Semantic Priming. K.R. Laws, S.A. Humber, D.J.C. Ramsey, R.A. McCarthy, Probing Sensory and Associative Semantics for Animals and Objects in Normal Subjects. K.R. Laws, J.J. Evans, J. R. Hodges, R.A. McCarthy, Naming without Knowing and Appearance without Associations: Evidence for Constructive Processes in Semantic Memory? J. Powell, J. Davidoff, Selective Impairments of Object-knowledge in a Case of Acquired Cortical Blindness. J.R. Hodges, N. Graham, K. Patterson, Charting the Progression in Semantic Dementia: Implications for the Organisation of Semantic Memory. E. Funnell, Objects and Properties: A Study of the Breakdown of Semantic Memory. L.J. Tippett, S. McAuliffe, M. J. Farrar, Preservation of Categorical Knowledge in Alzheimer's Disease: A Computational Account. G. W. Humphreys, C. Lamote, T.J. Lloyd-Jones, An Interactive Activation Approach to Object Processing: Effects of Structural Similarity, Name Frequency, and Task in Normality and Pathology.

Footnote

This book is also a double special issue of the journal Memory which forms Issues 3 and 4 of Volume 3 (1995).

LCSH

Memory
Humans / Memory (Mental processes)

Series

Memory; 3,3/4

Subject

Memory
Humans / Memory (Mental processes)

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Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

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Abstract

In 2008 the FRBR/CRM Harmonisation Working Group has achieved a major milestone: a complete version of the object-oriented definition of FRBR (FRBRoo) was released for comment. After a brief overview of the history and context of the Working Group, this paper focuses on the primary contributions resulting from this work. - FRBRoo is a self-contained document which expresses the concepts of FRBR using the objectoriented methodology and framework of CIDOC CRM. It is an alternative view on library conceptualisation for a different purpose, not a replacement for FRBR. - This 'translation' process presented an opportunity to verify and confirm FRBR's internal consistency. - FRBRoo offers a common view of library and museum documentation as two kinds of information from memory institutions. Such a common view is necessary to provide interoperable information systems for all users interested in accessing common or related content. - The analysis provided an opportunity for mutual enrichment of FRBR and CIDOC CRM. Examples include: - - Addition of the modelling of time and events to FRBR, which can be seen in its application to the publishing process - - Clarification of the manifestation entity - - Explicit modelling of performances and recordings in FRBR - - Adding the work entity to CRM - - Adding the identifier assignment process to CRM. - Producing a formalisation which is more suited for implementation with object-oriented tools, and which facilitates the testing and adoption of FRBR concepts in implementations with different functional specifications and in different environments.

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Abstract

Nowadays, online data shows an astonishing increase and the issue of semantic indexing remains an open question. Ontologies and knowledge bases have been widely used to optimize performance. However, researchers are placing increased emphasis on internal relations of ontologies but neglect latent semantic relations between ontologies and documents. They generally annotate instances mentioned in documents, which are related to concepts in ontologies. In this paper, we propose an Ontology-based Latent Semantic Indexing approach utilizing Long Short-Term Memory networks (LSTM-OLSI). We utilize an importance-aware topic model to extract document-level semantic features and leverage ontologies to extract word-level contextual features. Then we encode the above two levels of features and match their embedding vectors utilizing LSTM networks. Finally, the experimental results reveal that LSTM-OLSI outperforms existing techniques and demonstrates deep comprehension of instances and articles.

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Abstract

Today's semantic web deals with meaning in a very restricted sense and offers static solutions. This is adequate for many scientific, technical purposes and for business transactions requiring machine-to-machine communication, but does not answer the needs of culture. Science, technology and business are concerned primarily with the latest findings, the state of the art, i.e. the paradigm or dominant world-view of the day. In this context, history is considered non-essential because it deals with things that are out of date. By contrast, culture faces a much larger challenge, namely, to re-present changes in ways of knowing; changing meanings in different places at a given time (synchronically) and over time (diachronically). Culture is about both objects and the commentaries on them; about a cumulative body of knowledge; about collective memory and heritage. Here, history plays a central role and older does not mean less important or less relevant. Hence, a Leonardo painting that is 400 years old, or a Greek statue that is 2500 years old, typically have richer commentaries and are often more valuable than their contemporary equivalents. In this context, the science of meaning (semantics) is necessarily much more complex than semantic primitives. A semantic web in the cultural domain must enable us to trace how meaning and knowledge organisation have evolved historically in different cultures. This paper examines five issues to address this challenge: 1) different world-views (i.e. a shift from substance to function and from ontology to multiple ontologies); 2) developments in definitions and meaning; 3) distinctions between words and concepts; 4) new classes of relations; and 5) dynamic models of knowledge organisation. These issues reveal that historical dimensions of cultural diversity in knowledge organisation are also central to classification of biological diversity. New ways are proposed of visualizing knowledge using a time/space horizon to distinguish between universals and particulars. It is suggested that new visualization methods make possible a history of questions as well as of answers, thus enabling dynamic access to cultural and historical dimensions of knowledge. Unlike earlier media, which were limited to recording factual dimensions of collective memory, digital media enable us to explore theories, ways of perceiving, ways of knowing; to enter into other mindsets and world-views and thus to attain novel insights and new levels of tolerance. Some practical consequences are outlined.

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Abstract

Overlapping structures in XML are not symptoms of a misunderstanding of the intrinsic characteristics of a text document nor evidence of extreme scholarly requirements far beyond those needed by the most common XML-based applications. On the contrary, overlaps have started to appear in a large number of incredibly popular applications hidden under the guise of syntactical tricks to the basic hierarchy of the XML data format. Unfortunately, syntactical tricks have the drawback that the affected structures require complicated workarounds to support even the simplest query or usage. In this article, we present Extremely Annotational Resource Description Framework (RDF) Markup (EARMARK), an approach to overlapping markup that simplifies and streamlines the management of multiple hierarchies on the same content, and provides an approach to sophisticated queries and usages over such structures without the need of ad-hoc applications, simply by using Semantic Web tools and languages. We compare how relevant tasks (e.g., the identification of the contribution of an author in a word processor document) are of some substantial complexity when using the original data format and become more or less trivial when using EARMARK. We finally evaluate positively the memory and disk requirements of EARMARK documents in comparison to Open Office and Microsoft Word XML-based formats.

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Content

Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.

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Content

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

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Pages

S.11-22

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Date

13.12.2017 14:17:22

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Date

26.12.2011 13:22:07

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Date

30. 5.2010 16:22:35

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Source

Spektrum der Wissenschaft. 2018, H.1, S.22-27

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Abstract

This chapter examines the nature of semantic relations and their main applications in information science. The nature and types of semantic relations are discussed from the perspectives of linguistics and psychology. An overview of the semantic relations used in knowledge structures such as thesauri and ontologies is provided, as well as the main techniques used in the automatic extraction of semantic relations from text. The chapter then reviews the use of semantic relations in information extraction, information retrieval, question-answering, and automatic text summarization applications. Concepts and relations are the foundation of knowledge and thought. When we look at the world, we perceive not a mass of colors but objects to which we automatically assign category labels. Our perceptual system automatically segments the world into concepts and categories. Concepts are the building blocks of knowledge; relations act as the cement that links concepts into knowledge structures. We spend much of our lives identifying regular associations and relations between objects, events, and processes so that the world has an understandable structure and predictability. Our lives and work depend on the accuracy and richness of this knowledge structure and its web of relations. Relations are needed for reasoning and inferencing. Chaffin and Herrmann (1988b, p. 290) noted that "relations between ideas have long been viewed as basic to thought, language, comprehension, and memory." Aristotle's Metaphysics (Aristotle, 1961; McKeon, expounded on several types of relations. The majority of the 30 entries in a section of the Metaphysics known today as the Philosophical Lexicon referred to relations and attributes, including cause, part-whole, same and opposite, quality (i.e., attribute) and kind-of, and defined different types of each relation. Hume (1955) pointed out that there is a connection between successive ideas in our minds, even in our dreams, and that the introduction of an idea in our mind automatically recalls an associated idea. He argued that all the objects of human reasoning are divided into relations of ideas and matters of fact and that factual reasoning is founded on the cause-effect relation. His Treatise of Human Nature identified seven kinds of relations: resemblance, identity, relations of time and place, proportion in quantity or number, degrees in quality, contrariety, and causation. Mill (1974, pp. 989-1004) discoursed on several types of relations, claiming that all things are either feelings, substances, or attributes, and that attributes can be a quality (which belongs to one object) or a relation to other objects.

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Content

Short Papers * A Database Backend for OWL, Jörg Henss, Joachim Kleb and Stephan Grimm. * Unifying SysML and OWL, Henson Graves. * The OWLlink Protocol, Thorsten Liebig, Marko Luther and Olaf Noppens. * A Reasoning Broker Framework for OWL, Juergen Bock, Tuvshintur Tserendorj, Yongchun Xu, Jens Wissmann and Stephan Grimm. * Change Representation For OWL 2 Ontologies, Raul Palma, Peter Haase, Oscar Corcho and Asunción Gómez-Pérez. * Practical Aspects of Query Rewriting for OWL 2, Héctor Pérez-Urbina, Ian Horrocks and Boris Motik. * CSage: Use of a Configurable Semantically Attributed Graph Editor as Framework for Editing and Visualization, Lawrence Levin. * A Conformance Test Suite for the OWL 2 RL/RDF Rules Language and the OWL 2 RDF-Based Semantics, Michael Schneider and Kai Mainzer. * Improving the Data Quality of Relational Databases using OBDA and OWL 2 QL, Olivier Cure. * Temporal Classes and OWL, Natalya Keberle. * Using Ontologies for Medical Image Retrieval - An Experiment, Jasmin Opitz, Bijan Parsia and Ulrike Sattler. * Task Representation and Retrieval in an Ontology-Guided Modelling System, Yuan Ren, Jens Lemcke, Andreas Friesen, Tirdad Rahmani, Srdjan Zivkovic, Boris Gregorcic, Andreas Bartho, Yuting Zhao and Jeff Z. Pan. * A platform for reasoning with OWL-EL knowledge bases in a Peer-to-Peer environment, Alexander De Leon and Michel Dumontier. * Axiomé: a Tool for the Elicitation and Management of SWRL Rules, Saeed Hassanpour, Martin O'Connor and Amar Das. * SQWRL: A Query Language for OWL, Martin O'Connor and Amar Das. * Classifying ELH Ontologies In SQL Databases, Vincent Delaitre and Yevgeny Kazakov. * A Semantic Web Approach to Represent and Retrieve Information in a Corporate Memory, Ana B. Rios-Alvarado, R. Carolina Medina-Ramirez and Ricardo Marcelin-Jimenez. * Towards a Graphical Notation for OWL 2, Elisa Kendall, Roy Bell, Roger Burkhart, Mark Dutra and Evan Wallace.

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22. 1.2017 16:54:03
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31. 7.2010 16:58:22

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14. 8.2011 13:33:22