Search (201 results, page 1 of 11)

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Abstract

On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts. We call these attributes facets: classification has a few facets such as application (e.g., face recognition), model (e.g., svm, knn), and metric (e.g., precision). In this work, we aim at building faceted concept hierarchies from scientific literature. Hierarchy construction methods heavily rely on hypernym detection, however, the faceted relations are parent-to-child links but the hypernym relation is a multi-hop, i.e., ancestor-to-descendent link with a specific facet "type-of". We use information extraction techniques to find synonyms, sibling concepts, and ancestor-descendent relations from a data science corpus. And we propose a hierarchy growth algorithm to infer the parent-child links from the three types of relationships. It resolves conflicts by maintaining the acyclic structure of a hierarchy.

Content

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

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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.

Imprint

Pittsburgh, PA : Carnegie Mellon University, School of Computer Science, Language Technologies Institute

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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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Abstract

What is knowledge representation and reasoning (KR&R)? Alas, a thorough account would require a book, or at least a dedicated, full-length paper, but here we shall have to make do with something simpler. Since most readers are likely to have an intuitive grasp of the essence of KR&R, our simple account should suffice. The interesting thing is that this simple account itself makes reference to some of the foundational distinctions in the field of philosophy. These distinctions also play a central role in artificial intelligence (AI) and computer science. To begin with, the first distinction in KR&R is that we identify knowledge with knowledge that such-and-such holds (possibly to a degree), rather than knowing how. If you ask an expert tennis player how he manages to serve a ball at 130 miles per hour on his first serve, and then serve a safer, topspin serve on his second should the first be out, you may well receive a confession that, if truth be told, this athlete can't really tell you. He just does it; he does something he has been doing since his youth. Yet, there is no denying that he knows how to serve. In contrast, the knowledge in KR&R must be expressible in declarative statements. For example, our tennis player knows that if his first serve lands outside the service box, it's not in play. He thus knows a proposition, conditional in form.

Date

9. 2.2017 19:22:14

Series

History and philosophy of technoscience; 3

Source

Philosophy, computing and information science. Eds.: R. Hagengruber u. U.V. Riss

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Content

One of a series of three publications influenced by the travelling exhibit Places & Spaces: Mapping Science, curated by the Cyberinfrastructure for Network Science Center at Indiana University. - Additional materials can be found at http://http://scimaps.org/atlas2. Erweitert durch: Börner, Katy. Atlas of Science: Visualizing What We Know.

Date

22. 1.2017 16:54:03
22. 1.2017 17:10:56

LCSH

Science / Atlases
Science / Study and teaching / Graphic methods
Communication in science / Data processing

Subject

Science / Atlases
Science / Study and teaching / Graphic methods
Communication in science / Data processing

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Abstract

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. The earliest use of the term 'ontology' (or 'ontologia') seems to have been in 1606 in the book Ogdoas Scholastica by the German Protestant scholastic Jacob Lorhard. For Lorhard, as for many subsequent philosophers, 'ontology' is a synonym of 'metaphysics' (a label meaning literally: 'what comes after the Physics'), a term used by early students of Aristotle to refer to what Aristotle himself called 'first philosophy'. Some philosophers use 'ontology' and 'metaphysics' to refer to two distinct, though interrelated, disciplines, the former to refer to the study of what might exist; the latter to the study of which of the various alternative possible ontologies is in fact true of reality. The term - and the philosophical discipline of ontology - has enjoyed a chequered history since 1606, with a significant expansion, and consolidation, in recent decades. We shall not discuss here the successive rises and falls in philosophical acceptance of the term, but rather focus on certain phases in the history of recent philosophy which are most relevant to the consideration of its recent advance, and increased acceptance, also outside the discipline of philosophy.

Series

History and philosophy of technoscience; 3

Source

Philosophy, computing and information science. Eds.: R. Hagengruber u. U.V. Riss

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Abstract

There are quite a few success stories illustrating philosophy's relevance to information science. One can cite, for example, Leibniz's work on a characteristica universalis and a corresponding calculus ratiocinator through which he aspired to reduce reasoning to calculating. It goes without saying that formal logic initiated research on decidability and computational complexity. But even beyond the realm of formal logic, philosophy has served as a source of inspiration for developments in information and computer science. At the end of the twentieth century, formal ontology emerged from a quest for a semantic foundation of information systems having a higher reusability than systems being available at the time. A success story that is less well documented is the advent of frame systems in computer science. Minsky is credited with having laid out the foundational ideas of such systems. There, the logic programming approach to knowledge representation is criticized by arguing that one should be more careful about the way human beings recognize objects and situations. Notably, the paper draws heavily on the writings of Kuhn and the Gestalt-theorists. It is not our intent, however, to document the traces of the frame idea in the works of philosophers. What follows is, rather, an exposition of a methodology for representing scientific knowledge that is essentially frame-like. This methodology is labelled as structuralist theory of science or, in short, as structuralism. The frame-like character of its basic meta-theoretical concepts makes structuralism likely to be useful in knowledge representation.

Series

History and philosophy of technoscience; 3

Source

Philosophy, computing and information science. Eds.: R. Hagengruber u. U.V. Riss

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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

Ontology - in Aristotelian terms the science of being qua being - as a classical branch of philosophy describes the foundations of being in general. In this context, ontology is general metaphysics: the science of everything. Pursuing ontology means establishing some systematic order among the being, i.e. dividing things into categories or conceptual frameworks. Explaining the reasons why there are things or even anything, however, is part of what is called special metaphysics (theology, cosmology and psychology). If putting things into categories is the key issue of ontology, then general structures are its main level of analysis. To categorize things is to put them into a structural order. Such categorization of things enables one to understand what reality is about. If this is true, and characterizing the general structures of being is a reasonable access for us to reality, then two kinds of analysis of those structures are available: (i) realism and (ii) nominalism. In a realist (Aristotelian) ontology the general structures of being are understood as a kind of mirror reflecting things in their natural order. Those categories, as they are called in realism, then represent or show the structure of being. Ontological realism understands the relation between categories and being as a kind of correspondence or mapping which gives access to reality itself.

Series

History and philosophy of technoscience; 3

Source

Philosophy, computing and information science. Eds.: R. Hagengruber u. U.V. Riss

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Abstract

Knowledge tends to be structured differently in different types of information resources and information genres due to the different purposes of the resource/genre, and the characteristics of the media or format of the resource. This study investigates subject organization in three types of information resources: books (i.e. monographs), Web directories and information websites that provide information on particular subjects. Twelve subjects (topics) were selected in the areas of science, arts/humanities and social science, and two books, two Web directories and two information websites were sampled for each subject. The top two levels of the hierarchical subject organization in each resource were harvested and analyzed. Books have the highest proportion of general subject categories (e.g. history, theory and definition) and process categories (indicating step-by-step instructions). Information websites have the highest proportion of target user categories and genre-specific categories (e.g. about us and contact us), whereas Web directories have the highest proportion of specialty categories (i.e. sub-disciplines), industry-role categories (e.g. stores, schools and associations) and format categories (e.g. books, blogs and videos). Some disciplinary differences were also identified.

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Abstract

Knowledge representation, which is today used in database applications, artificial intelligence (AI), software engineering and many other disciplines of computer science has deep roots in logic and philosophy. In the beginning, there was Aristotle (384 bc-322 bc) who developed logic as a precise method for reasoning about knowledge. Syllogisms were introduced as formal patterns for representing special figures of logical deductions. According to Aristotle, the subject of ontology is the study of categories of things that exist or may exist in some domain. In modern times, Descartes considered the human brain as a store of knowledge representation. Recognition was made possible by an isomorphic correspondence between internal geometrical representations (ideae) and external situations and events. Leibniz was deeply influenced by these traditions. In his mathesis universalis, he required a universal formal language (lingua universalis) to represent human thinking by calculation procedures and to implement them by means of mechanical calculating machines. An ars iudicandi should allow every problem to be decided by an algorithm after representation in numeric symbols. An ars iveniendi should enable users to seek and enumerate desired data and solutions of problems. In the age of mechanics, knowledge representation was reduced to mechanical calculation procedures. In the twentieth century, computational cognitivism arose in the wake of Turing's theory of computability. In its functionalism, the hardware of a computer is related to the wetware of the human brain. The mind is understood as the software of a computer.

Series

History and philosophy of technoscience; 3

Source

Philosophy, computing and information science. Eds.: R. Hagengruber u. U.V. Riss

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Abstract

Purpose The open science movement calls for transparent and retraceable research processes. While infrastructures to support these practices in qualitative research are lacking, the design needs to consider different approaches and workflows. The paper bases on the definition of ontologies as shared conceptualizations of knowledge (Borst, 1999). The authors argue that participatory design is a good way to create these shared conceptualizations by giving domain experts and future users a voice in the design process via interviews, workshops and observations. Design/methodology/approach This paper presents a novel approach for creating ontologies in the field of open science using participatory design. As a case study the creation of an ontology for qualitative coding schemas is presented. Coding schemas are an important result of qualitative research, and reuse can yield great potential for open science making qualitative research more transparent, enhance sharing of coding schemas and teaching of qualitative methods. The participatory design process consisted of three parts: a requirement analysis using interviews and an observation, a design phase accompanied by interviews and an evaluation phase based on user tests as well as interviews. Findings The research showed several positive outcomes due to participatory design: higher commitment of users, mutual learning, high quality feedback and better quality of the ontology. However, there are two obstacles in this approach: First, contradictive answers by the interviewees, which needs to be balanced; second, this approach takes more time due to interview planning and analysis. Practical implications The implication of the paper is in the long run to decentralize the design of open science infrastructures and to involve parties affected on several levels. Originality/value In ontology design, several methods exist by using user-centered design or participatory design doing workshops. In this paper, the authors outline the potentials for participatory design using mainly interviews in creating an ontology for open science. The authors focus on close contact to researchers in order to build the ontology upon the expert's knowledge.

Date

20. 1.2015 18:30:22

Footnote

Beitrag in einem Special Issue: Showcasing Doctoral Research in Information Science.

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History and philosophy of technoscience; 3

Source

Philosophy, computing and information science. Eds.: R. Hagengruber u. U.V. Riss

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History and philosophy of technoscience; 3

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Philosophy, computing and information science. Eds.: R. Hagengruber u. U.V. Riss

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History and philosophy of technoscience; 3

Source

Philosophy, computing and information science. Eds.: R. Hagengruber u. U.V. Riss

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Abstract

The purpose of this work is to develop an ontology-based framework for developing an information retrieval system to cater to specific queries of users. For creating such an ontology, information was obtained from a wide range of information sources involved with brain tumour study and research. The information thus obtained was compiled and analysed to provide a standard, reliable and relevant information base to aid our proposed system. Facet-based methodology has been used for ontology formalization for quite some time. Ontology formalization involves different steps such as identification of the terminology, analysis, synthesis, standardization and ordering. A vast majority of the ontologies being developed nowadays lack flexibility. This becomes a formidable constraint when it comes to interoperability. We found that a facet-based method provides a distinct guideline for the development of a robust and flexible model concerning the domain of brain tumours. Our attempt has been to bridge library and information science and computer science, which itself involved an experimental approach. It was discovered that a faceted approach is really enduring, as it helps in the achievement of properties like navigation, exploration and faceted browsing. Computer-based brain tumour ontology supports the work of researchers towards gathering information on brain tumour research and allows users across the world to intelligently access new scientific information quickly and efficiently.

Date

12. 3.2016 13:21:22

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Series

Lecture notes in computer science; vol.4172

Source

Research and advanced technology for digital libraries : 10th European conference, proceedings / ECDL 2006, Alicante, Spain, September 17 - 22, 2006

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Source

Bulletin of the Association for Information Science and Technology. 41(2015) no.4, S.18-22

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Content

Submitted to the Faculty of the Computer Science and Engineering Department of the University of Engineering and Technology Lahore in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Computer Science (2009 - 009-PhD-CS-04). Vgl.: http://prr.hec.gov.pk/jspui/bitstream/123456789/8375/1/Taybah_Kiren_Computer_Science_HSR_2017_UET_Lahore_14.12.2017.pdf.

Date

20. 1.2015 18:30:22

Imprint

Lahore : University of Engineering and Technology / Department of Computer Science and Engineering

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Date

1. 6.2010 9:55:22

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1144-1165