Search (477 results, page 1 of 24)

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Abstract

The successes of information retrieval (IR) in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the external and carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems. This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a general framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our query representations, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity space.
This proposal includes plans to improve the quality of relevant entities with a co-learning framework that learns from both entity labels and document labels. We also plan to develop a hybrid ranking system that combines word based and entity based representations together with their uncertainties considered. At last, we plan to enrich the text representations with connections between entities. We propose several ways to infer entity graph representations for texts, and to rank documents using their structure representations. This dissertation overcomes the limitation of word based representations with external and carefully curated information from knowledge bases. We believe this thesis research is a solid start towards the new generation of intelligent, semantic, and structured information retrieval.

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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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

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.

Source

Graph-Based Methods for Natural Language Processing - proceedings of the Thirteenth Workshop (TextGraphs-13): November 4, 2019, Hong Kong : EMNLP-IJCNLP 2019. Ed.: Dmitry Ustalov

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Abstract

By the explosion of possibilities for a ubiquitous content production, the information overload problem reaches the level of complexity which cannot be managed by traditional modelling approaches anymore. Due to their pure syntactical nature traditional information retrieval approaches did not succeed in treating content itself (i.e. its meaning, and not its representation). This leads to a very low usefulness of the results of a retrieval process for a user's task at hand. In the last ten years ontologies have been emerged from an interesting conceptualisation paradigm to a very promising (semantic) modelling technology, especially in the context of the Semantic Web. From the information retrieval point of view, ontologies enable a machine-understandable form of content description, such that the retrieval process can be driven by the meaning of the content. However, the very ambiguous nature of the retrieval process in which a user, due to the unfamiliarity with the underlying repository and/or query syntax, just approximates his information need in a query, implies a necessity to include the user in the retrieval process more actively in order to close the gap between the meaning of the content and the meaning of a user's query (i.e. his information need). This thesis lays foundation for such an ontology-based interactive retrieval process, in which the retrieval system interacts with a user in order to conceptually interpret the meaning of his query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user's information need and to deliver the relevant content proactively. Moreover, the notion of content relevance for a user's query evolves from a content dependent artefact to the multidimensional context-dependent structure, strongly influenced by the user's preferences. This cooperation process is realized as the so-called Librarian Agent Query Refinement Process. In order to clarify the impact of an ontology on the retrieval process (regarding its complexity and quality), a set of methods and tools for different levels of content and query formalisation is developed, ranging from pure ontology-based inferencing to keyword-based querying in which semantics automatically emerges from the results. Our evaluation studies have shown that the possibilities to conceptualize a user's information need in the right manner and to interpret the retrieval results accordingly are key issues for realizing much more meaningful information retrieval systems.

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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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

Indexing plays a vital role in Information Retrieval. With the availability of huge volume of information, it has become necessary to index the information in such a way to make easier for the end users to find the information they want efficiently and accurately. Keyword-based indexing uses words as indexing terms. It is not capable of capturing the implicit relation among terms or the semantics of the words in the document. To eliminate this limitation, ontology-based indexing came into existence, which allows semantic based indexing to solve complex and indirect user queries. Ontologies are used for document indexing which allows semantic based information retrieval. Existing ontologies or the ones constructed from scratch are used presently for indexing. Constructing ontologies from scratch is a labor-intensive task and requires extensive domain knowledge whereas use of an existing ontology may leave some important concepts in documents un-annotated. Using multiple ontologies can overcome the problem of missing out concepts to a great extent, but it is difficult to manage (changes in ontologies over time by their developers) multiple ontologies and ontology heterogeneity also arises due to ontologies constructed by different ontology developers. One possible solution to managing multiple ontologies and build from scratch is to use modular ontologies for indexing.
Modular ontologies are built in modular manner by combining modules from multiple relevant ontologies. Ontology heterogeneity also arises during modular ontology construction because multiple ontologies are being dealt with, during this process. Ontologies need to be aligned before using them for modular ontology construction. The existing approaches for ontology alignment compare all the concepts of each ontology to be aligned, hence not optimized in terms of time and search space utilization. A new indexing technique is proposed based on modular ontology. An efficient ontology alignment technique is proposed to solve the heterogeneity problem during the construction of modular ontology. Results are satisfactory as Precision and Recall are improved by (8%) and (10%) respectively. The value of Pearsons Correlation Coefficient for degree of similarity, time, search space requirement, precision and recall are close to 1 which shows that the results are significant. Further research can be carried out for using modular ontology based indexing technique for Multimedia Information Retrieval and Bio-Medical information retrieval.

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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Abstract

The digitalization of research enables new scientific insights and methods, especially in the humanities. Nonetheless, electronic book editions, encyclopedias, mobile applications or web sites presenting research projects are not in broad use in academic philosophy. This is contradictory to the large amount of helpful tools facilitating research also bearing new scientific subjects and approaches. A possible solution to this dilemma is the systematization and promotion of these tools in order to improve their accessibility and fully exploit the potential of digitalization for philosophy.

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Abstract

In Linked Data, the use of owl:sameAs is ubiquitous in 'inter-linking' data-sets. However, there is a lurking suspicion within the Linked Data community that this use of owl:sameAs may be somehow incorrect, in particular with regards to its interactions with inference. In fact, owl:sameAs can be considered just one type of 'identity link', a link that declares two items to be identical in some fashion. After reviewing the definitions and history of the problem of identity in philosophy and knowledge representation, we outline four alternative readings of owl:sameAs, showing with examples how it is being (ab)used on the Web of data. Then we present possible solutions to this problem by introducing alternative identity links that rely on named graphs.

Source

Linked Data on the Web (LDOW2010). Proceedings of the WWW2010 Workshop on Linked Data on the Web. Raleigh, USA, April 27, 2010. Edited by Christian Bizer et al

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Date

30. 5.2010 16:22:35

Object

Mind Manager
Visual Mind

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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

Many top-level ontologies like Basic Formal Ontology (BFO) have been developed as a framework for ontologies in the natural sciences. The aim of the present essay is to extend the account of BFO to a very special layer of reality, the world of social entities. While natural entities like bacteria, thunderstorms or temperatures exist independently from human action and thought, social entities like countries, hospitals or money come into being only through human collective intentions and collective actions. Recently, the regional ontology of the social world has attracted considerable research interest in philosophy - witness, e.g., the pioneering work by Gilbert, Tuomela and Searle. There is a considerable class of phenomena that require the participation of more than one human agent: nobody can tango alone, play tennis against oneself, or set up a parliamentary democracy for oneself. Through cooperation and coordination of their wills and actions, agents can act together - they can perform social actions and group actions. An important kind of social action is the establishment of an institution (e.g. a hospital, a research agency or a marriage) through mutual promise or (social) contract. Another important kind of social action is the imposition of a social status on certain entities. For example, a society can impose the status of being a 20 Euro note on certain pieces of paper or the status of being an approved medication to a certain chemical substance.

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

In philosophy, Ontology is the basic description of things in the world. In information science, an ontology refers to an engineering artifact, constituted by a specific vocabulary used to describe a certain reality. Ontologies have been proposed for validating both conceptual models and conceptual schemas; however, these roles are quite dissimilar. In this article, we show that ontologies can be better understood if we classify the different uses of the term as it appears in the literature. First, we explain Ontology (upper case O) as used in Philosophy. Then, we propose a differentiation between ontologies of information systems and ontologies for information systems. All three concepts have an important role in information science. We clarify the different meanings and uses of Ontology and ontologies through a comparison of research by Wand and Weber and by Guarino in ontology-driven information systems. The contributions of this article are twofold: (a) It provides a better understanding of what ontologies are, and (b) it explains the double role of ontologies in information science research.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.6, S.786-793

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Abstract

In order to transfer the Chinese Classified Thesaurus (CCT) into a machine-processable format and provide CCT-based Web services, a pilot study has been conducted in which a variety of selected CCT classes and mapped thesaurus entries are encoded with SKOS. OWL and RDFS are also used to encode the same contents for the purposes of feasibility and cost-benefit comparison. CCT is a collected effort led by the National Library of China. It is an integration of the national standards Chinese Library Classification (CLC) 4th edition and Chinese Thesaurus (CT). As a manually created mapping product, CCT provides for each of the classes the corresponding thesaurus terms, and vice versa. The coverage of CCT includes four major clusters: philosophy, social sciences and humanities, natural sciences and technologies, and general works. There are 22 main-classes, 52,992 sub-classes and divisions, 110,837 preferred thesaurus terms, 35,690 entry terms (non-preferred terms), and 59,738 pre-coordinated headings (Chinese Classified Thesaurus, 2005) Major challenges of encoding this large vocabulary comes from its integrated structure. CCT is a result of the combination of two structures (illustrated in Figure 1): a thesaurus that uses ISO-2788 standardized structure and a classification scheme that is basically enumerative, but provides some flexibility for several kinds of synthetic mechanisms Other challenges include the complex relationships caused by differences of granularities of two original schemes and their presentation with various levels of SKOS elements; as well as the diverse coordination of entries due to the use of auxiliary tables and pre-coordinated headings derived from combining classes, subdivisions, and thesaurus terms, which do not correspond to existing unique identifiers. The poster reports the progress, shares the sample SKOS entries, and summarizes problems identified during the SKOS encoding process. Although OWL Lite and OWL Full provide richer expressiveness, the cost-benefit issues and the final purposes of encoding CCT raise questions of using such approaches.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

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Abstract

Looking for ontology in a search engine, one can find so many different approaches that it can be difficult to understand which field of research the subject belongs to and how it can be useful. The term ontology is employed within philosophy, computer science, and information science with different meanings. To take advantage of what ontology theories have to offer, one should understand what they address and where they come from. In information science, except for a few papers, there is no initiative toward clarifying what ontology really is and the connections that it fosters among different research fields. This article provides such a clarification. We begin by revisiting the meaning of the term in its original field, philosophy, to reach its current use in other research fields. We advocate that ontology is a genuine and relevant subject of research in information science. Finally, we conclude by offering our view of the opportunities for interdisciplinary research.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.8, S.1682-1693

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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.
Linguists in the structuralist tradition (e.g., Lyons, 1977; Saussure, 1959) have asserted that concepts cannot be defined on their own but only in relation to other concepts. Semantic relations appear to reflect a logical structure in the fundamental nature of thought (Caplan & Herrmann, 1993). Green, Bean, and Myaeng (2002) noted that semantic relations play a critical role in how we represent knowledge psychologically, linguistically, and computationally, and that many systems of knowledge representation start with a basic distinction between entities and relations. Green (2001, p. 3) said that "relationships are involved as we combine simple entities to form more complex entities, as we compare entities, as we group entities, as one entity performs a process on another entity, and so forth. Indeed, many things that we might initially regard as basic and elemental are revealed upon further examination to involve internal structure, or in other words, internal relationships." Concepts and relations are often expressed in language and text. Language is used not just for communicating concepts and relations, but also for representing, storing, and reasoning with concepts and relations. We shall examine the nature of semantic relations from a linguistic and psychological perspective, with an emphasis on relations expressed in text. The usefulness of semantic relations in information science, especially in ontology construction, information extraction, information retrieval, question-answering, and text summarization is discussed. Research and development in information science have focused on concepts and terms, but the focus will increasingly shift to the identification, processing, and management of relations to achieve greater effectiveness and refinement in information science techniques. Previous chapters in ARIST on natural language processing (Chowdhury, 2003), text mining (Trybula, 1999), information retrieval and the philosophy of language (Blair, 2003), and query expansion (Efthimiadis, 1996) provide a background for this discussion, as semantic relations are an important part of these applications.

Source

Annual review of information science and technology. 40(2006), S.157-228

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Abstract

More and more information is available on-line every day. The greater the amount of on-line information, the greater the demand for tools that process and disseminate this information. Processing electronic information in the form of text and answering users' queries about that information intelligently is one of the great challenges in natural language processing and information retrieval. The research presented in this talk is centered on the latter of these two tasks: intelligent information retrieval. In order for information to be retrieved, it first needs to be formalized in a database or knowledge base. The ontology for this formalization and assumptions it is based on are crucial to successful intelligent information retrieval. We have concentrated our effort on developing an ontology for representing knowledge in the domains of experimental sciences, molecular biology in particular. We show that existing ontological models cannot be readily applied to represent this domain adequately. For example, the fundamental notion of ontology design that every "real" object is defined as an instance of a category seems incompatible with the universe where objects can change their category as a result of experimental procedures. Another important problem is representing complex structures such as DNA, mixtures, populations of molecules, etc., that are very common in molecular biology. We present extensions that need to be made to an ontology to cover these issues: the representation of transformations that change the structure and/or category of their participants, and the component relations and spatial structures of complex objects. We demonstrate examples of how the proposed representations can be used to improve the quality and completeness of answers to user queries; discuss techniques for evaluating ontologies and show a prototype of an Information Retrieval System that we developed.

Content

Submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Computer Science in the College of Computer Science at Northeastern University, Boston, MA. Vgl.: http://www.stanford.edu/~natalya/papers/Thesis.pdf.

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Abstract

The explosion of the infosphere has led to a proliferation of metadata and formal ontology artefacts for information systems. Information scientists are creating ontologies and metadata in order to facilitate the sharing of meaningful information rather than similarly structured information. Formal ontologies are a complex form of metadata that specify the underlying concepts and their relationships that comprise the information of and for an information system. The most common understanding of ontology in computer and information sciences is Gruber's specification of a conceptualization. However, formal ontologies are problematic in that they simultaneously crystallize and decontextualize information, which in order to be meaningful must be adaptive in context. In trying to construct a correct taxonomical system, formal ontologies are focused on syntactic precision rather than meaningful exchange of information. Smith describes accurately the motivation and practice of ontology creation: It becomes a theory of the ontological content of certain representations . The elicited principles may or may not be true, but this, to the practitioner . is of no concern, since the significance of these principles lies elsewhere - for instance in yielding a correct account of the taxonomical system used by speakers of a given language or by scientists working in a given discipline. It is not fair to claim that syntax is irrelevant, but the meaning we make of information is dependent upon more than its syntactic structure.

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

The management of knowledge is considered to be one of the most important factors in economic growth today. However, the question of how to deal with knowledge in the most efficient way is still far from answered. We observe two fundamentally different approaches to the question of how we should deal with knowledge. One view sees knowledge as a kind of static object that can be gathered, compiled and distributed; the other view regards knowledge as a dynamic process. This disaccord finds a parallel in an objective-subjective distinction where the first position sees knowledge as independent of personal opinion whereas the second position regards it as interpretative. These discussions are not merely academic but crucially influence the way that knowledge management (KM) is realized, i.e. whether the focus is placed on knowledge artefacts such as documents or on subjective acts. The particular interest of the current essay concerns the question of how KM can be supported by information technology (IT) and which are the fundamental structures that must be regarded. Traditionally, IT-based approaches favour an object-oriented view of knowledge since knowledge artefacts are the objects that can be best processed by IT systems. This even leads to the view that knowledge artefacts represent the only form of knowledge. On the philosophical side this perspective is fostered by analytical investigations that emphasize the primacy of propositional knowledge that is closely related to knowledge artefacts.

Series

History and philosophy of technoscience; 3

Source

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