Search (92 results, page 1 of 5)

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Abstract

Intuition suggests that one way to enhance the information retrieval process would be the use of phrases to characterize the contents of text. A number of researchers, however, have noted that phrases alone do not improve retrieval effectiveness. In this paper we briefly review the use of phrases in information retrieval and then suggest extensions to this paradigm using semantic information. We claim that semantic processing, which can be viewed as expressing relations between the concepts represented by phrases, will in fact enhance retrieval effectiveness. The availability of the UMLS® domain model, which we exploit extensively, significantly contributes to the feasibility of this processing.

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Abstract

The term "facet" was introduced into the field of library classification systems by Ranganathan in the 1930's [Ranganathan, 1962]. A facet is a viewpoint or aspect. In contrast to traditional classification systems, faceted systems are modular in that a domain is analyzed in terms of baseline facets which are then synthesized. In this paper, the term "facet" is used in a broader meaning. Facets can describe different aspects on the same level of abstraction or the same aspect on different levels of abstraction. The notion of facets is related to database views, multicontexts and conceptual scaling in formal concept analysis [Ganter and Wille, 1999], polymorphism in object-oriented design, aspect-oriented programming, views and contexts in description logic and semantic networks. This paper presents a definition of facets in terms of faceted knowledge representation that incorporates the traditional narrower notion of facets and potentially facilitates translation between different knowledge representation formalisms. A goal of this approach is a modular, machine-aided knowledge base design mechanism. A possible application is faceted thesaurus construction for information retrieval and data mining. Reasoning complexity depends on the size of the modules (facets). A more general analysis of complexity will be left for future research.

Date

22. 1.2016 17:30:31

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Abstract

Over the past 50 years, a variety of language-related capabilities has been developed in machine translation, information retrieval, speech recognition, text summarization, and so on. These applications rest upon a set of core techniques such as language modeling, information extraction, parsing, generation, and multimedia planning and integration; and they involve methods using statistics, rules, grammars, lexicons, ontologies, training techniques, and so on. It is a puzzling fact that although all of this work deals with language in some form or other, the major applications have each developed a separate research field. For example, there is no reason why speech recognition techniques involving n-grams and hidden Markov models could not have been used in machine translation 15 years earlier than they were, or why some of the lexical and semantic insights from the subarea called Computational Linguistics are still not used in information retrieval.
This picture will rapidly change. The twin challenges of massive information overload via the web and ubiquitous computers present us with an unavoidable task: developing techniques to handle multilingual and multi-modal information robustly and efficiently, with as high quality performance as possible. The most effective way for us to address such a mammoth task, and to ensure that our various techniques and applications fit together, is to start talking across the artificial research boundaries. Extending the current technologies will require integrating the various capabilities into multi-functional and multi-lingual natural language systems. However, at this time there is no clear vision of how these technologies could or should be assembled into a coherent framework. What would be involved in connecting a speech recognition system to an information retrieval engine, and then using machine translation and summarization software to process the retrieved text? How can traditional parsing and generation be enhanced with statistical techniques? What would be the effect of carefully crafted lexicons on traditional information retrieval? At which points should machine translation be interleaved within information retrieval systems to enable multilingual processing?

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Abstract

Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text (Landauer and Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other. The adequacy of LSA's reflection of human knowledge has been established in a variety of ways. For example, its scores overlap those of humans on standard vocabulary and subject matter tests; it mimics human word sorting and category judgments; it simulates word-word and passage-word lexical priming data; and as reported in 3 following articles in this issue, it accurately estimates passage coherence, learnability of passages by individual students, and the quality and quantity of knowledge contained in an essay.

Object

Latent Semantic Indexing

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Abstract

Information retrieval over the Internet increasingly requires the filtering of thousands of heterogeneous information sources. Important sources of information include not only traditional databases with structured data and queries, but also increasing numbers of non-traditional, semi- or unstructured collections such as Web sites, FTP archives, etc. As the number and variability of sources increases, new ways of automatically summarizing, discovering, and selecting collections relevant to a user's query are needed. One such method involves the use of classification schemes, such as the Library of Congress Classification (LCC), within which a collection may be represented based on its content, irrespective of the structure of the actual data or documents. For such a system to be useful in a large-scale distributed environment, it must be easy to use for both collection managers and users. As a result, it must be possible to classify documents automatically within a classification scheme. Furthermore, there must be a straightforward and intuitive interface with which the user may use the scheme to assist in information retrieval (IR). Our work with the Alexandria Digital Library (ADL) Project focuses on geo-referenced information, whether text, maps, aerial photographs, or satellite images. As a result, we have emphasized techniques which work with both text and non-text, such as combined textual and graphical queries, multi-dimensional indexing, and IR methods which are not solely dependent on words or phrases. Part of this work involves locating relevant online sources of information. In particular, we have designed and are currently testing aspects of an architecture, Pharos, which we believe will scale up to 1.000.000 heterogeneous sources. Pharos accommodates heterogeneity in content and format, both among multiple sources as well as within a single source. That is, we consider sources to include Web sites, FTP archives, newsgroups, and full digital libraries; all of these systems can include a wide variety of content and multimedia data formats. Pharos is based on the use of hierarchical classification schemes. These include not only well-known 'subject' (or 'concept') based schemes such as the Dewey Decimal System and the LCC, but also, for example, geographic classifications, which might be constructed as layers of smaller and smaller hierarchical longitude/latitude boxes. Pharos is designed to work with sophisticated queries which utilize subjects, geographical locations, temporal specifications, and other types of information domains. The Pharos architecture requires that hierarchically structured collection metadata be extracted so that it can be partitioned in such a way as to greatly enhance scalability. Automated classification is important to Pharos because it allows information sources to extract the requisite collection metadata automatically that must be distributed.
We are currently experimenting with newsgroups as collections. We have built an initial prototype which automatically classifies and summarizes newsgroups within the LCC. (The prototype can be tested below, and more details may be found at http://pharos.alexandria.ucsb.edu/). The prototype uses electronic library catalog records as a `training set' and Latent Semantic Indexing (LSI) for IR. We use the training set to build a rich set of classification terminology, and associate these terms with the relevant categories in the LCC. This association between terms and classification categories allows us to relate users' queries to nodes in the LCC so that users can select appropriate query categories. Newsgroups are similarly associated with classification categories. Pharos then matches the categories selected by users to relevant newsgroups. In principle, this approach allows users to exclude newsgroups that might have been selected based on an unintended meaning of a query term, and to include newsgroups with relevant content even though the exact query terms may not have been used. This work is extensible to other types of classification, including geographical, temporal, and image feature. Before discussing the methodology of the collection summarization and selection, we first present an online demonstration below. The demonstration is not intended to be a complete end-user interface. Rather, it is intended merely to offer a view of the process to suggest the "look and feel" of the prototype. The demo works as follows. First supply it with a few keywords of interest. The system will then use those terms to try to return to you the most relevant subject categories within the LCC. Assuming that the system recognizes any of your terms (it has over 400,000 terms indexed), it will give you a list of 15 LCC categories sorted by relevancy ranking. From there, you have two choices. The first choice, by clicking on the "News" links, is to get a list of newsgroups which the system has identified as relevant to the LCC category you select. The other choice, by clicking on the LCC ID links, is to enter the LCC hierarchy starting at the category of your choice and navigate the tree until you locate the best category for your query. From there, again, you can get a list of newsgroups by clicking on the "News" links. After having shown this demonstration to many people, we would like to suggest that you first give it easier examples before trying to break it. For example, "prostate cancer" (discussed below), "remote sensing", "investment banking", and "gershwin" all work reasonably well.

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Abstract

ANDREW, K. u. F. KAPPE: Serving information to the Web with Hyper-G; BARBIERI, K., H.M. DOERR u. D. DWYER: Creating a virtual classroom for interactive education on the Web; CAMPBELL, J.K., S.B. JONES, N.M. STEPHENS u. S. HURLEY: Constructing educational courseware using NCSA Mosaic and the World Wide Web; CATLEDGE, L.L. u. J.E. PITKOW: Characterizing browsing strategies in the World-Wide Web; CLAUSNITZER, A. u. P. VOGEL: A WWW interface to the OMNIS/Myriad literature retrieval engine; FISCHER, R. u. L. PERROCHON: IDLE: Unified W3-access to interactive information servers; FOLEY, J.D.: Visualizing the World-Wide Web with the navigational view builder; FRANKLIN, S.D. u. B. IBRAHIM: Advanced educational uses of the World-Wide Web; FUHR, N., U. PFEIFER u. T. HUYNH: Searching structured documents with the enhanced retrieval functionality of free WAIS-sf and SFgate; FIORITO, M., J. OKSANEN u. D.R. IOIVANE: An educational environment using WWW; KENT, R.E. u. C. NEUSS: Conceptual analysis of resource meta-information; SHELDON, M.A. u. R. WEISS: Discover: a resource discovery system based on content routing; WINOGRAD, T.: Beyond browsing: shared comments, SOAPs, Trails, and On-line communities

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Abstract

In this era of the Internet and distributed, multimedia computing, new and emerging classes of information systems applications have swept into the lives of office workers and people in general. From digital libraries, multimedia systems, geographic information systems, and collaborative computing to electronic commerce, virtual reality, and electronic video arts and games, these applications have created tremendous opportunities for information and computer science researchers and practitioners. As applications become more pervasive, pressing, and diverse, several well-known information retrieval (IR) problems have become even more urgent. Information overload, a result of the ease of information creation and transmission via the Internet and WWW, has become more troublesome (e.g., even stockbrokers and elementary school students, heavily exposed to various WWW search engines, are versed in such IR terminology as recall and precision). Significant variations in database formats and structures, the richness of information media (text, audio, and video), and an abundance of multilingual information content also have created severe information interoperability problems -- structural interoperability, media interoperability, and multilingual interoperability.

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Theme

Klassifikationssysteme im Online-Retrieval

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Abstract

Zu den großen Herausforderungen einer sinnvollen Suche im WWW gehören die riesige Menge des Verfügbaren und die Sparchbarrieren. Verfahren, die die Web-Ressourcen im Hinblick auf ein effizienteres Retrieval inhaltlich strukturieren, werden daher ebenso dringend benötigt wie Programme, die mit der Sprachvielfalt umgehen können. Im folgenden Vortrag werden wir einige Ansätze diskutieren, die zur Bewältigung der beiden Probleme derzeit unternommen werden

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Abstract

Over the past three years, the Dublin Core Metadata Initiative has achieved a broad international consensus on the semantics of a simple element set for describing electronic resources. Since the first workshop in March 1995, which was reported in the very first issue of D-Lib Magazine, Dublin Core has been the topic of perhaps a dozen articles here. Originally intended to be simple and intuitive enough for authors to tag Web pages without special training, Dublin Core is being adapted now for more specialized uses, from government information and legal deposit to museum informatics and electronic commerce. To meet such specialized requirements, Dublin Core can be customized with additional elements or qualifiers. However, these refinements can compromise interoperability across applications. There are tradeoffs between using specific terms that precisely meet local needs versus general terms that are understood more widely. We can better understand this inevitable tension between simplicity and complexity if we recognize that metadata is a form of human language. With Dublin Core, as with a natural language, people are inclined to stretch definitions, make general terms more specific, specific terms more general, misunderstand intended meanings, and coin new terms. One goal of this paper, therefore, will be to examine the experience of some related ways to seek semantic interoperability through simplicity: planned languages, interlingua constructs, and pidgins. The problem of semantic interoperability is compounded when we consider Dublin Core in translation. All of the workshops, documents, mailing lists, user guides, and working group outputs of the Dublin Core Initiative have been in English. But in many countries and for many applications, people need a metadata standard in their own language. In principle, the broad elements of Dublin Core can be defined equally well in Bulgarian or Hindi. Since Dublin Core is a controlled standard, however, any parallel definitions need to be kept in sync as the standard evolves. Another goal of the paper, then, will be to define the conceptual and organizational problem of maintaining a metadata standard in multiple languages. In addition to a name and definition, which are meant for human consumption, each Dublin Core element has a label, or indexing token, meant for harvesting by search engines. For practical reasons, these machine-readable tokens are English-looking strings such as Creator and Subject (just as HTML tags are called HEAD, BODY, or TITLE). These tokens, which are shared by Dublin Cores in every language, ensure that metadata fields created in any particular language are indexed together across repositories. As symbols of underlying universal semantics, these tokens form the basis of semantic interoperability among the multiple Dublin Cores. As long as we limit ourselves to sharing these indexing tokens among exact translations of a simple set of fifteen broad elements, the definitions of which fit easily onto two pages, the problem of Dublin Core in multiple languages is straightforward. But nothing having to do with human language is ever so simple. Just as speakers of various languages must learn the language of Dublin Core in their own tongues, we must find the right words to talk about a metadata language that is expressable in many discipline-specific jargons and natural languages and that inevitably will evolve and change over time.

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Abstract

The Z39.50 standard for information retrieval is important from a number of perspectives. While still not widely known within the computer networking community, it is a mature standard that represents the culmination of two decades of thinking and debate about how information retrieval functions can be modeled, standardized, and implemented in a distributed systems environment. And - importantly -- it has been tested through substantial deployment experience. Z39.50 is one of the few examples we have to date of a protocol that actually goes beyond codifying mechanism and moves into the area of standardizing shared semantic knowledge. The extent to which this should be a goal of the protocol has been an ongoing source of controversy and tension within the developer community, and differing views on this issue can be seen both in the standard itself and the way that it is used in practice. Given the growing emphasis on issues such as "semantic interoperability" as part of the research agenda for digital libraries (see Clifford A. Lynch and Hector Garcia-Molina. Interoperability, Scaling, and the Digital Libraries Research Agenda, Report on the May 18-19, 1995 IITA Libraries Workshop, <http://www- diglib.stanford.edu/diglib/pub/reports/iita-dlw/main.html>), the insights gained by the Z39.50 community into the complex interactions among various definitions of semantics and interoperability are particularly relevant. The development process for the Z39.50 standard is also of interest in its own right. Its history, dating back to the 1970s, spans a period that saw the eclipse of formal standards-making agencies by groups such as the Internet Engineering Task Force (IETF) and informal standards development consortia. Moreover, in order to achieve meaningful implementation, Z39.50 had to move beyond its origins in the OSI debacle of the 1980s. Z39.50 has also been, to some extent, a victim of its own success -- or at least promise. Recent versions of the standard are highly extensible, and the consensus process of standards development has made it hospitable to an ever-growing set of new communities and requirements. As this process of extension has proceeded, it has become ever less clear what the appropriate scope and boundaries of the protocol should be, and what expectations one should have of practical interoperability among implementations of the standard. Z39.50 thus offers an excellent case study of the problems involved in managing the evolution of a standard over time. It may well offer useful lessons for the future of other standards such as HTTP and HTML, which seem to be facing some of the same issues.

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Abstract

RDF - the Resource Description Framework - is a foundation for processing metadata; it provides interoperability between applications that exchange machine-understandable information on the Web. RDF emphasizes facilities to enable automated processing of Web resources. RDF metadata can be used in a variety of application areas; for example: in resource discovery to provide better search engine capabilities; in cataloging for describing the content and content relationships available at a particular Web site, page, or digital library; by intelligent software agents to facilitate knowledge sharing and exchange; in content rating; in describing collections of pages that represent a single logical "document"; for describing intellectual property rights of Web pages, and in many others. RDF with digital signatures will be key to building the "Web of Trust" for electronic commerce, collaboration, and other applications. Metadata is "data about data" or specifically in the context of RDF "data describing web resources." The distinction between "data" and "metadata" is not an absolute one; it is a distinction created primarily by a particular application. Many times the same resource will be interpreted in both ways simultaneously. RDF encourages this view by using XML as the encoding syntax for the metadata. The resources being described by RDF are, in general, anything that can be named via a URI. The broad goal of RDF is to define a mechanism for describing resources that makes no assumptions about a particular application domain, nor defines the semantics of any application domain. The definition of the mechanism should be domain neutral, yet the mechanism should be suitable for describing information about any domain. This document introduces a model for representing RDF metadata and one syntax for expressing and transporting this metadata in a manner that maximizes the interoperability of independently developed web servers and clients. The syntax described in this document is best considered as a "serialization syntax" for the underlying RDF representation model. The serialization syntax is XML, XML being the W3C's work-in-progress to define a richer Web syntax for a variety of applications. RDF and XML are complementary; there will be alternate ways to represent the same RDF data model, some more suitable for direct human authoring. Future work may lead to including such alternatives in this document.

Theme

Semantic Web

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Abstract

Information retrieval over the Internet increasingly requires the filtering of thousands of heterogeneous information sources. Important sources of information include not only traditional databases with structured data and queries, but also increasing numbers of non-traditional, semi- or unstructured collections such as Web sites, FTP archives, etc. As the number and variability of sources increases, new ways of automatically summarizing, discovering, and selecting collections relevant to a user's query are needed. One such method involves the use of classification schemes, such as the Library of Congress Classification (LCC) [10], within which a collection may be represented based on its content, irrespective of the structure of the actual data or documents. For such a system to be useful in a large-scale distributed environment, it must be easy to use for both collection managers and users. As a result, it must be possible to classify documents automatically within a classification scheme. Furthermore, there must be a straightforward and intuitive interface with which the user may use the scheme to assist in information retrieval (IR).

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Abstract

Faceted Knowledge Representation provides a formalism for implementing knowledge systems. The basic notions of faceted knowledge representation are "unit", "relation", "facet" and "interpretation". Units are atomic elements and can be abstract elements or refer to external objects in an application. Relations are sequences or matrices of 0 and 1's (binary matrices). Facets are relational structures that combine units and relations. Each facet represents an aspect or viewpoint of a knowledge system. Interpretations are mappings that can be used to translate between different representations. This paper introduces the basic notions of faceted knowledge representation. The formalism is applied here to an abstract modeling of a faceted thesaurus as used in information retrieval.

Date

22. 1.2016 17:30:31

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Content

Ramana Rao (Inxight, Palo Alto, CA) 7 ± 2 Insights on achieving Effective Information Access Session One: Updates and a twelve month perspective Danny Sullivan (Search Engine Watch, US / England) Portalization and other search trends Carol Tenopir (University of Tennessee) Search realities faced by end users and professional searchers Session Two: Today's search engines and beyond Daniel Hoogterp (Retrieval Technologies, McLean, VA) Effective presentation and utilization of search techniques Rick Kenny (Fulcrum Technologies, Ontario, Canada) Beyond document clustering: The knowledge impact statement Gary Stock (Ingenius, Kalamazoo, MI) Automated change monitoring Gary Culliss (Direct Hit, Wellesley Hills, MA) User popularity ranked search engines Byron Dom (IBM, CA) Automatically finding the best pages on the World Wide Web (CLEVER) Peter Tomassi (LookSmart, San Francisco, CA) Adding human intellect to search technology Session Three: Panel discussion: Human v automated categorization and editing Ev Brenner (New York, NY)- Chairman James Callan (University of Massachusetts, MA) Marc Krellenstein (Northern Light Technology, Cambridge, MA) Dan Miller (Ask Jeeves, Berkeley, CA) Session Four: Updates and a twelve month perspective Steve Arnold (AIT, Harrods Creek, KY) Review: The leading edge in search and retrieval software Ellen Voorhees (NIST, Gaithersburg, MD) TREC update Session Five: Search engines now and beyond Intelligent Agents John Snyder (Muscat, Cambridge, England) Practical issues behind intelligent agents Text summarization Therese Firmin, (Dept of Defense, Ft George G. Meade, MD) The TIPSTER/SUMMAC evaluation of automatic text summarization systems Cross language searching Elizabeth Liddy (TextWise, Syracuse, NY) A conceptual interlingua approach to cross-language retrieval. Video search and retrieval Armon Amir (IBM, Almaden, CA) CueVideo: Modular system for automatic indexing and browsing of video/audio Speech recognition Michael Witbrock (Lycos, Waltham, MA) Retrieval of spoken documents Visualization James A. Wise (Integral Visuals, Richland, WA) Information visualization in the new millennium: Emerging science or passing fashion? Text mining David Evans (Claritech, Pittsburgh, PA) Text mining - towards decision support

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LCSH

Information storage and retrieval systems / Research / Congresses
Information retrieval / Research / Congresses

RSWK

Information retrieval / Kongress / Aberdeen <1997>

Subject

Information storage and retrieval systems / Research / Congresses
Information retrieval / Research / Congresses
Information retrieval / Kongress / Aberdeen <1997>

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Date

22. 6.2002 19:35:09

Theme

Klassifikationssysteme im Online-Retrieval

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Theme

Klassifikationssysteme im Online-Retrieval

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Abstract

Although commonly used in both commercial and experimental information retrieval systems, thesauri have not demonstrated consistent benefits for retrieval performance, and it is difficult to construct a thesaurus automatically for large text databases. In this paper, an approach, called PhraseFinder, is proposed to construct collection-dependent association thesauri automatically using large full-text document collections. The association thesaurus can be accessed through natural language queries in INQUERY, an information retrieval system based on the probabilistic inference network. Experiments are conducted in INQUERY to evaluate different types of association thesauri, and thesauri constructed for a variety of collections

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Theme

Klassifikationssysteme im Online-Retrieval