Search (24 results, page 1 of 2)

0.095501445 = sum of:
  0.068955295 = product of:
    0.20686588 = sum of:
      0.20686588 = weight(_text_:3a in 4997) [ClassicSimilarity], result of:
        0.20686588 = score(doc=4997,freq=2.0), product of:
          0.4416923 = queryWeight, product of:
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.052098576 = queryNorm
          0.46834838 = fieldWeight in 4997, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.0390625 = fieldNorm(doc=4997)
    0.33333334 = coord(1/3)
  0.026546149 = product of:
    0.053092297 = sum of:
      0.053092297 = weight(_text_:web in 4997) [ClassicSimilarity], result of:
        0.053092297 = score(doc=4997,freq=6.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.3122631 = fieldWeight in 4997, product of:
            2.4494898 = tf(freq=6.0), with freq of:
              6.0 = termFreq=6.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.0390625 = fieldNorm(doc=4997)
    0.5 = coord(1/2)

Abstract

While classifications are heavily used to categorize web content, the evolution of the web foresees a more formal structure - ontology - which can serve this purpose. Ontologies are core artifacts of the Semantic Web which enable machines to use inference rules to conduct automated reasoning on data. Lightweight ontologies bridge the gap between classifications and ontologies. A lightweight ontology (LO) is an ontology representing a backbone taxonomy where the concept of the child node is more specific than the concept of the parent node. Formal lightweight ontologies can be generated from their informal ones. The key applications of formal lightweight ontologies are document classification, semantic search, and data integration. However, these applications suffer from the following problems: the disambiguation accuracy of the state of the art NLP tools used in generating formal lightweight ontologies from their informal ones; the lack of background knowledge needed for the formal lightweight ontologies; and the limitation of ontology reuse. In this dissertation, we propose a novel solution to these problems in formal lightweight ontologies; namely, faceted lightweight ontology (FLO). FLO is a lightweight ontology in which terms, present in each node label, and their concepts, are available in the background knowledge (BK), which is organized as a set of facets. A facet can be defined as a distinctive property of the groups of concepts that can help in differentiating one group from another. Background knowledge can be defined as a subset of a knowledge base, such as WordNet, and often represents a specific domain.

Content

PhD Dissertation at International Doctorate School in Information and Communication Technology. Vgl.: https%3A%2F%2Fcore.ac.uk%2Fdownload%2Fpdf%2F150083013.pdf&usg=AOvVaw2n-qisNagpyT0lli_6QbAQ.

0.0725041 = sum of:
  0.055164233 = product of:
    0.1654927 = sum of:
      0.1654927 = weight(_text_:3a in 701) [ClassicSimilarity], result of:
        0.1654927 = score(doc=701,freq=2.0), product of:
          0.4416923 = queryWeight, product of:
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.052098576 = queryNorm
          0.3746787 = fieldWeight in 701, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.03125 = fieldNorm(doc=701)
    0.33333334 = coord(1/3)
  0.017339872 = product of:
    0.034679744 = sum of:
      0.034679744 = weight(_text_:web in 701) [ClassicSimilarity], result of:
        0.034679744 = score(doc=701,freq=4.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.2039694 = fieldWeight in 701, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.03125 = fieldNorm(doc=701)
    0.5 = coord(1/2)

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.

Theme

Semantic Web

0.057959333 = product of:
  0.115918666 = sum of:
    0.115918666 = sum of:
      0.07356684 = weight(_text_:web in 563) [ClassicSimilarity], result of:
        0.07356684 = score(doc=563,freq=8.0), product of:
          0.17002425 = queryWeight, product of:
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.052098576 = queryNorm
          0.43268442 = fieldWeight in 563, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            3.2635105 = idf(docFreq=4597, maxDocs=44218)
            0.046875 = fieldNorm(doc=563)
      0.042351827 = weight(_text_:22 in 563) [ClassicSimilarity], result of:
        0.042351827 = score(doc=563,freq=2.0), product of:
          0.18244034 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052098576 = queryNorm
          0.23214069 = fieldWeight in 563, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=563)
  0.5 = coord(1/2)

Abstract

In this thesis we propose three new word association measures for multi-word term extraction. We combine these association measures with LocalMaxs algorithm in our extraction model and compare the results of different multi-word term extraction methods. Our approach is language and domain independent and requires no training data. It can be applied to such tasks as text summarization, information retrieval, and document classification. We further explore the potential of using multi-word terms as an effective representation for general web-page summarization. We extract multi-word terms from human written summaries in a large collection of web-pages, and generate the summaries by aligning document words with these multi-word terms. Our system applies machine translation technology to learn the aligning process from a training set and focuses on selecting high quality multi-word terms from human written summaries to generate suitable results for web-page summarization.

Date

10. 1.2013 19:22:47

0.027582116 = product of:
  0.055164233 = sum of:
    0.055164233 = product of:
      0.1654927 = sum of:
        0.1654927 = weight(_text_:3a in 5820) [ClassicSimilarity], result of:
          0.1654927 = score(doc=5820,freq=2.0), product of:
            0.4416923 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052098576 = queryNorm
            0.3746787 = fieldWeight in 5820, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03125 = fieldNorm(doc=5820)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)

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.

0.019964844 = product of:
  0.039929688 = sum of:
    0.039929688 = product of:
      0.079859376 = sum of:
        0.079859376 = weight(_text_:22 in 4204) [ClassicSimilarity], result of:
          0.079859376 = score(doc=4204,freq=4.0), product of:
            0.18244034 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052098576 = queryNorm
            0.4377287 = fieldWeight in 4204, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=4204)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 6.2018 13:24:08
22. 6.2018 13:54:52

0.019386567 = product of:
  0.038773134 = sum of:
    0.038773134 = product of:
      0.07754627 = sum of:
        0.07754627 = weight(_text_:web in 2567) [ClassicSimilarity], result of:
          0.07754627 = score(doc=2567,freq=20.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.45608947 = fieldWeight in 2567, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.03125 = fieldNorm(doc=2567)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

As the Web continues to grow and encompass broader and more diverse sources of information, providing effective search facilities to users becomes an increasingly challenging problem. To help users deal with the deluge of Web-accessible information, we propose a search system which makes use of context to improve search results in a scalable way. By context, we mean any sources of information, in addition to any search query, that provide clues about the user's true information need. For instance, a user's bookmarks and search history can be considered a part of the search context. We consider two types of context-based search. The first type of functionality we consider is "similarity search." In this case, as the user is browsing Web pages, URLs for pages similar to the current page are retrieved and displayed in a side panel. No query is explicitly issued; context alone (i.e., the page currently being viewed) is used to provide the user with useful related information. The second type of functionality involves taking search context into account when ranking results to standard search queries. Web search differs from traditional information retrieval tasks in several major ways, making effective context-sensitive Web search challenging. First, scalability is of critical importance. With billions of publicly accessible documents, the Web is much larger than traditional datasets. Similarly, with millions of search queries issued each day, the query load is much higher than for traditional information retrieval systems. Second, there are no guarantees on the quality ofWeb pages, with Web-authors taking an adversarial, rather than cooperative, approach in attempts to inflate the rankings of their pages. Third, there is a significant amount of metadata embodied in the link structure corresponding to the hyperlinks between Web pages that can be exploitedduring the retrieval process. In this thesis, we design a search system, using the Stanford WebBase platform, that exploits the link structure of the Web to provide scalable, context-sensitive search.

0.015326426 = product of:
  0.030652853 = sum of:
    0.030652853 = product of:
      0.061305705 = sum of:
        0.061305705 = weight(_text_:web in 4232) [ClassicSimilarity], result of:
          0.061305705 = score(doc=4232,freq=32.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.36057037 = fieldWeight in 4232, product of:
              5.656854 = tf(freq=32.0), with freq of:
                32.0 = termFreq=32.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.01953125 = fieldNorm(doc=4232)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

After the launch of the World Wide Web, it became clear that searching documentson the Web would not be trivial. Well-known engines to search the web, like Google, focus on search in web documents using keywords. The documents are structured and indexed to ensure keywords match documents as accurately as possible. However, searching by keywords does not always suice. It is oen the case that users do not know exactly how to formulate the search query or which keywords guarantee retrieving the most relevant documents. Besides that, it occurs that users rather want to browse information than looking up something specific. It turned out that there is need for systems that enable more interactivity and facilitate the gradual refinement of search queries to explore the Web. Users expect more from the Web because the short keyword-based queries they pose during search, do not suffice for all cases. On top of that, the Web is changing structurally. The Web comprises, apart from a collection of documents, more and more linked data, pieces of information structured so they can be processed by machines. The consequently applied semantics allow users to exactly indicate machines their search intentions. This is made possible by describing data following controlled vocabularies, concept lists composed by experts, published uniquely identifiable on the Web. Even so, it is still not trivial to explore data on the Web. There is a large variety of vocabularies and various data sources use different terms to identify the same concepts.
This PhD-thesis describes how to effectively explore linked data on the Web. The main focus is on scenarios where users want to discover relationships between resources rather than finding out more about something specific. Searching for a specific document or piece of information fits in the theoretical framework of information retrieval and is associated with exploratory search. Exploratory search goes beyond 'looking up something' when users are seeking more detailed understanding, further investigation or navigation of the initial search results. The ideas behind exploratory search and querying linked data merge when it comes to the way knowledge is represented and indexed by machines - how data is structured and stored for optimal searchability. Queries and information should be aligned to facilitate that searches also reveal connections between results. This implies that they take into account the same semantic entities, relevant at that moment. To realize this, we research three techniques that are evaluated one by one in an experimental set-up to assess how well they succeed in their goals. In the end, the techniques are applied to a practical use case that focuses on forming a bridge between the Web and the use of digital libraries in scientific research. Our first technique focuses on the interactive visualization of search results. Linked data resources can be brought in relation with each other at will. This leads to complex and diverse graphs structures. Our technique facilitates navigation and supports a workflow starting from a broad overview on the data and allows narrowing down until the desired level of detail to then broaden again. To validate the flow, two visualizations where implemented and presented to test-users. The users judged the usability of the visualizations, how the visualizations fit in the workflow and to which degree their features seemed useful for the exploration of linked data.
The ideas behind exploratory search and querying linked data merge when it comes to the way knowledge is represented and indexed by machines - how data is structured and stored for optimal searchability. eries and information should be aligned to facilitate that searches also reveal connections between results. This implies that they take into account the same semantic entities, relevant at that moment. To realize this, we research three techniques that are evaluated one by one in an experimental set-up to assess how well they succeed in their goals. In the end, the techniques are applied to a practical use case that focuses on forming a bridge between the Web and the use of digital libraries in scientific research.

Theme

Semantic Web

0.013708373 = product of:
  0.027416745 = sum of:
    0.027416745 = product of:
      0.05483349 = sum of:
        0.05483349 = weight(_text_:web in 2281) [ClassicSimilarity], result of:
          0.05483349 = score(doc=2281,freq=10.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.32250395 = fieldWeight in 2281, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.03125 = fieldNorm(doc=2281)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

An information system like the Web is a continuously evolving system consisting of multiple heterogeneous information sources, covering a wide domain of discourse, and a huge number of users (human or software) with diverse characteristics and needs, that produce and consume information. The challenge nowadays is to build a scalable information infrastructure enabling the effective, accurate, content based retrieval of information, in a way that adapts to the characteristics and interests of the users. The aim of this work is to propose formally sound methods for building such an information network based on ontologies which are widely used and are easy to grasp by ordinary Web users. The main results of this work are: - A novel scheme for indexing and retrieving objects according to multiple aspects or facets. The proposed scheme is a faceted scheme enriched with a method for specifying the combinations of terms that are valid. We give a model-theoretic interpretation to this model and we provide mechanisms for inferring the valid combinations of terms. This inference service can be exploited for preventing errors during the indexing process, which is very important especially in the case where the indexing is done collaboratively by many users, and for deriving "complete" navigation trees suitable for browsing through the Web. The proposed scheme has several advantages over the hierarchical classification schemes currently employed by Web catalogs, namely, conceptual clarity (it is easier to understand), compactness (it takes less space), and scalability (the update operations can be formulated more easily and be performed more effciently). - A exible and effecient model for building mediators over ontology based information sources. The proposed mediators support several modes of query translation and evaluation which can accommodate various application needs and levels of answer quality. The proposed model can be used for providing users with customized views of Web catalogs. It can also complement the techniques for building mediators over relational sources so as to support approximate translation of partially ordered domain values.

0.013004904 = product of:
  0.026009807 = sum of:
    0.026009807 = product of:
      0.052019615 = sum of:
        0.052019615 = weight(_text_:web in 4839) [ClassicSimilarity], result of:
          0.052019615 = score(doc=4839,freq=4.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.3059541 = fieldWeight in 4839, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=4839)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Exploration of heterogeneous data sources increases the value of information by allowing users to answer questions through exploration across multiple sources; Users can use information that has been posted across the Web to answer questions and learn about new domains. We have conducted research that lowers the interrogation time of faceted data, by combining related information from different sources. The work contributes methodologies in combining heterogenous sources, and how to deliver that data to a user interface scalably, with enough performance to support rapid interrogation of the knowledge by the user. The work also contributes how to combine linked data sources so that users can create faceted browsers that target the information facets of their needs. The work is grounded and proven in a number of experiments and test cases that study the contributions in domain research work.

Theme

Semantic Web

0.012708003 = product of:
  0.025416005 = sum of:
    0.025416005 = product of:
      0.05083201 = sum of:
        0.05083201 = weight(_text_:web in 3173) [ClassicSimilarity], result of:
          0.05083201 = score(doc=3173,freq=22.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.29896918 = fieldWeight in 3173, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.01953125 = fieldNorm(doc=3173)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The integration of metadata from distinct, heterogeneous data sources requires metadata interoperability, which is a qualitative property of metadata information objects that is not given by default. The technique of metadata mapping allows domain experts to establish metadata interoperability in a certain integration scenario. Mapping solutions, as a technical manifestation of this technique, are already available for the intensively studied domain of database system interoperability, but they rarely exist for the Web. If we consider the amount of steadily increasing structured metadata and corresponding metadata schemes on theWeb, we can observe a clear need for a mapping solution that can operate in aWeb-based environment. To achieve that, we first need to build its technical core, which is a mapping model that provides the language primitives to define mapping relationships. Existing SemanticWeb languages such as RDFS and OWL define some basic mapping elements (e.g., owl:equivalentProperty, owl:sameAs), but do not address the full spectrum of semantic and structural heterogeneities that can occur among distinct, incompatible metadata information objects. Furthermore, it is still unclear how to process defined mapping relationships during run-time in order to deliver metadata to the client in a uniform way. As the main contribution of this thesis, we present an abstract mapping model, which reflects the mapping problem on a generic level and provides the means for reconciling incompatible metadata. Instance transformation functions and URIs take a central role in that model. The former cover a broad spectrum of possible structural and semantic heterogeneities, while the latter bind the complete mapping model to the architecture of the Word Wide Web. On the concrete, language-specific level we present a binding of the abstract mapping model for the RDF Vocabulary Description Language (RDFS), which allows us to create mapping specifications among incompatible metadata schemes expressed in RDFS. The mapping model is embedded in a cyclic process that categorises the requirements a mapping solution should fulfil into four subsequent phases: mapping discovery, mapping representation, mapping execution, and mapping maintenance. In this thesis, we mainly focus on mapping representation and on the transformation of mapping specifications into executable SPARQL queries. For mapping discovery support, the model provides an interface for plugging-in schema and ontology matching algorithms. For mapping maintenance we introduce the concept of a simple, but effective mapping registry. Based on the mapping model, we propose aWeb-based mediator wrapper-architecture that allows domain experts to set up mediation endpoints that provide a uniform SPARQL query interface to a set of distributed metadata sources. The involved data sources are encapsulated by wrapper components that expose the contained metadata and the schema definitions on the Web and provide a SPARQL query interface to these metadata. In this thesis, we present the OAI2LOD Server, a wrapper component for integrating metadata that are accessible via the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). In a case study, we demonstrate how mappings can be created in aWeb environment and how our mediator wrapper architecture can easily be configured in order to integrate metadata from various heterogeneous data sources without the need to install any mapping solution or metadata integration solution in a local system environment.

Content

Die Integration von Metadaten aus unterschiedlichen, heterogenen Datenquellen erfordert Metadaten-Interoperabilität, eine Eigenschaft die nicht standardmäßig gegeben ist. Metadaten Mapping Verfahren ermöglichen es Domänenexperten Metadaten-Interoperabilität in einem bestimmten Integrationskontext herzustellen. Mapping Lösungen sollen dabei die notwendige Unterstützung bieten. Während diese für den etablierten Bereich interoperabler Datenbanken bereits existieren, ist dies für Web-Umgebungen nicht der Fall. Betrachtet man das Ausmaß ständig wachsender strukturierter Metadaten und Metadatenschemata im Web, so zeichnet sich ein Bedarf nach Web-basierten Mapping Lösungen ab. Den Kern einer solchen Lösung bildet ein Mappingmodell, das die zur Spezifikation von Mappings notwendigen Sprachkonstrukte definiert. Existierende Semantic Web Sprachen wie beispielsweise RDFS oder OWL bieten zwar grundlegende Mappingelemente (z.B.: owl:equivalentProperty, owl:sameAs), adressieren jedoch nicht das gesamte Sprektrum möglicher semantischer und struktureller Heterogenitäten, die zwischen unterschiedlichen, inkompatiblen Metadatenobjekten auftreten können. Außerdem fehlen technische Lösungsansätze zur Überführung zuvor definierter Mappings in ausfu¨hrbare Abfragen. Als zentraler wissenschaftlicher Beitrag dieser Dissertation, wird ein abstraktes Mappingmodell pr¨asentiert, welches das Mappingproblem auf generischer Ebene reflektiert und Lösungsansätze zum Abgleich inkompatibler Schemata bietet. Instanztransformationsfunktionen und URIs nehmen in diesem Modell eine zentrale Rolle ein. Erstere überbrücken ein breites Spektrum möglicher semantischer und struktureller Heterogenitäten, während letztere das Mappingmodell in die Architektur des World Wide Webs einbinden. Auf einer konkreten, sprachspezifischen Ebene wird die Anbindung des abstrakten Modells an die RDF Vocabulary Description Language (RDFS) präsentiert, wodurch ein Mapping zwischen unterschiedlichen, in RDFS ausgedrückten Metadatenschemata ermöglicht wird. Das Mappingmodell ist in einen zyklischen Mappingprozess eingebunden, der die Anforderungen an Mappinglösungen in vier aufeinanderfolgende Phasen kategorisiert: mapping discovery, mapping representation, mapping execution und mapping maintenance. Im Rahmen dieser Dissertation beschäftigen wir uns hauptsächlich mit der Representation-Phase sowie mit der Transformation von Mappingspezifikationen in ausführbare SPARQL-Abfragen. Zur Unterstützung der Discovery-Phase bietet das Mappingmodell eine Schnittstelle zur Einbindung von Schema- oder Ontologymatching-Algorithmen. Für die Maintenance-Phase präsentieren wir ein einfaches, aber seinen Zweck erfüllendes Mapping-Registry Konzept. Auf Basis des Mappingmodells stellen wir eine Web-basierte Mediator-Wrapper Architektur vor, die Domänenexperten die Möglichkeit bietet, SPARQL-Mediationsschnittstellen zu definieren. Die zu integrierenden Datenquellen müssen dafür durch Wrapper-Komponenen gekapselt werden, welche die enthaltenen Metadaten im Web exponieren und SPARQL-Zugriff ermöglichen. Als beipielhafte Wrapper Komponente präsentieren wir den OAI2LOD Server, mit dessen Hilfe Datenquellen eingebunden werden können, die ihre Metadaten über das Open Archives Initative Protocol for Metadata Harvesting (OAI-PMH) exponieren. Im Rahmen einer Fallstudie zeigen wir, wie Mappings in Web-Umgebungen erstellt werden können und wie unsere Mediator-Wrapper Architektur nach wenigen, einfachen Konfigurationsschritten Metadaten aus unterschiedlichen, heterogenen Datenquellen integrieren kann, ohne dass dadurch die Notwendigkeit entsteht, eine Mapping Lösung in einer lokalen Systemumgebung zu installieren.

0.010728499 = product of:
  0.021456998 = sum of:
    0.021456998 = product of:
      0.042913996 = sum of:
        0.042913996 = weight(_text_:web in 1653) [ClassicSimilarity], result of:
          0.042913996 = score(doc=1653,freq=2.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.25239927 = fieldWeight in 1653, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1653)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The thesis deals with the evaluation of mathematics information retrieval (IR). It gives an overview of the history of regular IR evaluation, initiatives that are engaged in this field of research as well as most common methods and measures used for evaluation. The findings are applied to the specifics of mathematics retrieval. This thesis also summarizes the state-of-the-art of MIaS math search system, which is already being used in an international web portal. Latest developments aiming towards the second version of the system are described. In addition to participating in the international evaluation conference and workshop, MIaS is tested for effectiveness and efficiency in this work. Measured performance indicators are evaluated and future work is suggested accordingly.

0.010728499 = product of:
  0.021456998 = sum of:
    0.021456998 = product of:
      0.042913996 = sum of:
        0.042913996 = weight(_text_:web in 5496) [ClassicSimilarity], result of:
          0.042913996 = score(doc=5496,freq=2.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.25239927 = fieldWeight in 5496, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5496)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

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.

0.009195855 = product of:
  0.01839171 = sum of:
    0.01839171 = product of:
      0.03678342 = sum of:
        0.03678342 = weight(_text_:web in 3829) [ClassicSimilarity], result of:
          0.03678342 = score(doc=3829,freq=2.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.21634221 = fieldWeight in 3829, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.046875 = fieldNorm(doc=3829)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Theme

Semantic Web

0.008823298 = product of:
  0.017646596 = sum of:
    0.017646596 = product of:
      0.03529319 = sum of:
        0.03529319 = weight(_text_:22 in 595) [ClassicSimilarity], result of:
          0.03529319 = score(doc=595,freq=2.0), product of:
            0.18244034 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052098576 = queryNorm
            0.19345059 = fieldWeight in 595, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=595)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

3. 2.2013 18:00:22

0.008823298 = product of:
  0.017646596 = sum of:
    0.017646596 = product of:
      0.03529319 = sum of:
        0.03529319 = weight(_text_:22 in 642) [ClassicSimilarity], result of:
          0.03529319 = score(doc=642,freq=2.0), product of:
            0.18244034 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052098576 = queryNorm
            0.19345059 = fieldWeight in 642, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=642)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 7.2022 12:16:58

0.008669936 = product of:
  0.017339872 = sum of:
    0.017339872 = product of:
      0.034679744 = sum of:
        0.034679744 = weight(_text_:web in 4746) [ClassicSimilarity], result of:
          0.034679744 = score(doc=4746,freq=4.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.2039694 = fieldWeight in 4746, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.03125 = fieldNorm(doc=4746)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Many real-world domains can be represented as large node-link graphs: backbone Internet routers connect with 70,000 other hosts, mid-sized Web servers handle between 20,000 and 200,000 hyperlinked documents, and dictionaries contain millions of words defined in terms of each other. Computational manipulation of such large graphs is common, but previous tools for graph visualization have been limited to datasets of a few thousand nodes. Visual depictions of graphs and networks are external representations that exploit human visual processing to reduce the cognitive load of many tasks that require understanding of global or local structure. We assert that the two key advantages of computer-based systems for information visualization over traditional paper-based visual exposition are interactivity and scalability. We also argue that designing visualization software by taking the characteristics of a target user's task domain into account leads to systems that are more effective and scale to larger datasets than previous work. This thesis contains a detailed analysis of three specialized systems for the interactive exploration of large graphs, relating the intended tasks to the spatial layout and visual encoding choices. We present two novel algorithms for specialized layout and drawing that use quite different visual metaphors. The H3 system for visualizing the hyperlink structures of web sites scales to datasets of over 100,000 nodes by using a carefully chosen spanning tree as the layout backbone, 3D hyperbolic geometry for a Focus+Context view, and provides a fluid interactive experience through guaranteed frame rate drawing. The Constellation system features a highly specialized 2D layout intended to spatially encode domain-specific information for computational linguists checking the plausibility of a large semantic network created from dictionaries. The Planet Multicast system for displaying the tunnel topology of the Internet's multicast backbone provides a literal 3D geographic layout of arcs on a globe to help MBone maintainers find misconfigured long-distance tunnels. Each of these three systems provides a very different view of the graph structure, and we evaluate their efficacy for the intended task. We generalize these findings in our analysis of the importance of interactivity and specialization for graph visualization systems that are effective and scalable.

0.008669936 = product of:
  0.017339872 = sum of:
    0.017339872 = product of:
      0.034679744 = sum of:
        0.034679744 = weight(_text_:web in 2191) [ClassicSimilarity], result of:
          0.034679744 = score(doc=2191,freq=4.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.2039694 = fieldWeight in 2191, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.03125 = fieldNorm(doc=2191)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In the Internet information space, advanced information retrieval (IR) methods and automatic text processing are used in conjunction with traditional knowledge organization systems (KOS). New information technology provides a platform for better KOS publishing, exploitation and sharing both for human and machine use. Networked KOS services are now being planned and developed as powerful tools for resource discovery. They will enable automatic contextualisation, interpretation and query matching to different indexing languages. The Semantic Web promises to be an environment in which the quality of semantic relationships in bibliographic classification systems can be fully exploited. Their use in the networked environment is, however, limited by the fact that they are not prepared or made available for advanced machine processing. The UDC was chosen for this research because of its widespread use and its long-term presence in online information retrieval systems. It was also the first system to be used for the automatic classification of Internet resources, and the first to be made available as a classification tool on the Web. The objective of this research is to establish the advantages of using UDC for information retrieval in a networked environment, to highlight the problems of automation and classification exchange, and to offer possible solutions. The first research question was is there enough evidence of the use of classification on the Internet to justify further development with this particular environment in mind? The second question is what are the automation requirements for the full exploitation of UDC and its exchange? The third question is which areas are in need of improvement and what specific recommendations can be made for implementing the UDC in a networked environment? A summary of changes required in the management and development of the UDC to facilitate its full adaptation for future use is drawn from this analysis.

0.007663213 = product of:
  0.015326426 = sum of:
    0.015326426 = product of:
      0.030652853 = sum of:
        0.030652853 = weight(_text_:web in 599) [ClassicSimilarity], result of:
          0.030652853 = score(doc=599,freq=2.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.18028519 = fieldWeight in 599, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.0390625 = fieldNorm(doc=599)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Linked Data hat sich im Laufe der letzten Jahre zu einem vorherrschenden Thema der Bibliothekswissenschaft entwickelt. Als ein Standard für Erfassung und Austausch von Daten, bestehen zahlreiche Berührungspunkte mit traditionellen bibliothekarischen Techniken. Diese Arbeit stellt in einem ersten Teil die grundlegenden Technologien dieses neuen Paradigmas vor, um sodann deren Anwendung auf bibliothekarische Daten zu untersuchen. Den zentralen Prinzipien der Linked Data Initiative folgend, werden dabei die Adressierung von Entitäten durch URIs, die Anwendung des RDF Datenmodells und die Verknüpfung von heterogenen Datenbeständen näher beleuchtet. Den dabei zu Tage tretenden Herausforderungen der Sicherstellung von qualitativ hochwertiger Information, der permanenten Adressierung von Inhalten im World Wide Web sowie Problemen der Interoperabilität von Metadatenstandards wird dabei besondere Aufmerksamkeit geschenkt. Der letzte Teil der Arbeit skizziert ein Programm, welches eine mögliche Erweiterung der Suchmaschine des österreichischen Bibliothekenverbundes darstellt. Dessen prototypische Umsetzung erlaubt eine realistische Einschätzung der derzeitigen Möglichkeiten von Linked Data und unterstreicht viele der vorher theoretisch erarbeiteten Themengebiete. Es zeigt sich, dass für den voll produktiven Einsatz von Linked Data noch viele Hürden zu überwinden sind. Insbesondere befinden sich viele Projekte derzeit noch in einem frühen Reifegrad. Andererseits sind die Möglichkeiten, die aus einem konsequenten Einsatz von RDF resultieren würden, vielversprechend. RDF qualifiziert sich somit als Kandidat für den Ersatz von auslaufenden bibliographischen Datenformaten wie MAB oder MARC.

0.007058638 = product of:
  0.014117276 = sum of:
    0.014117276 = product of:
      0.028234553 = sum of:
        0.028234553 = weight(_text_:22 in 4399) [ClassicSimilarity], result of:
          0.028234553 = score(doc=4399,freq=2.0), product of:
            0.18244034 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.052098576 = queryNorm
            0.15476047 = fieldWeight in 4399, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=4399)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

20. 1.2015 18:30:22

0.0061305705 = product of:
  0.012261141 = sum of:
    0.012261141 = product of:
      0.024522282 = sum of:
        0.024522282 = weight(_text_:web in 4659) [ClassicSimilarity], result of:
          0.024522282 = score(doc=4659,freq=2.0), product of:
            0.17002425 = queryWeight, product of:
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.052098576 = queryNorm
            0.14422815 = fieldWeight in 4659, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.2635105 = idf(docFreq=4597, maxDocs=44218)
              0.03125 = fieldNorm(doc=4659)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This dissertation studies ontology mapping: the problem of finding semantic correspondences between similar elements of different ontologies. In the dissertation, elements denote classes or properties of ontologies. The goal of this research is to use ontology mapping to make heterogeneous information more accessible. The World Wide Web (WWW) now is widely used as a universal medium for information exchange. Semantic interoperability among different information systems in the WWW is limited due to information heterogeneity, and the non semantic nature of HTML and URLs. Ontologies have been suggested as a way to solve the problem of information heterogeneity by providing formal, explicit definitions of data and reasoning ability over related concepts. Given that no universal ontology exists for the WWW, work has focused on finding semantic correspondences between similar elements of different ontologies, i.e., ontology mapping. Ontology mapping can be done either by hand or using automated tools. Manual mapping becomes impractical as the size and complexity of ontologies increases. Full or semi-automated mapping approaches have been examined by several research studies. Previous full or semiautomated mapping approaches include analyzing linguistic information of elements in ontologies, treating ontologies as structural graphs, applying heuristic rules and machine learning techniques, and using probabilistic and reasoning methods etc. In this paper, two generic ontology mapping approaches are proposed. One is the PRIOR+ approach, which utilizes both information retrieval and artificial intelligence techniques in the context of ontology mapping. The other is the non-instance learning based approach, which experimentally explores machine learning algorithms to solve ontology mapping problem without requesting any instance. The results of the PRIOR+ on different tests at OAEI ontology matching campaign 2007 are encouraging. The non-instance learning based approach has shown potential for solving ontology mapping problem on OAEI benchmark tests.