Search (140 results, page 1 of 7)

  • × theme_ss:"Wissensrepräsentation"
  • × type_ss:"el"
  1. Halpin, H.; Hayes, P.J.: When owl:sameAs isn't the same : an analysis of identity links on the Semantic Web (2010) 0.07
    0.071265064 = product of:
      0.1187751 = sum of:
        0.014111101 = product of:
          0.0705555 = sum of:
            0.0705555 = weight(_text_:problem in 4834) [ClassicSimilarity], result of:
              0.0705555 = score(doc=4834,freq=4.0), product of:
                0.17731056 = queryWeight, product of:
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.04177434 = queryNorm
                0.39792046 = fieldWeight in 4834, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.046875 = fieldNorm(doc=4834)
          0.2 = coord(1/5)
        0.084348574 = weight(_text_:philosophy in 4834) [ClassicSimilarity], result of:
          0.084348574 = score(doc=4834,freq=2.0), product of:
            0.23055021 = queryWeight, product of:
              5.5189433 = idf(docFreq=481, maxDocs=44218)
              0.04177434 = queryNorm
            0.36585772 = fieldWeight in 4834, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.5189433 = idf(docFreq=481, maxDocs=44218)
              0.046875 = fieldNorm(doc=4834)
        0.02031542 = weight(_text_:of in 4834) [ClassicSimilarity], result of:
          0.02031542 = score(doc=4834,freq=18.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.3109903 = fieldWeight in 4834, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=4834)
      0.6 = coord(3/5)
    
    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
  2. Putkey, T.: Using SKOS to express faceted classification on the Semantic Web (2011) 0.05
    0.046018858 = product of:
      0.076698095 = sum of:
        0.0094074 = product of:
          0.047036998 = sum of:
            0.047036998 = weight(_text_:problem in 311) [ClassicSimilarity], result of:
              0.047036998 = score(doc=311,freq=4.0), product of:
                0.17731056 = queryWeight, product of:
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.04177434 = queryNorm
                0.2652803 = fieldWeight in 311, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.03125 = fieldNorm(doc=311)
          0.2 = coord(1/5)
        0.05623238 = weight(_text_:philosophy in 311) [ClassicSimilarity], result of:
          0.05623238 = score(doc=311,freq=2.0), product of:
            0.23055021 = queryWeight, product of:
              5.5189433 = idf(docFreq=481, maxDocs=44218)
              0.04177434 = queryNorm
            0.24390514 = fieldWeight in 311, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.5189433 = idf(docFreq=481, maxDocs=44218)
              0.03125 = fieldNorm(doc=311)
        0.011058315 = weight(_text_:of in 311) [ClassicSimilarity], result of:
          0.011058315 = score(doc=311,freq=12.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.16928169 = fieldWeight in 311, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03125 = fieldNorm(doc=311)
      0.6 = coord(3/5)
    
    Abstract
    This paper looks at Simple Knowledge Organization System (SKOS) to investigate how a faceted classification can be expressed in RDF and shared on the Semantic Web. Statement of the Problem Faceted classification outlines facets as well as subfacets and facet values. Hierarchical relationships and associative relationships are established in a faceted classification. RDF is used to describe how a specific URI has a relationship to a facet value. Not only does RDF decompose "information into pieces," but by incorporating facet values RDF also given the URI the hierarchical and associative relationships expressed in the faceted classification. Combining faceted classification and RDF creates more knowledge than if the two stood alone. An application understands the subjectpredicate-object relationship in RDF and can display hierarchical and associative relationships based on the object (facet) value. This paper continues to investigate if the above idea is indeed useful, used, and applicable. If so, how can a faceted classification be expressed in RDF? What would this expression look like? Literature Review This paper used the same articles as the paper A Survey of Faceted Classification: History, Uses, Drawbacks and the Semantic Web (Putkey, 2010). In that paper, appropriate resources were discovered by searching in various databases for "faceted classification" and "faceted search," either in the descriptor or title fields. Citations were also followed to find more articles as well as searching the Internet for the same terms. To retrieve the documents about RDF, searches combined "faceted classification" and "RDF, " looking for these words in either the descriptor or title.
    Methodology Based on information from research papers, more research was done on SKOS and examples of SKOS and shared faceted classifications in the Semantic Web and about SKOS and how to express SKOS in RDF/XML. Once confident with these ideas, the author used a faceted taxonomy created in a Vocabulary Design class and encoded it using SKOS. Instead of writing RDF in a program such as Notepad, a thesaurus tool was used to create the taxonomy according to SKOS standards and then export the thesaurus in RDF/XML format. These processes and tools are then analyzed. Results The initial statement of the problem was simply an extension of the survey paper done earlier in this class. To continue on with the research, more research was done into SKOS - a standard for expressing thesauri, taxonomies and faceted classifications so they can be shared on the semantic web.
    Source
    Library philosophy and practice, 2011
  3. Guizzardi, G.; Guarino, N.: Semantics, ontology and explanation (2023) 0.04
    0.041400857 = product of:
      0.10350214 = sum of:
        0.084348574 = weight(_text_:philosophy in 976) [ClassicSimilarity], result of:
          0.084348574 = score(doc=976,freq=2.0), product of:
            0.23055021 = queryWeight, product of:
              5.5189433 = idf(docFreq=481, maxDocs=44218)
              0.04177434 = queryNorm
            0.36585772 = fieldWeight in 976, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.5189433 = idf(docFreq=481, maxDocs=44218)
              0.046875 = fieldNorm(doc=976)
        0.019153563 = weight(_text_:of in 976) [ClassicSimilarity], result of:
          0.019153563 = score(doc=976,freq=16.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.2932045 = fieldWeight in 976, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=976)
      0.4 = coord(2/5)
    
    Abstract
    The terms 'semantics' and 'ontology' are increasingly appearing together with 'explanation', not only in the scientific literature, but also in organizational communication. However, all of these terms are also being significantly overloaded. In this paper, we discuss their strong relation under particular interpretations. Specifically, we discuss a notion of explanation termed ontological unpacking, which aims at explaining symbolic domain descriptions (conceptual models, knowledge graphs, logical specifications) by revealing their ontological commitment in terms of their assumed truthmakers, i.e., the entities in one's ontology that make the propositions in those descriptions true. To illustrate this idea, we employ an ontological theory of relations to explain (by revealing the hidden semantics of) a very simple symbolic model encoded in the standard modeling language UML. We also discuss the essential role played by ontology-driven conceptual models (resulting from this form of explanation processes) in properly supporting semantic interoperability tasks. Finally, we discuss the relation between ontological unpacking and other forms of explanation in philosophy and science, as well as in the area of Artificial Intelligence.
  4. Jansen, B.; Browne, G.M.: Navigating information spaces : index / mind map / topic map? (2021) 0.04
    0.035024326 = product of:
      0.08756082 = sum of:
        0.01563882 = weight(_text_:of in 436) [ClassicSimilarity], result of:
          0.01563882 = score(doc=436,freq=6.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.23940048 = fieldWeight in 436, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0625 = fieldNorm(doc=436)
        0.071922 = product of:
          0.143844 = sum of:
            0.143844 = weight(_text_:mind in 436) [ClassicSimilarity], result of:
              0.143844 = score(doc=436,freq=2.0), product of:
                0.2607373 = queryWeight, product of:
                  6.241566 = idf(docFreq=233, maxDocs=44218)
                  0.04177434 = queryNorm
                0.5516817 = fieldWeight in 436, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  6.241566 = idf(docFreq=233, maxDocs=44218)
                  0.0625 = fieldNorm(doc=436)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    This paper discusses the use of wiki technology to provide a navigation structure for a collection of newspaper clippings. We overview the architecture of the wiki, discuss the navigation structure and pose the question: is the navigation structure an index, and if so, what type, or is it just a linkage structure or topic map. Does such a distinction really matter? Are these definitions in reality function based?
  5. Styltsvig, H.B.: Ontology-based information retrieval (2006) 0.03
    0.031522032 = product of:
      0.078805074 = sum of:
        0.05623238 = weight(_text_:philosophy in 1154) [ClassicSimilarity], result of:
          0.05623238 = score(doc=1154,freq=2.0), product of:
            0.23055021 = queryWeight, product of:
              5.5189433 = idf(docFreq=481, maxDocs=44218)
              0.04177434 = queryNorm
            0.24390514 = fieldWeight in 1154, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.5189433 = idf(docFreq=481, maxDocs=44218)
              0.03125 = fieldNorm(doc=1154)
        0.022572692 = weight(_text_:of in 1154) [ClassicSimilarity], result of:
          0.022572692 = score(doc=1154,freq=50.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.34554482 = fieldWeight in 1154, product of:
              7.071068 = tf(freq=50.0), with freq of:
                50.0 = termFreq=50.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03125 = fieldNorm(doc=1154)
      0.4 = coord(2/5)
    
    Abstract
    In this thesis, we will present methods for introducing ontologies in information retrieval. The main hypothesis is that the inclusion of conceptual knowledge such as ontologies in the information retrieval process can contribute to the solution of major problems currently found in information retrieval. This utilization of ontologies has a number of challenges. Our focus is on the use of similarity measures derived from the knowledge about relations between concepts in ontologies, the recognition of semantic information in texts and the mapping of this knowledge into the ontologies in use, as well as how to fuse together the ideas of ontological similarity and ontological indexing into a realistic information retrieval scenario. To achieve the recognition of semantic knowledge in a text, shallow natural language processing is used during indexing that reveals knowledge to the level of noun phrases. Furthermore, we briefly cover the identification of semantic relations inside and between noun phrases, as well as discuss which kind of problems are caused by an increase in compoundness with respect to the structure of concepts in the evaluation of queries. Measuring similarity between concepts based on distances in the structure of the ontology is discussed. In addition, a shared nodes measure is introduced and, based on a set of intuitive similarity properties, compared to a number of different measures. In this comparison the shared nodes measure appears to be superior, though more computationally complex. Some of the major problems of shared nodes which relate to the way relations differ with respect to the degree they bring the concepts they connect closer are discussed. A generalized measure called weighted shared nodes is introduced to deal with these problems. Finally, the utilization of concept similarity in query evaluation is discussed. A semantic expansion approach that incorporates concept similarity is introduced and a generalized fuzzy set retrieval model that applies expansion during query evaluation is presented. While not commonly used in present information retrieval systems, it appears that the fuzzy set model comprises the flexibility needed when generalizing to an ontology-based retrieval model and, with the introduction of a hierarchical fuzzy aggregation principle, compound concepts can be handled in a straightforward and natural manner.
    Content
    A dissertation Presented to the Faculties of Roskilde University in Partial Fulfillment of the Requirement for the Degree of Doctor of Philosophy. Vgl. unter: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.117.987 oder http://coitweb.uncc.edu/~ras/RS/Onto-Retrieval.pdf.
  6. Kottmann, N.; Studer, T.: Improving semantic query answering (2006) 0.02
    0.01786652 = product of:
      0.044666298 = sum of:
        0.026608143 = product of:
          0.13304071 = sum of:
            0.13304071 = weight(_text_:problem in 3979) [ClassicSimilarity], result of:
              0.13304071 = score(doc=3979,freq=8.0), product of:
                0.17731056 = queryWeight, product of:
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.04177434 = queryNorm
                0.750326 = fieldWeight in 3979, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.0625 = fieldNorm(doc=3979)
          0.2 = coord(1/5)
        0.018058153 = weight(_text_:of in 3979) [ClassicSimilarity], result of:
          0.018058153 = score(doc=3979,freq=8.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.27643585 = fieldWeight in 3979, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0625 = fieldNorm(doc=3979)
      0.4 = coord(2/5)
    
    Abstract
    The retrieval problem is one of the main reasoning tasks for knowledge base systems. Given a knowledge base K and a concept C, the retrieval problem consists of finding all individuals a for which K logically entails C(a). We present an approach to answer retrieval queries over (a restriction of) OWL ontologies. Our solution is based on reducing the retrieval problem to a problem of evaluating an SQL query over a database constructed from the original knowledge base. We provide complete answers to retrieval problems. Still, our system performs very well as is shown by a standard benchmark.
  7. OWL Web Ontology Language Test Cases (2004) 0.02
    0.016279016 = product of:
      0.040697537 = sum of:
        0.018058153 = weight(_text_:of in 4685) [ClassicSimilarity], result of:
          0.018058153 = score(doc=4685,freq=8.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.27643585 = fieldWeight in 4685, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0625 = fieldNorm(doc=4685)
        0.022639386 = product of:
          0.045278773 = sum of:
            0.045278773 = weight(_text_:22 in 4685) [ClassicSimilarity], result of:
              0.045278773 = score(doc=4685,freq=2.0), product of:
                0.14628662 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.04177434 = queryNorm
                0.30952093 = fieldWeight in 4685, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0625 = fieldNorm(doc=4685)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    This document contains and presents test cases for the Web Ontology Language (OWL) approved by the Web Ontology Working Group. Many of the test cases illustrate the correct usage of the Web Ontology Language (OWL), and the formal meaning of its constructs. Other test cases illustrate the resolution of issues considered by the Working Group. Conformance for OWL documents and OWL document checkers is specified.
    Date
    14. 8.2011 13:33:22
  8. Hollink, L.; Assem, M. van: Estimating the relevance of search results in the Culture-Web : a study of semantic distance measures (2010) 0.02
    0.015775634 = product of:
      0.039439082 = sum of:
        0.022459546 = weight(_text_:of in 4649) [ClassicSimilarity], result of:
          0.022459546 = score(doc=4649,freq=22.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.34381276 = fieldWeight in 4649, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=4649)
        0.016979538 = product of:
          0.033959076 = sum of:
            0.033959076 = weight(_text_:22 in 4649) [ClassicSimilarity], result of:
              0.033959076 = score(doc=4649,freq=2.0), product of:
                0.14628662 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.04177434 = queryNorm
                0.23214069 = fieldWeight in 4649, 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=4649)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    More and more cultural heritage institutions publish their collections, vocabularies and metadata on the Web. The resulting Web of linked cultural data opens up exciting new possibilities for searching and browsing through these cultural heritage collections. We report on ongoing work in which we investigate the estimation of relevance in this Web of Culture. We study existing measures of semantic distance and how they apply to two use cases. The use cases relate to the structured, multilingual and multimodal nature of the Culture Web. We distinguish between measures using the Web, such as Google distance and PMI, and measures using the Linked Data Web, i.e. the semantic structure of metadata vocabularies. We perform a small study in which we compare these semantic distance measures to human judgements of relevance. Although it is too early to draw any definitive conclusions, the study provides new insights into the applicability of semantic distance measures to the Web of Culture, and clear starting points for further research.
    Date
    26.12.2011 13:40:22
  9. Priss, U.: Description logic and faceted knowledge representation (1999) 0.02
    0.015775634 = product of:
      0.039439082 = sum of:
        0.022459546 = weight(_text_:of in 2655) [ClassicSimilarity], result of:
          0.022459546 = score(doc=2655,freq=22.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.34381276 = fieldWeight in 2655, product of:
              4.690416 = tf(freq=22.0), with freq of:
                22.0 = termFreq=22.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=2655)
        0.016979538 = product of:
          0.033959076 = sum of:
            0.033959076 = weight(_text_:22 in 2655) [ClassicSimilarity], result of:
              0.033959076 = score(doc=2655,freq=2.0), product of:
                0.14628662 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.04177434 = queryNorm
                0.23214069 = fieldWeight in 2655, 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=2655)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    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
  10. Gödert, W.: ¬An ontology-based model for indexing and retrieval (2013) 0.02
    0.015536862 = product of:
      0.038842157 = sum of:
        0.0133040715 = product of:
          0.066520356 = sum of:
            0.066520356 = weight(_text_:problem in 1510) [ClassicSimilarity], result of:
              0.066520356 = score(doc=1510,freq=2.0), product of:
                0.17731056 = queryWeight, product of:
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.04177434 = queryNorm
                0.375163 = fieldWeight in 1510, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.0625 = fieldNorm(doc=1510)
          0.2 = coord(1/5)
        0.025538085 = weight(_text_:of in 1510) [ClassicSimilarity], result of:
          0.025538085 = score(doc=1510,freq=16.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.39093933 = fieldWeight in 1510, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0625 = fieldNorm(doc=1510)
      0.4 = coord(2/5)
    
    Abstract
    Starting from an unsolved problem of information retrieval this paper presents an ontology-based model for indexing and retrieval. The model combines the methods and experiences of cognitive-to-interpret indexing languages with the strengths and possibilities of formal knowledge representation. The core component of the model uses inferences along the paths of typed relations between the entities of a knowledge representation for enabling the determination of hit quantities in the context of retrieval processes. The entities are arranged in aspect-oriented facets to ensure a consistent hierarchical structure. The possible consequences for indexing and retrieval are discussed.
  11. Definition of the CIDOC Conceptual Reference Model (2003) 0.02
    0.015357548 = product of:
      0.03839387 = sum of:
        0.021414334 = weight(_text_:of in 1652) [ClassicSimilarity], result of:
          0.021414334 = score(doc=1652,freq=20.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.32781258 = fieldWeight in 1652, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=1652)
        0.016979538 = product of:
          0.033959076 = sum of:
            0.033959076 = weight(_text_:22 in 1652) [ClassicSimilarity], result of:
              0.033959076 = score(doc=1652,freq=2.0), product of:
                0.14628662 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.04177434 = queryNorm
                0.23214069 = fieldWeight in 1652, 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=1652)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    This document is the formal definition of the CIDOC Conceptual Reference Model ("CRM"), a formal ontology intended to facilitate the integration, mediation and interchange of heterogeneous cultural heritage information. The CRM is the culmination of more than a decade of standards development work by the International Committee for Documentation (CIDOC) of the International Council of Museums (ICOM). Work on the CRM itself began in 1996 under the auspices of the ICOM-CIDOC Documentation Standards Working Group. Since 2000, development of the CRM has been officially delegated by ICOM-CIDOC to the CIDOC CRM Special Interest Group, which collaborates with the ISO working group ISO/TC46/SC4/WG9 to bring the CRM to the form and status of an International Standard.
    Date
    6. 8.2010 14:22:28
  12. Priss, U.: Faceted knowledge representation (1999) 0.01
    0.014990156 = product of:
      0.03747539 = sum of:
        0.017665926 = weight(_text_:of in 2654) [ClassicSimilarity], result of:
          0.017665926 = score(doc=2654,freq=10.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.2704316 = fieldWeight in 2654, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2654)
        0.019809462 = product of:
          0.039618924 = sum of:
            0.039618924 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
              0.039618924 = score(doc=2654,freq=2.0), product of:
                0.14628662 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.04177434 = queryNorm
                0.2708308 = fieldWeight in 2654, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=2654)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    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
  13. Aitken, S.; Reid, S.: Evaluation of an ontology-based information retrieval tool (2000) 0.01
    0.014877105 = product of:
      0.037192762 = sum of:
        0.0133040715 = product of:
          0.066520356 = sum of:
            0.066520356 = weight(_text_:problem in 2862) [ClassicSimilarity], result of:
              0.066520356 = score(doc=2862,freq=2.0), product of:
                0.17731056 = queryWeight, product of:
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.04177434 = queryNorm
                0.375163 = fieldWeight in 2862, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.0625 = fieldNorm(doc=2862)
          0.2 = coord(1/5)
        0.023888692 = weight(_text_:of in 2862) [ClassicSimilarity], result of:
          0.023888692 = score(doc=2862,freq=14.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.36569026 = fieldWeight in 2862, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0625 = fieldNorm(doc=2862)
      0.4 = coord(2/5)
    
    Abstract
    This paper evaluates the use of an explicit domain ontology in an information retrieval tool. The evaluation compares the performance of ontology-enhanced retrieval with keyword retrieval for a fixed set of queries across several data sets. The robustness of the IR approach is assessed by comparing the performance of the tool on the original data set with that on previously unseen data.
    Content
    Beitrag für: Workshop on the Applications of Ontologies and Problem-Solving Methods, (eds) Gómez-Pérez, A., Benjamins, V.R., Guarino, N., and Uschold, M. European Conference on Artificial Intelligence 2000, Berlin.
  14. Mayfield, J.; Finin, T.: Information retrieval on the Semantic Web : integrating inference and retrieval 0.01
    0.013397372 = product of:
      0.03349343 = sum of:
        0.013683967 = weight(_text_:of in 4330) [ClassicSimilarity], result of:
          0.013683967 = score(doc=4330,freq=6.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.20947541 = fieldWeight in 4330, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4330)
        0.019809462 = product of:
          0.039618924 = sum of:
            0.039618924 = weight(_text_:22 in 4330) [ClassicSimilarity], result of:
              0.039618924 = score(doc=4330,freq=2.0), product of:
                0.14628662 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.04177434 = queryNorm
                0.2708308 = fieldWeight in 4330, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=4330)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    One vision of the Semantic Web is that it will be much like the Web we know today, except that documents will be enriched by annotations in machine understandable markup. These annotations will provide metadata about the documents as well as machine interpretable statements capturing some of the meaning of document content. We discuss how the information retrieval paradigm might be recast in such an environment. We suggest that retrieval can be tightly bound to inference. Doing so makes today's Web search engines useful to Semantic Web inference engines, and causes improvements in either retrieval or inference to lead directly to improvements in the other.
    Date
    12. 2.2011 17:35:22
  15. Beppler, F.D.; Fonseca, F.T.; Pacheco, R.C.S.: Hermeneus: an architecture for an ontology-enabled information retrieval (2008) 0.01
    0.012848704 = product of:
      0.03212176 = sum of:
        0.015142222 = weight(_text_:of in 3261) [ClassicSimilarity], result of:
          0.015142222 = score(doc=3261,freq=10.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.23179851 = fieldWeight in 3261, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=3261)
        0.016979538 = product of:
          0.033959076 = sum of:
            0.033959076 = weight(_text_:22 in 3261) [ClassicSimilarity], result of:
              0.033959076 = score(doc=3261,freq=2.0), product of:
                0.14628662 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.04177434 = queryNorm
                0.23214069 = fieldWeight in 3261, 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=3261)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    Ontologies improve IR systems regarding its retrieval and presentation of information, which make the task of finding information more effective, efficient, and interactive. In this paper we argue that ontologies also greatly improve the engineering of such systems. We created a framework that uses ontology to drive the process of engineering an IR system. We developed a prototype that shows how a domain specialist without knowledge in the IR field can build an IR system with interactive components. The resulting system provides support for users not only to find their information needs but also to extend their state of knowledge. This way, our approach to ontology-enabled information retrieval addresses both the engineering aspect described here and also the usability aspect described elsewhere.
    Date
    28.11.2016 12:43:22
  16. Hauff-Hartig, S.: Wissensrepräsentation durch RDF: Drei angewandte Forschungsbeispiele : Bitte recht vielfältig: Wie Wissensgraphen, Disco und FaBiO Struktur in Mangas und die Humanities bringen (2021) 0.01
    0.012667385 = product of:
      0.03166846 = sum of:
        0.009029076 = weight(_text_:of in 318) [ClassicSimilarity], result of:
          0.009029076 = score(doc=318,freq=2.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.13821793 = fieldWeight in 318, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0625 = fieldNorm(doc=318)
        0.022639386 = product of:
          0.045278773 = sum of:
            0.045278773 = weight(_text_:22 in 318) [ClassicSimilarity], result of:
              0.045278773 = score(doc=318,freq=2.0), product of:
                0.14628662 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.04177434 = queryNorm
                0.30952093 = fieldWeight in 318, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0625 = fieldNorm(doc=318)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    In der Session "Knowledge Representation" auf der ISI 2021 wurden unter der Moderation von Jürgen Reischer (Uni Regensburg) drei Projekte vorgestellt, in denen Knowledge Representation mit RDF umgesetzt wird. Die Domänen sind erfreulich unterschiedlich, die gemeinsame Klammer indes ist die Absicht, den Zugang zu Forschungsdaten zu verbessern: - Japanese Visual Media Graph - Taxonomy of Digital Research Activities in the Humanities - Forschungsdaten im konzeptuellen Modell von FRBR
    Date
    22. 5.2021 12:43:05
  17. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005) 0.01
    0.01258021 = product of:
      0.031450525 = sum of:
        0.011641062 = product of:
          0.05820531 = sum of:
            0.05820531 = weight(_text_:problem in 4324) [ClassicSimilarity], result of:
              0.05820531 = score(doc=4324,freq=2.0), product of:
                0.17731056 = queryWeight, product of:
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.04177434 = queryNorm
                0.3282676 = fieldWeight in 4324, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=4324)
          0.2 = coord(1/5)
        0.019809462 = product of:
          0.039618924 = sum of:
            0.039618924 = weight(_text_:22 in 4324) [ClassicSimilarity], result of:
              0.039618924 = score(doc=4324,freq=2.0), product of:
                0.14628662 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.04177434 = queryNorm
                0.2708308 = fieldWeight in 4324, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=4324)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    Ontologien werden eingesetzt, um durch semantische Fundierung insbesondere für das Dokumentenretrieval eine grundlegend bessere Basis zu haben, als dies gegenwärtiger Stand der Technik ist. Vorgestellt wird eine an der FH Darmstadt entwickelte und eingesetzte Ontologie, die den Gegenstandsbereich Hochschule sowohl breit abdecken und gleichzeitig differenziert semantisch beschreiben soll. Das Problem der semantischen Suche besteht nun darin, dass sie für Informationssuchende so einfach wie bei gängigen Suchmaschinen zu nutzen sein soll, und gleichzeitig auf der Grundlage des aufwendigen Informationsmodells hochwertige Ergebnisse liefern muss. Es wird beschrieben, welche Möglichkeiten die verwendete Software K-Infinity bereitstellt und mit welchem Konzept diese Möglichkeiten für eine semantische Suche nach Dokumenten und anderen Informationseinheiten (Personen, Veranstaltungen, Projekte etc.) eingesetzt werden.
    Date
    11. 2.2011 18:22:25
  18. Bittner, T.; Donnelly, M.; Winter, S.: Ontology and semantic interoperability (2006) 0.01
    0.012209262 = product of:
      0.030523153 = sum of:
        0.013543615 = weight(_text_:of in 4820) [ClassicSimilarity], result of:
          0.013543615 = score(doc=4820,freq=8.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.20732689 = fieldWeight in 4820, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.046875 = fieldNorm(doc=4820)
        0.016979538 = product of:
          0.033959076 = sum of:
            0.033959076 = weight(_text_:22 in 4820) [ClassicSimilarity], result of:
              0.033959076 = score(doc=4820,freq=2.0), product of:
                0.14628662 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.04177434 = queryNorm
                0.23214069 = fieldWeight in 4820, 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=4820)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    One of the major problems facing systems for Computer Aided Design (CAD), Architecture Engineering and Construction (AEC) and Geographic Information Systems (GIS) applications today is the lack of interoperability among the various systems. When integrating software applications, substantial di culties can arise in translating information from one application to the other. In this paper, we focus on semantic di culties that arise in software integration. Applications may use di erent terminologies to describe the same domain. Even when appli-cations use the same terminology, they often associate di erent semantics with the terms. This obstructs information exchange among applications. To cir-cumvent this obstacle, we need some way of explicitly specifying the semantics for each terminology in an unambiguous fashion. Ontologies can provide such specification. It will be the task of this paper to explain what ontologies are and how they can be used to facilitate interoperability between software systems used in computer aided design, architecture engineering and construction, and geographic information processing.
    Date
    3.12.2016 18:39:22
  19. Xu, G.; Cao, Y.; Ren, Y.; Li, X.; Feng, Z.: Network security situation awareness based on semantic ontology and user-defined rules for Internet of Things (2017) 0.01
    0.011841811 = product of:
      0.029604528 = sum of:
        0.0117592495 = product of:
          0.058796246 = sum of:
            0.058796246 = weight(_text_:problem in 306) [ClassicSimilarity], result of:
              0.058796246 = score(doc=306,freq=4.0), product of:
                0.17731056 = queryWeight, product of:
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.04177434 = queryNorm
                0.33160037 = fieldWeight in 306, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  4.244485 = idf(docFreq=1723, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=306)
          0.2 = coord(1/5)
        0.017845279 = weight(_text_:of in 306) [ClassicSimilarity], result of:
          0.017845279 = score(doc=306,freq=20.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.27317715 = fieldWeight in 306, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=306)
      0.4 = coord(2/5)
    
    Abstract
    Internet of Things (IoT) brings the third development wave of the global information industry which makes users, network and perception devices cooperate more closely. However, if IoT has security problems, it may cause a variety of damage and even threaten human lives and properties. To improve the abilities of monitoring, providing emergency response and predicting the development trend of IoT security, a new paradigm called network security situation awareness (NSSA) is proposed. However, it is limited by its ability to mine and evaluate security situation elements from multi-source heterogeneous network security information. To solve this problem, this paper proposes an IoT network security situation awareness model using situation reasoning method based on semantic ontology and user-defined rules. Ontology technology can provide a unified and formalized description to solve the problem of semantic heterogeneity in the IoT security domain. In this paper, four key sub-domains are proposed to reflect an IoT security situation: context, attack, vulnerability and network flow. Further, user-defined rules can compensate for the limited description ability of ontology, and hence can enhance the reasoning ability of our proposed ontology model. The examples in real IoT scenarios show that the ability of the network security situation awareness that adopts our situation reasoning method is more comprehensive and more powerful reasoning abilities than the traditional NSSA methods. [http://ieeexplore.ieee.org/abstract/document/7999187/]
  20. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.01
    0.0107072545 = product of:
      0.026768135 = sum of:
        0.012618518 = weight(_text_:of in 4553) [ClassicSimilarity], result of:
          0.012618518 = score(doc=4553,freq=10.0), product of:
            0.06532493 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.04177434 = queryNorm
            0.19316542 = fieldWeight in 4553, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4553)
        0.0141496165 = product of:
          0.028299233 = sum of:
            0.028299233 = weight(_text_:22 in 4553) [ClassicSimilarity], result of:
              0.028299233 = score(doc=4553,freq=2.0), product of:
                0.14628662 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.04177434 = queryNorm
                0.19345059 = fieldWeight in 4553, 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=4553)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.
    Date
    16.11.2018 14:22:01

Years

Languages

  • e 133
  • d 6
  • More… Less…

Types

  • a 64
  • n 11
  • p 3
  • x 3
  • r 2
  • s 1
  • More… Less…