Search (471 results, page 1 of 24)

  • × theme_ss:"Wissensrepräsentation"
  1. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.04
    0.040590517 = product of:
      0.060885776 = sum of:
        0.052187677 = product of:
          0.20875071 = sum of:
            0.20875071 = weight(_text_:3a in 400) [ClassicSimilarity], result of:
              0.20875071 = score(doc=400,freq=2.0), product of:
                0.37143064 = queryWeight, product of:
                  8.478011 = idf(docFreq=24, maxDocs=44218)
                  0.043811057 = queryNorm
                0.56201804 = fieldWeight in 400, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  8.478011 = idf(docFreq=24, maxDocs=44218)
                  0.046875 = fieldNorm(doc=400)
          0.25 = coord(1/4)
        0.008698098 = product of:
          0.017396197 = sum of:
            0.017396197 = weight(_text_:of in 400) [ClassicSimilarity], result of:
              0.017396197 = score(doc=400,freq=12.0), product of:
                0.06850986 = queryWeight, product of:
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.043811057 = queryNorm
                0.25392252 = fieldWeight in 400, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.046875 = fieldNorm(doc=400)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts. We call these attributes facets: classification has a few facets such as application (e.g., face recognition), model (e.g., svm, knn), and metric (e.g., precision). In this work, we aim at building faceted concept hierarchies from scientific literature. Hierarchy construction methods heavily rely on hypernym detection, however, the faceted relations are parent-to-child links but the hypernym relation is a multi-hop, i.e., ancestor-to-descendent link with a specific facet "type-of". We use information extraction techniques to find synonyms, sibling concepts, and ancestor-descendent relations from a data science corpus. And we propose a hierarchy growth algorithm to infer the parent-child links from the three types of relationships. It resolves conflicts by maintaining the acyclic structure of a hierarchy.
    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
    Source
    Graph-Based Methods for Natural Language Processing - proceedings of the Thirteenth Workshop (TextGraphs-13): November 4, 2019, Hong Kong : EMNLP-IJCNLP 2019. Ed.: Dmitry Ustalov
  2. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.03
    0.029507384 = product of:
      0.044261076 = sum of:
        0.034791786 = product of:
          0.13916714 = sum of:
            0.13916714 = weight(_text_:3a in 701) [ClassicSimilarity], result of:
              0.13916714 = score(doc=701,freq=2.0), product of:
                0.37143064 = queryWeight, product of:
                  8.478011 = idf(docFreq=24, maxDocs=44218)
                  0.043811057 = 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)
          0.25 = coord(1/4)
        0.009469291 = product of:
          0.018938582 = sum of:
            0.018938582 = weight(_text_:of in 701) [ClassicSimilarity], result of:
              0.018938582 = score(doc=701,freq=32.0), product of:
                0.06850986 = queryWeight, product of:
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.043811057 = queryNorm
                0.27643585 = fieldWeight in 701, product of:
                  5.656854 = tf(freq=32.0), with freq of:
                    32.0 = termFreq=32.0
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.03125 = fieldNorm(doc=701)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    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.
  3. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.03
    0.029306926 = product of:
      0.04396039 = sum of:
        0.034791786 = product of:
          0.13916714 = sum of:
            0.13916714 = weight(_text_:3a in 5820) [ClassicSimilarity], result of:
              0.13916714 = score(doc=5820,freq=2.0), product of:
                0.37143064 = queryWeight, product of:
                  8.478011 = idf(docFreq=24, maxDocs=44218)
                  0.043811057 = 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)
          0.25 = coord(1/4)
        0.009168602 = product of:
          0.018337203 = sum of:
            0.018337203 = weight(_text_:of in 5820) [ClassicSimilarity], result of:
              0.018337203 = score(doc=5820,freq=30.0), product of:
                0.06850986 = queryWeight, product of:
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.043811057 = queryNorm
                0.26765788 = fieldWeight in 5820, product of:
                  5.477226 = tf(freq=30.0), with freq of:
                    30.0 = termFreq=30.0
                  1.5637573 = idf(docFreq=25162, maxDocs=44218)
                  0.03125 = fieldNorm(doc=5820)
          0.5 = coord(1/2)
      0.6666667 = coord(2/3)
    
    Abstract
    The successes of information retrieval (IR) in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the external and carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems. This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a general framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our query representations, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity space.
    This proposal includes plans to improve the quality of relevant entities with a co-learning framework that learns from both entity labels and document labels. We also plan to develop a hybrid ranking system that combines word based and entity based representations together with their uncertainties considered. At last, we plan to enrich the text representations with connections between entities. We propose several ways to infer entity graph representations for texts, and to rank documents using their structure representations. This dissertation overcomes the limitation of word based representations with external and carefully curated information from knowledge bases. We believe this thesis research is a solid start towards the new generation of intelligent, semantic, and structured information retrieval.
    Content
    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
    Imprint
    Pittsburgh, PA : Carnegie Mellon University, School of Computer Science, Language Technologies Institute
  4. Schmitz-Esser, W.: Language of general communication and concept compatibility (1996) 0.03
    0.026619852 = product of:
      0.079859555 = sum of:
        0.079859555 = sum of:
          0.020501617 = weight(_text_:of in 6089) [ClassicSimilarity], result of:
            0.020501617 = score(doc=6089,freq=6.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.2992506 = fieldWeight in 6089, product of:
                2.4494898 = tf(freq=6.0), with freq of:
                  6.0 = termFreq=6.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.078125 = fieldNorm(doc=6089)
          0.059357934 = weight(_text_:22 in 6089) [ClassicSimilarity], result of:
            0.059357934 = score(doc=6089,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.38690117 = fieldWeight in 6089, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.078125 = fieldNorm(doc=6089)
      0.33333334 = coord(1/3)
    
    Pages
    S.11-22
    Source
    Compatibility and integration of order systems: Research Seminar Proceedings of the TIP/ISKO Meeting, Warsaw, 13-15 September 1995
  5. Giunchiglia, F.; Villafiorita, A.; Walsh, T.: Theories of abstraction (1997) 0.02
    0.024756517 = product of:
      0.07426955 = sum of:
        0.07426955 = sum of:
          0.0267832 = weight(_text_:of in 4476) [ClassicSimilarity], result of:
            0.0267832 = score(doc=4476,freq=16.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.39093933 = fieldWeight in 4476, 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=4476)
          0.047486346 = weight(_text_:22 in 4476) [ClassicSimilarity], result of:
            0.047486346 = score(doc=4476,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.30952093 = fieldWeight in 4476, 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=4476)
      0.33333334 = coord(1/3)
    
    Abstract
    Describes the types of representations used in different theories of abstractions. Shows how the type of mapping between these representations has been increasingly generalised. Discusses desirable properties preserved by such mappings and identifies how these properties are influenced by the mappings and the presentations defined. Surveys programs made in understanding the complexity reduction associated with abstraction. Focuses on formal models of how abstraction reduces the search space. Presents some of the systems that implement abstraction. shows how the efforts in this area have focused on the mechanisation of languages for the declarative representation of abstraction.
    Date
    1.10.2018 14:13:22
  6. OWL Web Ontology Language Test Cases (2004) 0.02
    0.022141643 = product of:
      0.06642493 = sum of:
        0.06642493 = sum of:
          0.018938582 = weight(_text_:of in 4685) [ClassicSimilarity], result of:
            0.018938582 = score(doc=4685,freq=8.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = 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)
          0.047486346 = weight(_text_:22 in 4685) [ClassicSimilarity], result of:
            0.047486346 = score(doc=4685,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = 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)
      0.33333334 = coord(1/3)
    
    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
  7. Börner, K.: Atlas of knowledge : anyone can map (2015) 0.02
    0.021523606 = product of:
      0.064570814 = sum of:
        0.064570814 = sum of:
          0.014203937 = weight(_text_:of in 3355) [ClassicSimilarity], result of:
            0.014203937 = score(doc=3355,freq=8.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.20732689 = fieldWeight in 3355, 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=3355)
          0.05036688 = weight(_text_:22 in 3355) [ClassicSimilarity], result of:
            0.05036688 = score(doc=3355,freq=4.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.32829654 = fieldWeight in 3355, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=3355)
      0.33333334 = coord(1/3)
    
    Content
    One of a series of three publications influenced by the travelling exhibit Places & Spaces: Mapping Science, curated by the Cyberinfrastructure for Network Science Center at Indiana University. - Additional materials can be found at http://http://scimaps.org/atlas2. Erweitert durch: Börner, Katy. Atlas of Science: Visualizing What We Know.
    Date
    22. 1.2017 16:54:03
    22. 1.2017 17:10:56
  8. Priss, U.: Faceted information representation (2000) 0.02
    0.020615373 = product of:
      0.06184612 = sum of:
        0.06184612 = sum of:
          0.020295564 = weight(_text_:of in 5095) [ClassicSimilarity], result of:
            0.020295564 = score(doc=5095,freq=12.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.29624295 = fieldWeight in 5095, product of:
                3.4641016 = tf(freq=12.0), with freq of:
                  12.0 = termFreq=12.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.0546875 = fieldNorm(doc=5095)
          0.041550554 = weight(_text_:22 in 5095) [ClassicSimilarity], result of:
            0.041550554 = score(doc=5095,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.2708308 = fieldWeight in 5095, 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=5095)
      0.33333334 = coord(1/3)
    
    Abstract
    This paper presents an abstract formalization of the notion of "facets". Facets are relational structures of units, relations and other facets selected for a certain purpose. Facets can be used to structure large knowledge representation systems into a hierarchical arrangement of consistent and independent subsystems (facets) that facilitate flexibility and combinations of different viewpoints or aspects. This paper describes the basic notions, facet characteristics and construction mechanisms. It then explicates the theory in an example of a faceted information retrieval system (FaIR)
    Date
    22. 1.2016 17:47:06
  9. Priss, U.: Faceted knowledge representation (1999) 0.02
    0.02002593 = product of:
      0.060077786 = sum of:
        0.060077786 = sum of:
          0.018527232 = weight(_text_:of in 2654) [ClassicSimilarity], result of:
            0.018527232 = score(doc=2654,freq=10.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = 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)
          0.041550554 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
            0.041550554 = score(doc=2654,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = 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)
      0.33333334 = coord(1/3)
    
    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
  10. Hollink, L.; Assem, M. van: Estimating the relevance of search results in the Culture-Web : a study of semantic distance measures (2010) 0.02
    0.019723108 = product of:
      0.059169322 = sum of:
        0.059169322 = sum of:
          0.023554565 = weight(_text_:of in 4649) [ClassicSimilarity], result of:
            0.023554565 = score(doc=4649,freq=22.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = 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)
          0.03561476 = weight(_text_:22 in 4649) [ClassicSimilarity], result of:
            0.03561476 = score(doc=4649,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = 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)
      0.33333334 = coord(1/3)
    
    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
  11. Priss, U.: Description logic and faceted knowledge representation (1999) 0.02
    0.019723108 = product of:
      0.059169322 = sum of:
        0.059169322 = sum of:
          0.023554565 = weight(_text_:of in 2655) [ClassicSimilarity], result of:
            0.023554565 = score(doc=2655,freq=22.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = 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)
          0.03561476 = weight(_text_:22 in 2655) [ClassicSimilarity], result of:
            0.03561476 = score(doc=2655,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = 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)
      0.33333334 = coord(1/3)
    
    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
  12. Madalli, D.P.; Balaji, B.P.; Sarangi, A.K.: Music domain analysis for building faceted ontological representation (2014) 0.02
    0.019373938 = product of:
      0.058121815 = sum of:
        0.058121815 = sum of:
          0.01657126 = weight(_text_:of in 1437) [ClassicSimilarity], result of:
            0.01657126 = score(doc=1437,freq=8.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.24188137 = fieldWeight in 1437, product of:
                2.828427 = tf(freq=8.0), with freq of:
                  8.0 = termFreq=8.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.0546875 = fieldNorm(doc=1437)
          0.041550554 = weight(_text_:22 in 1437) [ClassicSimilarity], result of:
            0.041550554 = score(doc=1437,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.2708308 = fieldWeight in 1437, 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=1437)
      0.33333334 = coord(1/3)
    
    Abstract
    This paper describes to construct faceted ontologies for domain modeling. Building upon the faceted theory of S.R. Ranganathan (1967), the paper intends to address the faceted classification approach applied to build domain ontologies. As classificatory ontologies are employed to represent the relationships of entities and objects on the web, the faceted approach helps to analyze domain representation in an effective way for modeling. Based on this perspective, an ontology of the music domain has been analyzed that would serve as a case study.
    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  13. Definition of the CIDOC Conceptual Reference Model (2003) 0.02
    0.019357719 = product of:
      0.058073156 = sum of:
        0.058073156 = sum of:
          0.022458395 = weight(_text_:of in 1652) [ClassicSimilarity], result of:
            0.022458395 = score(doc=1652,freq=20.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = 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)
          0.03561476 = weight(_text_:22 in 1652) [ClassicSimilarity], result of:
            0.03561476 = score(doc=1652,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = 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)
      0.33333334 = coord(1/3)
    
    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
  14. Synak, M.; Dabrowski, M.; Kruk, S.R.: Semantic Web and ontologies (2009) 0.02
    0.018985212 = product of:
      0.056955636 = sum of:
        0.056955636 = sum of:
          0.009469291 = weight(_text_:of in 3376) [ClassicSimilarity], result of:
            0.009469291 = score(doc=3376,freq=2.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.13821793 = fieldWeight in 3376, 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=3376)
          0.047486346 = weight(_text_:22 in 3376) [ClassicSimilarity], result of:
            0.047486346 = score(doc=3376,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.30952093 = fieldWeight in 3376, 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=3376)
      0.33333334 = coord(1/3)
    
    Abstract
    This chapter presents ontologies and their role in the creation of the Semantic Web. Ontologies hold special interest, because they are very closely related to the way we understand the world. They provide common understanding, the very first step to successful communication. In following sections, we will present ontologies, how they are created and used. We will describe available tools for specifying and working with ontologies.
    Date
    31. 7.2010 16:58:22
  15. 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) 0.02
    0.018985212 = product of:
      0.056955636 = sum of:
        0.056955636 = sum of:
          0.009469291 = weight(_text_:of in 318) [ClassicSimilarity], result of:
            0.009469291 = score(doc=318,freq=2.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = 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)
          0.047486346 = weight(_text_:22 in 318) [ClassicSimilarity], result of:
            0.047486346 = score(doc=318,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = 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)
      0.33333334 = coord(1/3)
    
    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
  16. Mayfield, J.; Finin, T.: Information retrieval on the Semantic Web : integrating inference and retrieval 0.02
    0.018633895 = product of:
      0.055901684 = sum of:
        0.055901684 = sum of:
          0.014351131 = weight(_text_:of in 4330) [ClassicSimilarity], result of:
            0.014351131 = score(doc=4330,freq=6.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = 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)
          0.041550554 = weight(_text_:22 in 4330) [ClassicSimilarity], result of:
            0.041550554 = score(doc=4330,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = 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)
      0.33333334 = coord(1/3)
    
    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
  17. Kruk, S.R.; Kruk, E.; Stankiewicz, K.: Evaluation of semantic and social technologies for digital libraries (2009) 0.02
    0.018567387 = product of:
      0.055702157 = sum of:
        0.055702157 = sum of:
          0.020087399 = weight(_text_:of in 3387) [ClassicSimilarity], result of:
            0.020087399 = score(doc=3387,freq=16.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.2932045 = fieldWeight in 3387, 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=3387)
          0.03561476 = weight(_text_:22 in 3387) [ClassicSimilarity], result of:
            0.03561476 = score(doc=3387,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.23214069 = fieldWeight in 3387, 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=3387)
      0.33333334 = coord(1/3)
    
    Abstract
    Libraries are the tools we use to learn and to answer our questions. The quality of our work depends, among others, on the quality of the tools we use. Recent research in digital libraries is focused, on one hand on improving the infrastructure of the digital library management systems (DLMS), and on the other on improving the metadata models used to annotate collections of objects maintained by DLMS. The latter includes, among others, the semantic web and social networking technologies. Recently, the semantic web and social networking technologies are being introduced to the digital libraries domain. The expected outcome is that the overall quality of information discovery in digital libraries can be improved by employing social and semantic technologies. In this chapter we present the results of an evaluation of social and semantic end-user information discovery services for the digital libraries.
    Date
    1. 8.2010 12:35:22
  18. Baião Salgado Silva, G.; Lima, G.Â. Borém de Oliveira: Using topic maps in establishing compatibility of semantically structured hypertext contents (2012) 0.02
    0.018492091 = product of:
      0.05547627 = sum of:
        0.05547627 = sum of:
          0.025797302 = weight(_text_:of in 633) [ClassicSimilarity], result of:
            0.025797302 = score(doc=633,freq=38.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.37654874 = fieldWeight in 633, product of:
                6.164414 = tf(freq=38.0), with freq of:
                  38.0 = termFreq=38.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.0390625 = fieldNorm(doc=633)
          0.029678967 = weight(_text_:22 in 633) [ClassicSimilarity], result of:
            0.029678967 = score(doc=633,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.19345059 = fieldWeight in 633, 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=633)
      0.33333334 = coord(1/3)
    
    Abstract
    Considering the characteristics of hypertext systems and problems such as cognitive overload and the disorientation of users, this project studies subject hypertext documents that have undergone conceptual structuring using facets for content representation and improvement of information retrieval during navigation. The main objective was to assess the possibility of the application of topic map technology for automating the compatibilization process of these structures. For this purpose, two dissertations from the UFMG Information Science Post-Graduation Program were adopted as samples. Both dissertations had been duly analyzed and structured on the MHTX (Hypertextual Map) prototype database. The faceted structures of both dissertations, which had been represented in conceptual maps, were then converted into topic maps. It was then possible to use the merge property of the topic maps to promote the semantic interrelationship between the maps and, consequently, between the hypertextual information resources proper. The merge results were then analyzed in the light of theories dealing with the compatibilization of languages developed within the realm of information technology and librarianship from the 1960s on. The main goals accomplished were: (a) the detailed conceptualization of the merge process of the topic maps, considering the possible compatibilization levels and the applicability of this technology in the integration of faceted structures; and (b) the production of a detailed sequence of steps that may be used in the implementation of topic maps based on faceted structures.
    Date
    22. 2.2013 11:39:23
  19. Zeng, M.L.; Fan, W.; Lin, X.: SKOS for an integrated vocabulary structure (2008) 0.02
    0.01788844 = product of:
      0.053665318 = sum of:
        0.053665318 = sum of:
          0.020087399 = weight(_text_:of in 2654) [ClassicSimilarity], result of:
            0.020087399 = score(doc=2654,freq=36.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.2932045 = fieldWeight in 2654, product of:
                6.0 = tf(freq=36.0), with freq of:
                  36.0 = termFreq=36.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.03125 = fieldNorm(doc=2654)
          0.03357792 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
            0.03357792 = score(doc=2654,freq=4.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.21886435 = fieldWeight in 2654, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.03125 = fieldNorm(doc=2654)
      0.33333334 = coord(1/3)
    
    Abstract
    In order to transfer the Chinese Classified Thesaurus (CCT) into a machine-processable format and provide CCT-based Web services, a pilot study has been conducted in which a variety of selected CCT classes and mapped thesaurus entries are encoded with SKOS. OWL and RDFS are also used to encode the same contents for the purposes of feasibility and cost-benefit comparison. CCT is a collected effort led by the National Library of China. It is an integration of the national standards Chinese Library Classification (CLC) 4th edition and Chinese Thesaurus (CT). As a manually created mapping product, CCT provides for each of the classes the corresponding thesaurus terms, and vice versa. The coverage of CCT includes four major clusters: philosophy, social sciences and humanities, natural sciences and technologies, and general works. There are 22 main-classes, 52,992 sub-classes and divisions, 110,837 preferred thesaurus terms, 35,690 entry terms (non-preferred terms), and 59,738 pre-coordinated headings (Chinese Classified Thesaurus, 2005) Major challenges of encoding this large vocabulary comes from its integrated structure. CCT is a result of the combination of two structures (illustrated in Figure 1): a thesaurus that uses ISO-2788 standardized structure and a classification scheme that is basically enumerative, but provides some flexibility for several kinds of synthetic mechanisms Other challenges include the complex relationships caused by differences of granularities of two original schemes and their presentation with various levels of SKOS elements; as well as the diverse coordination of entries due to the use of auxiliary tables and pre-coordinated headings derived from combining classes, subdivisions, and thesaurus terms, which do not correspond to existing unique identifiers. The poster reports the progress, shares the sample SKOS entries, and summarizes problems identified during the SKOS encoding process. Although OWL Lite and OWL Full provide richer expressiveness, the cost-benefit issues and the final purposes of encoding CCT raise questions of using such approaches.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  20. Kiren, T.; Shoaib, M.: ¬A novel ontology matching approach using key concepts (2016) 0.02
    0.017274415 = product of:
      0.051823243 = sum of:
        0.051823243 = sum of:
          0.022144277 = weight(_text_:of in 2589) [ClassicSimilarity], result of:
            0.022144277 = score(doc=2589,freq=28.0), product of:
              0.06850986 = queryWeight, product of:
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.043811057 = queryNorm
              0.32322758 = fieldWeight in 2589, product of:
                5.2915025 = tf(freq=28.0), with freq of:
                  28.0 = termFreq=28.0
                1.5637573 = idf(docFreq=25162, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2589)
          0.029678967 = weight(_text_:22 in 2589) [ClassicSimilarity], result of:
            0.029678967 = score(doc=2589,freq=2.0), product of:
              0.15341885 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.043811057 = queryNorm
              0.19345059 = fieldWeight in 2589, 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=2589)
      0.33333334 = coord(1/3)
    
    Abstract
    Purpose Ontologies are used to formally describe the concepts within a domain in a machine-understandable way. Matching of heterogeneous ontologies is often essential for many applications like semantic annotation, query answering or ontology integration. Some ontologies may include a large number of entities which make the ontology matching process very complex in terms of the search space and execution time requirements. The purpose of this paper is to present a technique for finding degree of similarity between ontologies that trims down the search space by eliminating the ontology concepts that have less likelihood of being matched. Design/methodology/approach Algorithms are written for finding key concepts, concept matching and relationship matching. WordNet is used for solving synonym problems during the matching process. The technique is evaluated using the reference alignments between ontologies from ontology alignment evaluation initiative benchmark in terms of degree of similarity, Pearson's correlation coefficient and IR measures precision, recall and F-measure. Findings Positive correlation between the degree of similarity and degree of similarity (reference alignment) and computed values of precision, recall and F-measure showed that if only key concepts of ontologies are compared, a time and search space efficient ontology matching system can be developed. Originality/value On the basis of the present novel approach for ontology matching, it is concluded that using key concepts for ontology matching gives comparable results in reduced time and space.
    Date
    20. 1.2015 18:30:22
    Source
    Aslib journal of information management. 68(2016) no.1, S.99-111

Years

Languages

  • e 440
  • d 19
  • pt 4
  • f 1
  • sp 1
  • More… Less…

Types

  • a 347
  • el 138
  • m 27
  • x 19
  • n 13
  • s 13
  • p 6
  • r 3
  • A 1
  • EL 1
  • More… Less…

Subjects

Classifications