Search (30 results, page 1 of 2)

0.05078162 = product of:
  0.07617243 = sum of:
    0.06241733 = weight(_text_:im in 2245) [ClassicSimilarity], result of:
      0.06241733 = score(doc=2245,freq=6.0), product of:
        0.1442303 = queryWeight, product of:
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.051022716 = queryNorm
        0.43276152 = fieldWeight in 2245, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.0625 = fieldNorm(doc=2245)
    0.013755098 = product of:
      0.041265294 = sum of:
        0.041265294 = weight(_text_:retrieval in 2245) [ClassicSimilarity], result of:
          0.041265294 = score(doc=2245,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.26736724 = fieldWeight in 2245, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0625 = fieldNorm(doc=2245)
      0.33333334 = coord(1/3)
  0.6666667 = coord(2/3)

Abstract

Dieser Artikel adressiert einen Randbereich des Wissensmanagements: die Schnittstelle zwischen Unternehmens-externen Informationen im Internet und den Leistungsprozessen eines Unternehmens. Diese Schnittstelle ist besonders für Unternehmen von Interesse, deren Leistungsprozesse von externen Informationen abhängen und die auf diese Prozesse angewiesen sind. Wir zeigen an zwei Fallbeispielen, dass die inhaltliche Filterung von Informationen beim Eintritt ins Unternehmen ein wichtiges Werkzeug darstellt, um daran anschließende Wissens- und Informationsmanagementprozesse effizient zu gestalten.

Source

Open journal of knowledge management. 2013, Ausgabe VII = http://www.community-of-knowledge.de/beitrag/semantische-filterung-ein-werkzeug-zur-steigerung-der-effizienz-im-wissensmanagement/

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.036313996 = product of:
  0.05447099 = sum of:
    0.03603666 = weight(_text_:im in 318) [ClassicSimilarity], result of:
      0.03603666 = score(doc=318,freq=2.0), product of:
        0.1442303 = queryWeight, product of:
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.051022716 = queryNorm
        0.24985497 = fieldWeight in 318, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.8267863 = idf(docFreq=7115, maxDocs=44218)
          0.0625 = fieldNorm(doc=318)
    0.01843433 = product of:
      0.055302992 = sum of:
        0.055302992 = weight(_text_:22 in 318) [ClassicSimilarity], result of:
          0.055302992 = score(doc=318,freq=2.0), product of:
            0.17867287 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051022716 = 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)
  0.6666667 = coord(2/3)

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

0.02112943 = product of:
  0.06338829 = sum of:
    0.06338829 = product of:
      0.09508243 = sum of:
        0.05360519 = weight(_text_:retrieval in 3261) [ClassicSimilarity], result of:
          0.05360519 = score(doc=3261,freq=6.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.34732026 = fieldWeight in 3261, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=3261)
        0.04147724 = weight(_text_:22 in 3261) [ClassicSimilarity], result of:
          0.04147724 = score(doc=3261,freq=2.0), product of:
            0.17867287 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051022716 = 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)
      0.6666667 = coord(2/3)
  0.33333334 = coord(1/3)

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

0.018777166 = product of:
  0.056331497 = sum of:
    0.056331497 = product of:
      0.08449724 = sum of:
        0.03610713 = weight(_text_:retrieval in 2654) [ClassicSimilarity], result of:
          0.03610713 = score(doc=2654,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.23394634 = fieldWeight in 2654, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2654)
        0.048390117 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
          0.048390117 = score(doc=2654,freq=2.0), product of:
            0.17867287 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051022716 = 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)
      0.6666667 = coord(2/3)
  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

0.017231777 = product of:
  0.05169533 = sum of:
    0.05169533 = product of:
      0.07754299 = sum of:
        0.025961377 = weight(_text_:online in 5864) [ClassicSimilarity], result of:
          0.025961377 = score(doc=5864,freq=2.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.16765618 = fieldWeight in 5864, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5864)
        0.051581617 = weight(_text_:retrieval in 5864) [ClassicSimilarity], result of:
          0.051581617 = score(doc=5864,freq=8.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.33420905 = fieldWeight in 5864, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5864)
      0.6666667 = coord(2/3)
  0.33333334 = coord(1/3)

Abstract

Current retrieval and recommendation approaches rely on hard-wired data models. This hinders personalized cus-tomizations to meet information needs of users in a more flexible manner. Therefore, the paper investigates how similarity-basedretrieval strategies can be combined with graph queries to enable users or system providers to explore repositories in the LinkedOpen Data (LOD) cloud more thoroughly. For this purpose, we developed novel content-based recommendation approaches.They rely on concept annotations of Simple Knowledge Organization System (SKOS) vocabularies and a SPARQL-based querylanguage that facilitates advanced and personalized requests for openly available knowledge graphs. We have comprehensivelyevaluated the novel search strategies in several test cases and example application domains (i.e., travel search and multimediaretrieval). The results of the web-based online experiments showed that our approaches increase the recall and diversity of rec-ommendations or at least provide a competitive alternative strategy of resource access when conventional methods do not providehelpful suggestions. The findings may be of use for Linked Data-enabled recommender systems (LDRS) as well as for semanticsearch engines that can consume LOD resources. (PDF) Similarity-based knowledge graph queries for recommendation retrieval. Available from: https://www.researchgate.net/publication/333358714_Similarity-based_knowledge_graph_queries_for_recommendation_retrieval [accessed May 21 2020].

Content

Vgl.: https://www.researchgate.net/publication/333358714_Similarity-based_knowledge_graph_queries_for_recommendation_retrieval. Vgl. auch: http://semantic-web-journal.net/content/similarity-based-knowledge-graph-queries-recommendation-retrieval-1.

0.016094714 = product of:
  0.048284143 = sum of:
    0.048284143 = product of:
      0.072426215 = sum of:
        0.03094897 = weight(_text_:retrieval in 2655) [ClassicSimilarity], result of:
          0.03094897 = score(doc=2655,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.20052543 = fieldWeight in 2655, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=2655)
        0.04147724 = weight(_text_:22 in 2655) [ClassicSimilarity], result of:
          0.04147724 = score(doc=2655,freq=2.0), product of:
            0.17867287 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051022716 = 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)
      0.6666667 = coord(2/3)
  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

0.010252445 = product of:
  0.030757334 = sum of:
    0.030757334 = product of:
      0.092272 = sum of:
        0.092272 = weight(_text_:retrieval in 1510) [ClassicSimilarity], result of:
          0.092272 = score(doc=1510,freq=10.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.59785134 = fieldWeight in 1510, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0625 = fieldNorm(doc=1510)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

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.

0.009170066 = product of:
  0.027510196 = sum of:
    0.027510196 = product of:
      0.08253059 = sum of:
        0.08253059 = weight(_text_:retrieval in 2862) [ClassicSimilarity], result of:
          0.08253059 = score(doc=2862,freq=8.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.5347345 = fieldWeight in 2862, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0625 = fieldNorm(doc=2862)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

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.

0.008970889 = product of:
  0.026912667 = sum of:
    0.026912667 = product of:
      0.080738 = sum of:
        0.080738 = weight(_text_:retrieval in 4121) [ClassicSimilarity], result of:
          0.080738 = score(doc=4121,freq=10.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.5231199 = fieldWeight in 4121, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4121)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

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

0.008970889 = product of:
  0.026912667 = sum of:
    0.026912667 = product of:
      0.080738 = sum of:
        0.080738 = weight(_text_:retrieval in 1153) [ClassicSimilarity], result of:
          0.080738 = score(doc=1153,freq=10.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.5231199 = fieldWeight in 1153, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1153)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

In the proposed article a new, ontology-based approach to information retrieval (IR) is presented. The system is based on a domain knowledge representation schema in form of ontology. New resources registered within the system are linked to concepts from this ontology. In such a way resources may be retrieved based on the associations and not only based on partial or exact term matching as the use of vector model presumes In order to evaluate the quality of this retrieval mechanism, experiments to measure retrieval efficiency have been performed with well-known Cystic Fibrosis collection of medical scientific papers. The ontology-based retrieval mechanism has been compared with traditional full text search based on vector IR model as well as with the Latent Semantic Indexing method.

0.008023808 = product of:
  0.024071421 = sum of:
    0.024071421 = product of:
      0.07221426 = sum of:
        0.07221426 = weight(_text_:retrieval in 4329) [ClassicSimilarity], result of:
          0.07221426 = score(doc=4329,freq=8.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.46789268 = fieldWeight in 4329, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4329)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Plenty of contemporary attempts to search exist that are associated with the area of Semantic Web. But which of them qualify as information retrieval for the Semantic Web? Do such approaches exist? To answer these questions we take a look at the nature of the Semantic Web and Semantic Desktop and at definitions for information and data retrieval. We survey current approaches referred to by their authors as information retrieval for the Semantic Web or that use Semantic Web technology for search.

0.008023808 = product of:
  0.024071421 = sum of:
    0.024071421 = product of:
      0.07221426 = sum of:
        0.07221426 = weight(_text_:retrieval in 667) [ClassicSimilarity], result of:
          0.07221426 = score(doc=667,freq=8.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.46789268 = fieldWeight in 667, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=667)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

We present an overview of the NTCIR Math Tasks organized during NTCIR-10, 11, and 12. These tasks are primarily dedicated to techniques for searching mathematical content with formula expressions. In this chapter, we first summarize the task design and introduce test collections generated in the tasks. We also describe the features and main challenges of mathematical information retrieval systems and discuss future perspectives in the field.

Series

¬The Information retrieval series, vol 43

Source

Evaluating information retrieval and access tasks. Eds.: Sakai, T., Oard, D., Kando, N. [https://doi.org/10.1007/978-981-15-5554-1_12]

0.006877549 = product of:
  0.020632647 = sum of:
    0.020632647 = product of:
      0.06189794 = sum of:
        0.06189794 = weight(_text_:retrieval in 3829) [ClassicSimilarity], result of:
          0.06189794 = score(doc=3829,freq=8.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.40105087 = fieldWeight in 3829, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=3829)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

In this thesis, we present an ontology-based information extraction and retrieval system and its application to soccer domain. In general, we deal with three issues in semantic search, namely, usability, scalability and retrieval performance. We propose a keyword-based semantic retrieval approach. The performance of the system is improved considerably using domain-specific information extraction, inference and rules. Scalability is achieved by adapting a semantic indexing approach. The system is implemented using the state-of-the-art technologies in SemanticWeb and its performance is evaluated against traditional systems as well as the query expansion methods. Furthermore, a detailed evaluation is provided to observe the performance gain due to domain-specific information extraction and inference. Finally, we show how we use semantic indexing to solve simple structural ambiguities.

0.005956133 = product of:
  0.017868398 = sum of:
    0.017868398 = product of:
      0.05360519 = sum of:
        0.05360519 = weight(_text_:retrieval in 3264) [ClassicSimilarity], result of:
          0.05360519 = score(doc=3264,freq=6.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.34732026 = fieldWeight in 3264, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.046875 = fieldNorm(doc=3264)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

We scho how the beenfits of the view-based search method, developed within the information retrieval community, can be extended with ontology-based search, developed within the Semantic Web community, and with semantic recommendations. As a proof of the concept, we have implemented an ontology-and view-based search engine and recommendations system Ontogaotr for RDF(S) repositories. Ontogator is innovative in two ways. Firstly, the RDFS.based ontologies used for annotating metadata are used in the user interface to facilitate view-based information retrieval. The views provide the user with an overview of the repositorys contents and a vocabulary for expressing search queries. Secondlyy, a semantic browsing function is provided by a recommender system. This system enriches instance level metadata by ontologies and provides the user with links to semantically related relevant resources. The semantic linkage is specified in terms of logical rules. To illustrate and discuss the ideas, a deployed application of Ontogator to a photo repository of the Helsinki University Museum is presented.

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.0057112123 = product of:
  0.017133636 = sum of:
    0.017133636 = product of:
      0.051400907 = sum of:
        0.051400907 = weight(_text_:online in 52) [ClassicSimilarity], result of:
          0.051400907 = score(doc=52,freq=4.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.33194235 = fieldWeight in 52, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.0546875 = fieldNorm(doc=52)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

While thousands of ontologies exist on the web, a unified sys-tem for handling online ontologies - in particular with respect to discov-ery, versioning, access, quality-control, mappings - has not yet surfacedand users of ontologies struggle with many challenges. In this paper, wepresent an online ontology interface and augmented archive called DB-pedia Archivo, that discovers, crawls, versions and archives ontologies onthe DBpedia Databus. Based on this versioned crawl, different features,quality measures and, if possible, fixes are deployed to handle and sta-bilize the changes in the found ontologies at web-scale. A comparison toexisting approaches and ontology repositories is given.

0.005673688 = product of:
  0.017021064 = sum of:
    0.017021064 = product of:
      0.05106319 = sum of:
        0.05106319 = weight(_text_:retrieval in 3816) [ClassicSimilarity], result of:
          0.05106319 = score(doc=3816,freq=4.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.33085006 = fieldWeight in 3816, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3816)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Faceted arrangement of entities and typed relations for representing different associations between the entities are established tools in knowledge representation. In this paper, a proposal is being discussed combining both tools to draw inferences along relational paths. This approach may yield new benefit for information retrieval processes, especially when modeled for heterogeneous environments in the Semantic Web. Faceted arrangement can be used as a selection tool for the semantic knowledge modeled within the knowledge representation. Typed relations between the entities of different facets can be used as restrictions for selecting them across the facets.

0.0051262225 = product of:
  0.015378667 = sum of:
    0.015378667 = product of:
      0.046136 = sum of:
        0.046136 = weight(_text_:retrieval in 699) [ClassicSimilarity], result of:
          0.046136 = score(doc=699,freq=10.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.29892567 = fieldWeight in 699, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.03125 = fieldNorm(doc=699)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

Background: Because of the increasing number of electronic resources, designing efficient tools to retrieve and exploit them is a major challenge. Some improvements have been offered by semantic Web technologies and applications based on domain ontologies. In life science, for instance, the Gene Ontology is widely exploited in genomic applications and the Medical Subject Headings is the basis of biomedical publications indexation and information retrieval process proposed by PubMed. However current search engines suffer from two main drawbacks: there is limited user interaction with the list of retrieved resources and no explanation for their adequacy to the query is provided. Users may thus be confused by the selection and have no idea on how to adapt their queries so that the results match their expectations. Results: This paper describes an information retrieval system that relies on domain ontology to widen the set of relevant documents that is retrieved and that uses a graphical rendering of query results to favor user interactions. Semantic proximities between ontology concepts and aggregating models are used to assess documents adequacy with respect to a query. The selection of documents is displayed in a semantic map to provide graphical indications that make explicit to what extent they match the user's query; this man/machine interface favors a more interactive and iterative exploration of data corpus, by facilitating query concepts weighting and visual explanation. We illustrate the benefit of using this information retrieval system on two case studies one of which aiming at collecting human genes related to transcription factors involved in hemopoiesis pathway. Conclusions: The ontology based information retrieval system described in this paper (OBIRS) is freely available at: http://www.ontotoolkit.mines-ales.fr/ObirsClient/. This environment is a first step towards a user centred application in which the system enlightens relevant information to provide decision help.

0.0046085827 = product of:
  0.013825747 = sum of:
    0.013825747 = product of:
      0.04147724 = sum of:
        0.04147724 = weight(_text_:22 in 4820) [ClassicSimilarity], result of:
          0.04147724 = score(doc=4820,freq=2.0), product of:
            0.17867287 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051022716 = 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)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Date

3.12.2016 18:39:22

0.004585033 = product of:
  0.013755098 = sum of:
    0.013755098 = product of:
      0.041265294 = sum of:
        0.041265294 = weight(_text_:retrieval in 792) [ClassicSimilarity], result of:
          0.041265294 = score(doc=792,freq=2.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.26736724 = fieldWeight in 792, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0625 = fieldNorm(doc=792)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Theme

Semantisches Umfeld in Indexierung u. Retrieval

0.004052635 = product of:
  0.012157904 = sum of:
    0.012157904 = product of:
      0.03647371 = sum of:
        0.03647371 = weight(_text_:retrieval in 2208) [ClassicSimilarity], result of:
          0.03647371 = score(doc=2208,freq=4.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.23632148 = fieldWeight in 2208, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2208)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Abstract

The formal structure of the information on the Semantic Web lends itself to faceted browsing, an information retrieval method where users can filter results based on the values of properties ("facets"). Numerous faceted browsers have been created to browse RDF and Linked Data, but these systems use their own ontologies for defining how data is queried to populate their facets. Since the source data is the same format across these systems (specifically, RDF), we can unify the different methods of describing how to quer the underlying data, to enable compatibility across systems, and provide an extensible base ontology for future systems. To this end, we present FacetOntology, an ontology that defines how to query data to form a faceted browser, and a number of transformations and filters that can be applied to data before it is shown to users. FacetOntology overcomes limitations in the expressivity of existing work, by enabling the full expressivity of SPARQL when selecting data for facets. By applying a FacetOntology definition to data, a set of facets are specified, each with queries and filters to source RDF data, which enables faceted browsing systems to be created using that RDF data.

Theme

Semantisches Umfeld in Indexierung u. Retrieval