Search (279 results, page 2 of 14)

0.027823225 = product of:
  0.06955806 = sum of:
    0.032278713 = weight(_text_:context in 4800) [ClassicSimilarity], result of:
      0.032278713 = score(doc=4800,freq=2.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.18316938 = fieldWeight in 4800, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.03125 = fieldNorm(doc=4800)
    0.03727935 = weight(_text_:system in 4800) [ClassicSimilarity], result of:
      0.03727935 = score(doc=4800,freq=8.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.27838376 = fieldWeight in 4800, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.03125 = fieldNorm(doc=4800)
  0.4 = coord(2/5)

Abstract

The usefulness of online geological maps is hindered by linguistic barriers. Multilingual geoscience thesauri alleviate linguistic barriers of geological maps. However, the benefits of multilingual geoscience thesauri for online geological maps are less studied. In this regard, we developed a multilingual thesaurus of geological time scale (GTS) to alleviate linguistic barriers of GTS records among online geological maps. We extended the Simple Knowledge Organization System (SKOS) model to represent the ordinal hierarchical structure of GTS terms. We collected GTS terms in seven languages and encoded them into a thesaurus by using the extended SKOS model. We implemented methods of characteristic-oriented term retrieval in JavaScript programs for accessing Web Map Services (WMS), recognizing GTS terms, and making translations. With the developed thesaurus and programs, we set up a pilot system to test recognitions and translations of GTS terms in online geological maps. Results of this pilot system proved the accuracy of the developed thesaurus and the functionality of the developed programs. Therefore, with proper deployments, SKOS-based multilingual geoscience thesauri can be functional for alleviating linguistic barriers among online geological maps and, thus, improving their interoperability.

Content

Article Outline 1. Introduction 2. SKOS-based multilingual thesaurus of geological time scale 2.1. Addressing the insufficiency of SKOS in the context of the Semantic Web 2.2. Addressing semantics and syntax/lexicon in multilingual GTS terms 2.3. Extending SKOS model to capture GTS structure 2.4. Summary of building the SKOS-based MLTGTS 3. Recognizing and translating GTS terms retrieved from WMS 4. Pilot system, results, and evaluation 5. Discussion 6. Conclusions Vgl. unter: http://www.sciencedirect.com/science?_ob=MiamiImageURL&_cid=271720&_user=3865853&_pii=S0098300411000744&_check=y&_origin=&_coverDate=31-Oct-2011&view=c&wchp=dGLbVlt-zSkzS&_valck=1&md5=e2c1daf53df72d034d22278212578f42&ie=/sdarticle.pdf.

0.027260004 = product of:
  0.068150006 = sum of:
    0.044850416 = weight(_text_:index in 4817) [ClassicSimilarity], result of:
      0.044850416 = score(doc=4817,freq=2.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.24139762 = fieldWeight in 4817, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4817)
    0.023299592 = weight(_text_:system in 4817) [ClassicSimilarity], result of:
      0.023299592 = score(doc=4817,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.17398985 = fieldWeight in 4817, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4817)
  0.4 = coord(2/5)

Abstract

The chemistry schedule in the Universal Decimal Classification (UDC) is badly in need of revision. In many places it is enumerative rather than synthetic (giving rules for constructing numbers for any compound required). In principle, chemistry should be the ideal subject for a synthetic classification but many common compounds have complex formulae and a synthetic system becomes unwieldy. Also, all compounds belong to several hierarchies, e.g. chloroquin is a heterocycle, an aromatic compound, amine, antimalarial drug, etc. and rules need to be drawn up as to which ones take precedence and which ones should be taken into account in classifying a compound. There are obvious similarities between a classification and an ontology. This paper looks at existing ontologies for chemistry, especially ChEBI which is one of the largest, to examine how a classification and an ontology might draw on each other and what the problem areas are. An ontology might help in creating an index to a classification (for chemicals not listed or to provide access by facets not used in the classification) and a classification could provide a hierarchy to use in an ontology.

0.027260004 = product of:
  0.068150006 = sum of:
    0.044850416 = weight(_text_:index in 99) [ClassicSimilarity], result of:
      0.044850416 = score(doc=99,freq=2.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.24139762 = fieldWeight in 99, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0390625 = fieldNorm(doc=99)
    0.023299592 = weight(_text_:system in 99) [ClassicSimilarity], result of:
      0.023299592 = score(doc=99,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.17398985 = fieldWeight in 99, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0390625 = fieldNorm(doc=99)
  0.4 = coord(2/5)

Abstract

A new model is proposed to retrieve information by building automatically a semantic metatext structure for texts that allow searching and extracting discourse and semantic information according to certain linguistic categorizations. This paper presents approaches for searching and mining full text with semantic categories. The model is built up from two engines: The first one, called EXCOM (Djioua et al., 2006; Alrahabi, 2010), is an automatic system for text annotation, related to discourse and semantic maps, which are specification of general linguistic ontologies founded on the Applicative and Cognitive Grammar. The annotation layer uses a linguistic method called Contextual Exploration, which handles the polysemic values of a term in texts. Several 'semantic maps' underlying 'point of views' for text mining guide this automatic annotation process. The second engine uses semantic annotated texts, produced previously in order to create a semantic inverted index, which is able to retrieve relevant documents for queries associated with discourse and semantic categories such as definition, quotation, causality, relations between concepts, etc. (Djioua & Desclés, 2007). This semantic indexation process builds a metatext layer for textual contents. Some data and linguistic rules sets as well as the general architecture that extend third-party software are expressed as supplementary information.

0.025593145 = product of:
  0.04265524 = sum of:
    0.017940165 = weight(_text_:index in 4472) [ClassicSimilarity], result of:
      0.017940165 = score(doc=4472,freq=2.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.09655905 = fieldWeight in 4472, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.015625 = fieldNorm(doc=4472)
    0.02083979 = weight(_text_:system in 4472) [ClassicSimilarity], result of:
      0.02083979 = score(doc=4472,freq=10.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.15562126 = fieldWeight in 4472, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.015625 = fieldNorm(doc=4472)
    0.0038752859 = product of:
      0.011625857 = sum of:
        0.011625857 = weight(_text_:29 in 4472) [ClassicSimilarity], result of:
          0.011625857 = score(doc=4472,freq=2.0), product of:
            0.14956595 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.04251826 = queryNorm
            0.07773064 = fieldWeight in 4472, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.015625 = fieldNorm(doc=4472)
      0.33333334 = coord(1/3)
  0.6 = coord(3/5)

Abstract

Since its appearance in the early 90's, the World Wide Web (WWW or Web) has provided universal access to knowledge and the world of information has been primarily witness to a great revolution (the digital revolution). It quickly became very popular, making it the largest and most comprehensive database and knowledge base thanks to the amount and diversity of data it contains. However, the considerable increase and evolution of these data raises important problems for users, in particular for accessing the documents most relevant to their search queries. In order to cope with this exponential explosion of data volume and facilitate their access by users, various models are offered by information retrieval systems (IRS) for the representation and retrieval of web documents. Traditional SRIs use simple keywords that are not semantically linked to index and retrieve these documents. This creates limitations in terms of the relevance and ease of exploration of results. To overcome these limitations, existing techniques enrich documents by integrating external keywords from different sources. However, these systems still suffer from limitations that are related to the exploitation techniques of these sources of enrichment. When the different sources are used so that they cannot be distinguished by the system, this limits the flexibility of the exploration models that can be applied to the results returned by this system. Users then feel lost to these results, and find themselves forced to filter them manually to select the relevant information. If they want to go further, they must reformulate and target their search queries even more until they reach the documents that best meet their expectations. In this way, even if the systems manage to find more relevant results, their presentation remains problematic. In order to target research to more user-specific information needs and improve the relevance and exploration of its research findings, advanced SRIs adopt different data personalization techniques that assume that current research of user is directly related to his profile and / or previous browsing / search experiences.
However, this assumption does not hold in all cases, the needs of the user evolve over time and can move away from his previous interests stored in his profile. In other cases, the user's profile may be misused to extract or infer new information needs. This problem is much more accentuated with ambiguous queries. When multiple POIs linked to a search query are identified in the user's profile, the system is unable to select the relevant data from that profile to respond to that request. This has a direct impact on the quality of the results provided to this user. In order to overcome some of these limitations, in this research thesis, we have been interested in the development of techniques aimed mainly at improving the relevance of the results of current SRIs and facilitating the exploration of major collections of documents. To do this, we propose a solution based on a new concept and model of indexing and information retrieval called multi-spaces projection. This proposal is based on the exploitation of different categories of semantic and social information that enrich the universe of document representation and search queries in several dimensions of interpretations. The originality of this representation is to be able to distinguish between the different interpretations used for the description and the search for documents. This gives a better visibility on the results returned and helps to provide a greater flexibility of search and exploration, giving the user the ability to navigate one or more views of data that interest him the most. In addition, the proposed multidimensional representation universes for document description and search query interpretation help to improve the relevance of the user's results by providing a diversity of research / exploration that helps meet his diverse needs and those of other different users. This study exploits different aspects that are related to the personalized search and aims to solve the problems caused by the evolution of the information needs of the user. Thus, when the profile of this user is used by our system, a technique is proposed and used to identify the interests most representative of his current needs in his profile. This technique is based on the combination of three influential factors, including the contextual, frequency and temporal factor of the data. The ability of users to interact, exchange ideas and opinions, and form social networks on the Web, has led systems to focus on the types of interactions these users have at the level of interaction between them as well as their social roles in the system. This social information is discussed and integrated into this research work. The impact and how they are integrated into the IR process are studied to improve the relevance of the results.

Date

29. 9.2018 18:57:38

0.025463944 = product of:
  0.06365986 = sum of:
    0.045020185 = product of:
      0.13506055 = sum of:
        0.13506055 = weight(_text_:3a in 5820) [ClassicSimilarity], result of:
          0.13506055 = score(doc=5820,freq=2.0), product of:
            0.3604703 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.04251826 = queryNorm
            0.3746787 = fieldWeight in 5820, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03125 = fieldNorm(doc=5820)
      0.33333334 = coord(1/3)
    0.018639674 = weight(_text_:system in 5820) [ClassicSimilarity], result of:
      0.018639674 = score(doc=5820,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.13919188 = fieldWeight in 5820, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.03125 = fieldNorm(doc=5820)
  0.4 = coord(2/5)

Abstract

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.

0.025459195 = product of:
  0.063647985 = sum of:
    0.040348392 = weight(_text_:context in 2895) [ClassicSimilarity], result of:
      0.040348392 = score(doc=2895,freq=2.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.22896172 = fieldWeight in 2895, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2895)
    0.023299592 = weight(_text_:system in 2895) [ClassicSimilarity], result of:
      0.023299592 = score(doc=2895,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.17398985 = fieldWeight in 2895, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2895)
  0.4 = coord(2/5)

Abstract

The article presents a method for automatic semantic indexing of archaeological grey-literature reports using empirical (rule-based) Information Extraction techniques in combination with domain-specific knowledge organization systems. The semantic annotation system (OPTIMA) performs the tasks of Named Entity Recognition, Relation Extraction, Negation Detection, and Word-Sense Disambiguation using hand-crafted rules and terminological resources for associating contextual abstractions with classes of the standard ontology CIDOC Conceptual Reference Model (CRM) for cultural heritage and its archaeological extension, CRM-EH. Relation Extraction (RE) performance benefits from a syntactic-based definition of RE patterns derived from domain oriented corpus analysis. The evaluation also shows clear benefit in the use of assistive natural language processing (NLP) modules relating to Word-Sense Disambiguation, Negation Detection, and Noun Phrase Validation, together with controlled thesaurus expansion. The semantic indexing results demonstrate the capacity of rule-based Information Extraction techniques to deliver interoperable semantic abstractions (semantic annotations) with respect to the CIDOC CRM and archaeological thesauri. Major contributions include recognition of relevant entities using shallow parsing NLP techniques driven by a complimentary use of ontological and terminological domain resources and empirical derivation of context-driven RE rules for the recognition of semantic relationships from phrases of unstructured text.

0.024896324 = product of:
  0.06224081 = sum of:
    0.03588033 = weight(_text_:index in 4411) [ClassicSimilarity], result of:
      0.03588033 = score(doc=4411,freq=2.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.1931181 = fieldWeight in 4411, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.03125 = fieldNorm(doc=4411)
    0.02636048 = weight(_text_:system in 4411) [ClassicSimilarity], result of:
      0.02636048 = score(doc=4411,freq=4.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.19684705 = fieldWeight in 4411, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.03125 = fieldNorm(doc=4411)
  0.4 = coord(2/5)

Abstract

This chapter describes two case studies conducted by the Swiss Life insurance group with the objective of proving the practical applicability and superiority of ontology-based knowledge management over classical approaches based on text retrieval technologies. The first case study in the domain of skills management uses manually constructed ontologies about skills, job functions and education. The purpose of the system is to give support for finding employees with certain skills. The ontologies are used to ensure that the user description of skills and the machine-held index of skills and people use the same vocabulary. The use of a shared vocabulary increases the performance of such a system significantly. The second case study aims at improving content-oriented access to passages of a 1000 page document about the International Accounting Standard on the corporate intranet. To this end, an ontology was automatically extracted from the document. It can be used to reformulate queries that turned out not to deliver the intended results. Since the ontology was automatically built, it is of a rather simple structure, consisting of weighted semantic associations between the relevant concepts in the document. We therefore call it a 'lightweight ontology'. The two case studies cover quite different aspects of using ontologies in knowledge management applications. Whereas in the second case study an ontology was automatically derived from a search space to improve information retrieval, in the first skills management case study the ontology itself introduces a structured search space. In one case study we gathered experience in building an ontology manually, while the challenge of the other case study was automatic ontology creation. A number of the novel Semantic Web-based tools described elsewhere in this book were used to build the two systems and both case studies described have led to projects to deploy live systems within Swiss Life.

0.024858627 = product of:
  0.12429313 = sum of:
    0.12429313 = weight(_text_:index in 3397) [ClassicSimilarity], result of:
      0.12429313 = score(doc=3397,freq=6.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.6689808 = fieldWeight in 3397, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0625 = fieldNorm(doc=3397)
  0.2 = coord(1/5)

Abstract

EPC Exhibit 129-36.1 presented intermediate results of a project to connect Relative Index terms to topics associated with classes and to determine if those Relative Index terms approximated the whole of the corresponding class or were in standing room in the class. The Relative Index project constitutes the first stage of a long(er)-term project to instill a more systematic treatment of relationships within the DDC. The present exhibit sets out a plan of study for that long-term project.

0.024715079 = product of:
  0.061787695 = sum of:
    0.052099477 = weight(_text_:system in 3576) [ClassicSimilarity], result of:
      0.052099477 = score(doc=3576,freq=10.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.38905317 = fieldWeight in 3576, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3576)
    0.009688215 = product of:
      0.029064644 = sum of:
        0.029064644 = weight(_text_:29 in 3576) [ClassicSimilarity], result of:
          0.029064644 = score(doc=3576,freq=2.0), product of:
            0.14956595 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.04251826 = queryNorm
            0.19432661 = fieldWeight in 3576, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3576)
      0.33333334 = coord(1/3)
  0.4 = coord(2/5)

Abstract

This paper presents a proposal for the long-range development of an open, multifunctional, multilingual system for integrated access to many kinds of knowledge about concepts and terminology. The system would draw on existing knowledge bases that are accessible through the Internet or on CD-ROM an on a common integrated distributed knowledge base that would grow incrementally over time. Existing knowledge bases would be accessed through a common interface that would search several knowledge bases, collate the data into a common format, and present them to the user. The common integrated distributed knowledge base would provide an environment in which many contributors could carry out classification and terminological projects more efficiently, with the results available in a common format. Over time, data from other knowledge bases could be incorporated into the common knowledge base, either by actual transfer (provided the knowledge base producers are willing) or by reference through a link. Either way, such incorporation requires intellectual work but allows for tighter integration than common interface access to multiple knowledge bases. Each piece of information in the common knowledge base will have all its sources attached, providing an acknowledgment mechanism that gives due credit to all contributors. The whole system woul be designed to be usable by many levels of users for improved information exchange.

Content

Expanded version of a paper published in Advances in Knowledge Organization v.5 (1996): 165-173 (4th Annual ISKO Conference, Washington, D.C., 1996 July 15-18): SemWeb: proposal for an open, multifunctional, multilingual system for integrated access to knowledge about concepts and terminology.

Date

15. 6.2010 19:25:29

0.02397943 = product of:
  0.05994857 = sum of:
    0.048427295 = weight(_text_:system in 3261) [ClassicSimilarity], result of:
      0.048427295 = score(doc=3261,freq=6.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.36163113 = fieldWeight in 3261, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.046875 = fieldNorm(doc=3261)
    0.011521274 = product of:
      0.03456382 = sum of:
        0.03456382 = weight(_text_:22 in 3261) [ClassicSimilarity], result of:
          0.03456382 = score(doc=3261,freq=2.0), product of:
            0.1488917 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04251826 = 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)
      0.33333334 = coord(1/3)
  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

0.023877738 = product of:
  0.059694342 = sum of:
    0.04613084 = weight(_text_:system in 4471) [ClassicSimilarity], result of:
      0.04613084 = score(doc=4471,freq=4.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.34448233 = fieldWeight in 4471, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4471)
    0.013563501 = product of:
      0.0406905 = sum of:
        0.0406905 = weight(_text_:29 in 4471) [ClassicSimilarity], result of:
          0.0406905 = score(doc=4471,freq=2.0), product of:
            0.14956595 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.04251826 = queryNorm
            0.27205724 = fieldWeight in 4471, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4471)
      0.33333334 = coord(1/3)
  0.4 = coord(2/5)

Abstract

We present an Information Retrieval System for scientific publications that provides the possibility to filter results according to semantic facets. We use sentence-level semantic annotations that identify specific semantic relations in texts, such as methods, definitions, hypotheses, that correspond to common information needs related to scientific literature. The semantic annotations are obtained using a rule-based method that identifies linguistic clues organized into a linguistic ontology. The system is implemented using Solr Search Server and offers efficient search and navigation in scientific papers.

Source

Semantic Web Evaluation Challenge. SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers. Eds.: V. Presutti et al

0.023455678 = product of:
  0.05863919 = sum of:
    0.032278713 = weight(_text_:context in 4402) [ClassicSimilarity], result of:
      0.032278713 = score(doc=4402,freq=2.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.18316938 = fieldWeight in 4402, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.03125 = fieldNorm(doc=4402)
    0.02636048 = weight(_text_:system in 4402) [ClassicSimilarity], result of:
      0.02636048 = score(doc=4402,freq=4.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.19684705 = fieldWeight in 4402, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.03125 = fieldNorm(doc=4402)
  0.4 = coord(2/5)

Abstract

Ontologies need to be stored, sometimes aligned and their evolution needs to be managed. All these tasks together are called ontology management. Alignment is a central task in ontology re-use. Re-use of existing ontologies often requires considerable effort: the ontologies either need to be integrated, which means that they are merged into one new ontology, or the ontologies can be kept separate. In both cases, the ontologies have to be aligned, which means that they have to be brought into mutual agreement. The problems that underlie the difficulties in integrating and aligning are the mismatches that may exist between separate ontologies. Ontologies can differ at the language level, which can mean that they are represented in a different syntax, or that the expressiveness of the ontology language is dissimilar. Ontologies also can have mismatches at the model level, for example, in the paradigm, or modelling style. Ontology alignment is very relevant in a Semantic Web context. The Semantic Web will provide us with a lot of freely accessible domain specific ontologies. To form a real web of semantics - which will allow computers to combine and infer implicit knowledge - those separate ontologies should be aligned and linked.
Support for evolving ontologies is required in almost all situations where ontologies are used in real-world applications. In those cases, ontologies are often developed by several persons and will continue to evolve over time, because of changes in the real world, adaptations to different tasks, or alignments to other ontologies. To prevent that such changes will invalidate existing usage, a change management methodology is needed. This involves advanced versioning methods for the development and the maintenance of ontologies, but also configuration management, that takes care of the identification, relations and interpretation of ontology versions. All these aspects come together in integrated ontology library systems. When the number of different ontologies is increasing, the task of storing, maintaining and re-organizing them to secure the successful re-use of ontologies is challenging. Ontology library systems can help in the grouping and reorganizing ontologies for further re-use, integration, maintenance, mapping and versioning. Basically, a library system offers various functions for managing, adapting and standardizing groups of ontologies. Such integrated systems are a requirement for the Semantic Web to grow further and scale up. In this chapter, we describe a number of results with respect to the above mentioned areas. We start with a description of the alignment task and show a meta-ontology that is developed to specify the mappings. Then, we discuss the problems that are caused by evolving ontologies and describe two important elements of a change management methodology. Finally, in Section 4.4 we survey existing library systems and formulate a wish-list of features of an ontology library system.

0.022597253 = product of:
  0.056493133 = sum of:
    0.028243875 = weight(_text_:context in 4409) [ClassicSimilarity], result of:
      0.028243875 = score(doc=4409,freq=2.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.16027321 = fieldWeight in 4409, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.02734375 = fieldNorm(doc=4409)
    0.028249256 = weight(_text_:system in 4409) [ClassicSimilarity], result of:
      0.028249256 = score(doc=4409,freq=6.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.21095149 = fieldWeight in 4409, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.02734375 = fieldNorm(doc=4409)
  0.4 = coord(2/5)

Abstract

We saw in the introduction how the Semantic Web makes possible a new generation of knowledge management tools. We now turn our attention more specifically to Semantic Web based support for virtual communities of practice. The notion of communities of practice has attracted much attention in the field of knowledge management. Communities of practice are groups within (or sometimes across) organizations who share a common set of information needs or problems. They are typically not a formal organizational unit but an informal network, each sharing in part a common agenda and shared interests or issues. In one example it was found that a lot of knowledge sharing among copier engineers took place through informal exchanges, often around a water cooler. As well as local, geographically based communities, trends towards flexible working and globalisation have led to interest in supporting dispersed communities using Internet technology. The challenge for organizations is to support such communities and make them effective. Provided with an ontology meeting the needs of a particular community of practice, knowledge management tools can arrange knowledge assets into the predefined conceptual classes of the ontology, allowing more natural and intuitive access to knowledge. Knowledge management tools must give users the ability to organize information into a controllable asset. Building an intranet-based store of information is not sufficient for knowledge management; the relationships within the stored information are vital. These relationships cover such diverse issues as relative importance, context, sequence, significance, causality and association. The potential for knowledge management tools is vast; not only can they make better use of the raw information already available, but they can sift, abstract and help to share new information, and present it to users in new and compelling ways.
In this chapter, we describe the OntoShare system which facilitates and encourages the sharing of information between communities of practice within (or perhaps across) organizations and which encourages people - who may not previously have known of each other's existence in a large organization - to make contact where there are mutual concerns or interests. As users contribute information to the community, a knowledge resource annotated with meta-data is created. Ontologies defined using the resource description framework (RDF) and RDF Schema (RDFS) are used in this process. RDF is a W3C recommendation for the formulation of meta-data for WWW resources. RDF(S) extends this standard with the means to specify domain vocabulary and object structures - that is, concepts and the relationships that hold between them. In the next section, we describe in detail the way in which OntoShare can be used to share and retrieve knowledge and how that knowledge is represented in an RDF-based ontology. We then proceed to discuss in Section 10.3 how the ontologies in OntoShare evolve over time based on user interaction with the system and motivate our approach to user-based creation of RDF-annotated information resources. The way in which OntoShare can help to locate expertise within an organization is then described, followed by a discussion of the sociotechnical issues of deploying such a tool. Finally, a planned evaluation exercise and avenues for further research are outlined.

0.022501035 = product of:
  0.056252588 = sum of:
    0.03994287 = weight(_text_:context in 4803) [ClassicSimilarity], result of:
      0.03994287 = score(doc=4803,freq=4.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.22666055 = fieldWeight in 4803, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.02734375 = fieldNorm(doc=4803)
    0.016309716 = weight(_text_:system in 4803) [ClassicSimilarity], result of:
      0.016309716 = score(doc=4803,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.1217929 = fieldWeight in 4803, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.02734375 = fieldNorm(doc=4803)
  0.4 = coord(2/5)

Abstract

The Linked Data initiative and the state of the art in semantic technologies led off all brand new search and mash-up applications. The basic idea is to have smarter lookup services for a huge, distributed and social knowledge base. All these applications catch and (re)propose, under a semantic data perspective, the view of the classical Web as a distributed collection of documents to retrieve. The interlinked nature of the Web, and consequently of the Semantic Web, is exploited (just) to collect and aggregate data coming from different sources. Of course, this is a big step forward in search and Web technologies, but if we limit our investi- gation to retrieval tasks, we miss another important feature of the current Web: browsing and in particular exploratory browsing (a.k.a. exploratory search). Thanks to its hyperlinked nature, the Web defined a new way of browsing documents and knowledge: selection by lookup, navigation and trial-and-error tactics were, and still are, exploited by users to search for relevant information satisfying some initial requirements. The basic assumptions behind a lookup search, typical of Information Retrieval (IR) systems, are no more valid in an exploratory browsing context. An IR system, such as a search engine, assumes that: the user has a clear picture of what she is looking for ; she knows the terminology of the specific knowledge space. On the other side, as argued in, the main challenges in exploratory search can be summarized as: support querying and rapid query refinement; other facets and metadata-based result filtering; leverage search context; support learning and understanding; other visualization to support insight/decision making; facilitate collaboration. In Section 3 we will show two applications for exploratory search in the Semantic Web addressing some of the above challenges.

0.020367354 = product of:
  0.050918385 = sum of:
    0.032278713 = weight(_text_:context in 578) [ClassicSimilarity], result of:
      0.032278713 = score(doc=578,freq=2.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.18316938 = fieldWeight in 578, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.03125 = fieldNorm(doc=578)
    0.018639674 = weight(_text_:system in 578) [ClassicSimilarity], result of:
      0.018639674 = score(doc=578,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.13919188 = fieldWeight in 578, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.03125 = fieldNorm(doc=578)
  0.4 = coord(2/5)

Abstract

This research explores current controversies within country dance communities and the implications of cultural and ethical issues related to representation of gender and race in a KOS for an ICH, while investigating the importance of context and the applicability of semantic approaches in the implementation of synonym rings. During development of a controlled vocabulary to represent dance concepts for country dance choreography, this study encountered and considered the importance of history and culture regarding synonymous and near-synonymous terms used to describe dance roles and choreographic elements. A subset of names for the same choreographic concepts across four subdomains of country dance (English country dance, Scottish country dance, contra dance, and modern western square dance) were used as a case study. These concepts included traditionally gendered dance roles and choreographic terms with a racially pejorative history. Through the lens of existing research on ethical knowl­edge organization, this study focused on principles and methods of transparency, multivocality, cultural warrant, cultural hospitality, and intersectionality to conduct a domain analysis of country dance resources. The analysis revealed differing levels of engagement and distinction among dance practitioners and communities for their preferences to use different terms for the same concept. Various lexical, grammatical, affective, social, political, and cultural aspects also emerged as important contextual factors for the use and assignment of terms. As a result, this study proposes the use of semantic annotation to represent those contextual factors and to allow mechanisms of user choice in the design of a country dance knowl­edge organization system. Future research arising from this study would focus on expanding examination to other country dance genres and continued exploration of the use of semantic approaches to represent contextual factors in controlled vocabulary development.

0.020296982 = product of:
  0.10148491 = sum of:
    0.10148491 = weight(_text_:index in 436) [ClassicSimilarity], result of:
      0.10148491 = score(doc=436,freq=4.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.5462205 = fieldWeight in 436, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0625 = fieldNorm(doc=436)
  0.2 = coord(1/5)

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?

0.020014644 = product of:
  0.05003661 = sum of:
    0.040348392 = weight(_text_:context in 2393) [ClassicSimilarity], result of:
      0.040348392 = score(doc=2393,freq=2.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.22896172 = fieldWeight in 2393, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2393)
    0.009688215 = product of:
      0.029064644 = sum of:
        0.029064644 = weight(_text_:29 in 2393) [ClassicSimilarity], result of:
          0.029064644 = score(doc=2393,freq=2.0), product of:
            0.14956595 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.04251826 = queryNorm
            0.19432661 = fieldWeight in 2393, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2393)
      0.33333334 = coord(1/3)
  0.4 = coord(2/5)

Abstract

This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases.

Date

9.11.2008 13:07:29

0.018424368 = product of:
  0.04606092 = sum of:
    0.03261943 = weight(_text_:system in 5095) [ClassicSimilarity], result of:
      0.03261943 = score(doc=5095,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.2435858 = fieldWeight in 5095, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5095)
    0.013441487 = product of:
      0.04032446 = sum of:
        0.04032446 = weight(_text_:22 in 5095) [ClassicSimilarity], result of:
          0.04032446 = score(doc=5095,freq=2.0), product of:
            0.1488917 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04251826 = 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)
  0.4 = coord(2/5)

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

0.018424368 = product of:
  0.04606092 = sum of:
    0.03261943 = weight(_text_:system in 2654) [ClassicSimilarity], result of:
      0.03261943 = score(doc=2654,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.2435858 = fieldWeight in 2654, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2654)
    0.013441487 = product of:
      0.04032446 = sum of:
        0.04032446 = weight(_text_:22 in 2654) [ClassicSimilarity], result of:
          0.04032446 = score(doc=2654,freq=2.0), product of:
            0.1488917 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04251826 = 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)
  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

0.017591758 = product of:
  0.043979395 = sum of:
    0.024209036 = weight(_text_:context in 1169) [ClassicSimilarity], result of:
      0.024209036 = score(doc=1169,freq=2.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.13737704 = fieldWeight in 1169, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.0234375 = fieldNorm(doc=1169)
    0.01977036 = weight(_text_:system in 1169) [ClassicSimilarity], result of:
      0.01977036 = score(doc=1169,freq=4.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.14763528 = fieldWeight in 1169, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0234375 = fieldNorm(doc=1169)
  0.4 = coord(2/5)

Abstract

T.2: The ability to identify and locate relevant information among vast collections and other resources is a major and pressing challenge today. Several different types of vocabulary are in use for this purpose. Some of the most widely used vocabularies were designed a hundred years ago and have been evolving steadily. A different generation of vocabularies is now emerging, designed to exploit the electronic media more effectively. A good understanding of the previous generation is still essential for effective access to collections indexed with them. An important object of ISO 25964 as a whole is to support data exchange and other forms of interoperability in circumstances in which more than one structured vocabulary is applied within one retrieval system or network. Sometimes one vocabulary has to be mapped to another, and it is important to understand both the potential and the limitations of such mappings. In other systems, a thesaurus is mapped to a classification scheme, or an ontology to a thesaurus. Comprehensive interoperability needs to cover the whole range of vocabulary types, whether young or old. Concepts in different vocabularies are related only in that they have the same or similar meaning. However, the meaning can be found in a number of different aspects within each particular type of structured vocabulary: - within terms or captions selected in different languages; - in the notation assigned indicating a place within a larger hierarchy; - in the definition, scope notes, history notes and other notes that explain the significance of that concept; and - in explicit relationships to other concepts or entities within the same vocabulary. In order to create mappings from one structured vocabulary to another it is first necessary to understand, within the context of each different type of structured vocabulary, the significance and relative importance of each of the different elements in defining the meaning of that particular concept. ISO 25964-1 describes the key characteristics of thesauri along with additional advice on best practice. ISO 25964-2 focuses on other types of vocabulary and does not attempt to cover all aspects of good practice. It concentrates on those aspects which need to be understood if one of the vocabularies is to work effectively alongside one or more of the others. Recognizing that a new standard cannot be applied to some existing vocabularies, this part of ISO 25964 provides informative description alongside the recommendations, the aim of which is to enable users and system developers to interpret and implement the existing vocabularies effectively. The remainder of ISO 25964-2 deals with the principles and practicalities of establishing mappings between vocabularies.