Search (21 results, page 1 of 2)

  • × theme_ss:"Wissensrepräsentation"
  • × theme_ss:"Computerlinguistik"
  1. Kunze, C.: Lexikalisch-semantische Wortnetze in Sprachwissenschaft und Sprachtechnologie (2006) 0.02 
    0.02147556 = product of:
  0.064426675 = sum of:
    0.014278769 = weight(_text_:in in 6023) [ClassicSimilarity], result of:
      0.014278769 = score(doc=6023,freq=8.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.24046129 = fieldWeight in 6023, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0625 = fieldNorm(doc=6023)
    0.050147906 = weight(_text_:und in 6023) [ClassicSimilarity], result of:
      0.050147906 = score(doc=6023,freq=14.0), product of:
        0.09675359 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.043654136 = queryNorm
        0.51830536 = fieldWeight in 6023, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0625 = fieldNorm(doc=6023)
  0.33333334 = coord(2/6)
    Abstract
    Dieser Beitrag beschreibt die Strukturierungsprinzipien und Anwendungskontexte lexikalisch-semantischer Wortnetze, insbesondere des deutschen Wortnetzes GermaNet. Wortnetze sind zurzeit besonders populäre elektronische Lexikonressourcen, die große Abdeckungen semantisch strukturierter Datenfür verschiedene Sprachen und Sprachverbünde enthalten. In Wortnetzen sind die häufigsten und wichtigsten Konzepte einer Sprache mit ihren elementaren Bedeutungsrelationen repräsentiert. Zentrale Anwendungen für Wortnetze sind u.a. die Lesartendisambiguierung und die Informationserschließung. Der Artikel skizziert die neusten Szenarien, in denen GermaNet eingesetzt wird: die Semantische Informationserschließung und die Integration allgemeinsprachlicher Wortnetze mit terminologischen Ressourcen vordem Hintergrund der Datenkonvertierung in OWL.
    Source
    Information - Wissenschaft und Praxis. 57(2006) H.6/7, S.309-314
  2. Helbig, H.: Wissensverarbeitung und die Semantik der natürlichen Sprache : Wissensrepräsentation mit MultiNet (2008) 0.01 
    0.012550942 = product of:
  0.037652824 = sum of:
    0.006310384 = weight(_text_:in in 2731) [ClassicSimilarity], result of:
      0.006310384 = score(doc=2731,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.10626988 = fieldWeight in 2731, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2731)
    0.03134244 = weight(_text_:und in 2731) [ClassicSimilarity], result of:
      0.03134244 = score(doc=2731,freq=14.0), product of:
        0.09675359 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.043654136 = queryNorm
        0.32394084 = fieldWeight in 2731, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2731)
  0.33333334 = coord(2/6)
    Abstract
    Das Buch gibt eine umfassende Darstellung einer Methodik zur Interpretation und Bedeutungsrepräsentation natürlichsprachlicher Ausdrücke. Diese Methodik der "Mehrschichtigen Erweiterten Semantischen Netze", das sogenannte MultiNet-Paradigma, ist sowohl für theoretische Untersuchungen als auch für die automatische Verarbeitung natürlicher Sprache auf dem Rechner geeignet. Im ersten Teil des zweiteiligen Buches werden grundlegende Probleme der semantischen Repräsentation von Wissen bzw. der semantischen Interpretation natürlichsprachlicher Phänomene behandelt. Der zweite Teil enthält eine systematische Zusammenstellung des gesamten Repertoires von Darstellungsmitteln, die jeweils nach einem einheitlichen Schema beschrieben werden. Er dient als Kompendium der im Buch verwendeten formalen Beschreibungsmittel von MultiNet. Die vorgestellten Ergebnisse sind eingebettet in ein System von Software-Werkzeugen, die eine praktische Nutzung der MultiNet-Darstellungsmittel als Formalismus zur Bedeutungsrepräsentation im Rahmen der automatischen Sprachverarbeitung sichern. Hierzu gehören: eine Werkbank für den Wissensingenieur, ein Übersetzungssystem zur automatischen Gewinnung von Bedeutungsdarstellungen natürlichsprachlicher Sätze und eine Werkbank für den Computerlexikographen. Der Inhalt des Buches beruht auf jahrzehntelanger Forschung auf dem Gebiet der automatischen Sprachverarbeitung und wurde mit Vorlesungen zur Künstlichen Intelligenz und Wissensverarbeitung an der TU Dresden und der FernUniversität Hagen wiederholt in der Hochschullehre eingesetzt. Als Vorkenntnisse werden beim Leser lediglich Grundlagen der traditionellen Grammatik und elementare Kenntnisse der Prädikatenlogik vorausgesetzt.
  3. Hodgson, J.P.E.: Knowledge representation and language in AI (1991) 0.01 
    0.009227715 = product of:
  0.027683146 = sum of:
    0.010929906 = weight(_text_:in in 1529) [ClassicSimilarity], result of:
      0.010929906 = score(doc=1529,freq=12.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.18406484 = fieldWeight in 1529, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1529)
    0.01675324 = weight(_text_:und in 1529) [ClassicSimilarity], result of:
      0.01675324 = score(doc=1529,freq=4.0), product of:
        0.09675359 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.043654136 = queryNorm
        0.17315367 = fieldWeight in 1529, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1529)
  0.33333334 = coord(2/6)
    Abstract
    The aim of this book is to highlight the relationship between knowledge representation and language in artificial intelligence, and in particular on the way in which the choice of representation influences the language used to discuss a problem - and vice versa. Opening with a discussion of knowledge representation methods, and following this with a look at reasoning methods, the author begins to make his case for the intimate relationship between language and representation. He shows how each representation method fits particularly well with some reasoning methods and less so with others, using specific languages as examples. The question of representation change, an important and complex issue about which very little is known, is addressed. Dr Hodgson gathers together recent work on problem solving, showing how, in some cases, it has been possible to use representation changes to recast problems into a language that makes them easier to solve. The author maintains throughout that the relationships that this book explores lie at the heart of the construction of large systems, examining a number of the current large AI systems from the viewpoint of representation and language to prove his point.
    Classification
    ST 285 Informatik / Monographien / Software und -entwicklung / Computer supported cooperative work (CSCW), Groupware
    RVK
    ST 285 Informatik / Monographien / Software und -entwicklung / Computer supported cooperative work (CSCW), Groupware
    Series
    Ellis Horwood series in artificial intelligence
  4. Helbig, H.: ¬Die semantische Struktur natürlicher Sprache : Wissensrepräsentation mit MultiNet (2001) 0.01 
    0.008697838 = product of:
  0.02609351 = sum of:
    0.0071393843 = weight(_text_:in in 7072) [ClassicSimilarity], result of:
      0.0071393843 = score(doc=7072,freq=2.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.120230645 = fieldWeight in 7072, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0625 = fieldNorm(doc=7072)
    0.018954126 = weight(_text_:und in 7072) [ClassicSimilarity], result of:
      0.018954126 = score(doc=7072,freq=2.0), product of:
        0.09675359 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.043654136 = queryNorm
        0.19590102 = fieldWeight in 7072, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.0625 = fieldNorm(doc=7072)
  0.33333334 = coord(2/6)
    Abstract
    Die Methodik der 'Mehrschichtigen Erweiterung Semantischer Netze' (MultiNet) ist sowohl für theoretische Untersuchungen als auch für die automatische Verarbeitung natürlicher Sprache auf dem Rechner geeignet. Die vorgestellten Ergebnisse sind eingebettet in ein System von Software-Werkzeugen, die eine praktische Nutzung der MultiNet-Darstellungsmittel als Formalismus zur Bedeutungsrepräsentation sichern
    Footnote
    2. Aufl. 2008 u.d.T.: Wissensverarbeitung und die Semantik der natürlichen Sprache
  5. Cimiano, P.; Völker, J.; Studer, R.: Ontologies on demand? : a description of the state-of-the-art, applications, challenges and trends for ontology learning from text (2006) 0.01 
    0.007829976 = product of:
  0.023489928 = sum of:
    0.009274333 = weight(_text_:in in 6014) [ClassicSimilarity], result of:
      0.009274333 = score(doc=6014,freq=6.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.1561842 = fieldWeight in 6014, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=6014)
    0.014215595 = weight(_text_:und in 6014) [ClassicSimilarity], result of:
      0.014215595 = score(doc=6014,freq=2.0), product of:
        0.09675359 = queryWeight, product of:
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.043654136 = queryNorm
        0.14692576 = fieldWeight in 6014, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.216367 = idf(docFreq=13101, maxDocs=44218)
          0.046875 = fieldNorm(doc=6014)
  0.33333334 = coord(2/6)
    Abstract
    Ontologies are nowadays used for many applications requiring data, services and resources in general to be interoperable and machine understandable. Such applications are for example web service discovery and composition, information integration across databases, intelligent search, etc. The general idea is that data and services are semantically described with respect to ontologies, which are formal specifications of a domain of interest, and can thus be shared and reused in a way such that the shared meaning specified by the ontology remains formally the same across different parties and applications. As the cost of creating ontologies is relatively high, different proposals have emerged for learning ontologies from structured and unstructured resources. In this article we examine the maturity of techniques for ontology learning from textual resources, addressing the question whether the state-of-the-art is mature enough to produce ontologies 'on demand'.
    Source
    Information - Wissenschaft und Praxis. 57(2006) H.6/7, S.315-320
  6. Rindflesch, T.C.; Fizsman, M.: The interaction of domain knowledge and linguistic structure in natural language processing : interpreting hypernymic propositions in biomedical text (2003) 0.00 
    0.0025762038 = product of:
  0.015457222 = sum of:
    0.015457222 = weight(_text_:in in 2097) [ClassicSimilarity], result of:
      0.015457222 = score(doc=2097,freq=24.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.260307 = fieldWeight in 2097, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2097)
  0.16666667 = coord(1/6)
    Abstract
    Interpretation of semantic propositions in free-text documents such as MEDLINE citations would provide valuable support for biomedical applications, and several approaches to semantic interpretation are being pursued in the biomedical informatics community. In this paper, we describe a methodology for interpreting linguistic structures that encode hypernymic propositions, in which a more specific concept is in a taxonomic relationship with a more general concept. In order to effectively process these constructions, we exploit underspecified syntactic analysis and structured domain knowledge from the Unified Medical Language System (UMLS). After introducing the syntactic processing on which our system depends, we focus on the UMLS knowledge that supports interpretation of hypernymic propositions. We first use semantic groups from the Semantic Network to ensure that the two concepts involved are compatible; hierarchical information in the Metathesaurus then determines which concept is more general and which more specific. A preliminary evaluation of a sample based on the semantic group Chemicals and Drugs provides 83% precision. An error analysis was conducted and potential solutions to the problems encountered are presented. The research discussed here serves as a paradigm for investigating the interaction between domain knowledge and linguistic structure in natural language processing, and could also make a contribution to research on automatic processing of discourse structure. Additional implications of the system we present include its integration in advanced semantic interpretation processors for biomedical text and its use for information extraction in specific domains. The approach has the potential to support a range of applications, including information retrieval and ontology engineering.
  7. Rindflesch, T.C.; Aronson, A.R.: Semantic processing in information retrieval (1993) 0.00 
    0.0020823204 = product of:
  0.012493922 = sum of:
    0.012493922 = weight(_text_:in in 4121) [ClassicSimilarity], result of:
      0.012493922 = score(doc=4121,freq=8.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.21040362 = fieldWeight in 4121, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4121)
  0.16666667 = coord(1/6)
    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.
  8. Wong, W.; Liu, W.; Bennamoun, M.: Ontology learning from text : a look back and into the future (2010) 0.00 
    0.0020823204 = product of:
  0.012493922 = sum of:
    0.012493922 = weight(_text_:in in 4733) [ClassicSimilarity], result of:
      0.012493922 = score(doc=4733,freq=8.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.21040362 = fieldWeight in 4733, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4733)
  0.16666667 = coord(1/6)
    Abstract
    Ontologies are often viewed as the answer to the need for inter-operable semantics in modern information systems. The explosion of textual information on the "Read/Write" Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas such as natural language processing have fuelled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium, and discusses the remaining challenges that will define the research directions in this area in the near future.
  9. Collard, J.; Paiva, V. de; Fong, B.; Subrahmanian, E.: Extracting mathematical concepts from text (2022) 0.00 
    0.0020823204 = product of:
  0.012493922 = sum of:
    0.012493922 = weight(_text_:in in 668) [ClassicSimilarity], result of:
      0.012493922 = score(doc=668,freq=8.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.21040362 = fieldWeight in 668, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0546875 = fieldNorm(doc=668)
  0.16666667 = coord(1/6)
    Abstract
    We investigate different systems for extracting mathematical entities from English texts in the mathematical field of category theory as a first step for constructing a mathematical knowledge graph. We consider four different term extractors and compare their results. This small experiment showcases some of the issues with the construction and evaluation of terms extracted from noisy domain text. We also make available two open corpora in research mathematics, in particular in category theory: a small corpus of 755 abstracts from the journal TAC (3188 sentences), and a larger corpus from the nLab community wiki (15,000 sentences).
  10. Helbig, H.: Knowledge representation and the semantics of natural language (2014) 0.00 
    0.001821651 = product of:
  0.010929906 = sum of:
    0.010929906 = weight(_text_:in in 2396) [ClassicSimilarity], result of:
      0.010929906 = score(doc=2396,freq=12.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.18406484 = fieldWeight in 2396, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2396)
  0.16666667 = coord(1/6)
    Abstract
    Natural Language is not only the most important means of communication between human beings, it is also used over historical periods for the preservation of cultural achievements and their transmission from one generation to the other. During the last few decades, the flod of digitalized information has been growing tremendously. This tendency will continue with the globalisation of information societies and with the growing importance of national and international computer networks. This is one reason why the theoretical understanding and the automated treatment of communication processes based on natural language have such a decisive social and economic impact. In this context, the semantic representation of knowledge originally formulated in natural language plays a central part, because it connects all components of natural language processing systems, be they the automatic understanding of natural language (analysis), the rational reasoning over knowledge bases, or the generation of natural language expressions from formal representations. This book presents a method for the semantic representation of natural language expressions (texts, sentences, phrases, etc.) which can be used as a universal knowledge representation paradigm in the human sciences, like linguistics, cognitive psychology, or philosophy of language, as well as in computational linguistics and in artificial intelligence. It is also an attempt to close the gap between these disciplines, which to a large extent are still working separately.
    Footnote
    Vgl. auch die anderen Ausgabe in dt. u. engl. Sprache
  11. Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021) 0.00 
    0.0018033426 = product of:
  0.010820055 = sum of:
    0.010820055 = weight(_text_:in in 667) [ClassicSimilarity], result of:
      0.010820055 = score(doc=667,freq=6.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.1822149 = fieldWeight in 667, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0546875 = fieldNorm(doc=667)
  0.16666667 = coord(1/6)
    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.
  12. Mustafa El Hadi, W.: Terminologies, ontologies and information access (2006) 0.00 
    0.001682769 = product of:
  0.010096614 = sum of:
    0.010096614 = weight(_text_:in in 1488) [ClassicSimilarity], result of:
      0.010096614 = score(doc=1488,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.17003182 = fieldWeight in 1488, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0625 = fieldNorm(doc=1488)
  0.16666667 = coord(1/6)
    Abstract
    Ontologies have become an important issue in research communities across several disciplines. This paper discusses some of the innovative techniques involving automatic terminology resources acquisition are briefly discussed. Suggests that NLP-based ontologies are useful in reducing the cost of ontology engineering. Emphasizes that linguistic ontologies covering both ontological and lexical information can offer solutions since they can be more easily updated by the resources of NLP products.
  13. Clark, M.; Kim, Y.; Kruschwitz, U.; Song, D.; Albakour, D.; Dignum, S.; Beresi, U.C.; Fasli, M.; Roeck, A De: Automatically structuring domain knowledge from text : an overview of current research (2012) 0.00 
    0.0015457221 = product of:
  0.009274333 = sum of:
    0.009274333 = weight(_text_:in in 2738) [ClassicSimilarity], result of:
      0.009274333 = score(doc=2738,freq=6.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.1561842 = fieldWeight in 2738, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=2738)
  0.16666667 = coord(1/6)
    Abstract
    This paper presents an overview of automatic methods for building domain knowledge structures (domain models) from text collections. Applications of domain models have a long history within knowledge engineering and artificial intelligence. In the last couple of decades they have surfaced noticeably as a useful tool within natural language processing, information retrieval and semantic web technology. Inspired by the ubiquitous propagation of domain model structures that are emerging in several research disciplines, we give an overview of the current research landscape and some techniques and approaches. We will also discuss trade-offs between different approaches and point to some recent trends.
    Content
    Beitrag in einem Themenheft "Soft Approaches to IA on the Web". Vgl.: doi:10.1016/j.ipm.2011.07.002.
  14. Rajasurya, S.; Muralidharan, T.; Devi, S.; Swamynathan, S.: Semantic information retrieval using ontology in university domain (2012) 0.00 
    0.0012881019 = product of:
  0.007728611 = sum of:
    0.007728611 = weight(_text_:in in 2861) [ClassicSimilarity], result of:
      0.007728611 = score(doc=2861,freq=6.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.1301535 = fieldWeight in 2861, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2861)
  0.16666667 = coord(1/6)
    Abstract
    Today's conventional search engines hardly do provide the essential content relevant to the user's search query. This is because the context and semantics of the request made by the user is not analyzed to the full extent. So here the need for a semantic web search arises. SWS is upcoming in the area of web search which combines Natural Language Processing and Artificial Intelligence. The objective of the work done here is to design, develop and implement a semantic search engine- SIEU(Semantic Information Extraction in University Domain) confined to the university domain. SIEU uses ontology as a knowledge base for the information retrieval process. It is not just a mere keyword search. It is one layer above what Google or any other search engines retrieve by analyzing just the keywords. Here the query is analyzed both syntactically and semantically. The developed system retrieves the web results more relevant to the user query through keyword expansion. The results obtained here will be accurate enough to satisfy the request made by the user. The level of accuracy will be enhanced since the query is analyzed semantically. The system will be of great use to the developers and researchers who work on web. The Google results are re-ranked and optimized for providing the relevant links. For ranking an algorithm has been applied which fetches more apt results for the user query.
  15. Rosemblat, G.; Resnick, M.P.; Auston, I.; Shin, D.; Sneiderman, C.; Fizsman, M.; Rindflesch, T.C.: Extending SemRep to the public health domain (2013) 0.00 
    0.0012620769 = product of:
  0.0075724614 = sum of:
    0.0075724614 = weight(_text_:in in 2096) [ClassicSimilarity], result of:
      0.0075724614 = score(doc=2096,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.12752387 = fieldWeight in 2096, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=2096)
  0.16666667 = coord(1/6)
    Abstract
    We describe the use of a domain-independent method to extend a natural language processing (NLP) application, SemRep (Rindflesch, Fiszman, & Libbus, 2005), based on the knowledge sources afforded by the Unified Medical Language System (UMLS®; Humphreys, Lindberg, Schoolman, & Barnett, 1998) to support the area of health promotion within the public health domain. Public health professionals require good information about successful health promotion policies and programs that might be considered for application within their own communities. Our effort seeks to improve access to relevant information for the public health profession, to help those in the field remain an information-savvy workforce. Natural language processing and semantic techniques hold promise to help public health professionals navigate the growing ocean of information by organizing and structuring this knowledge into a focused public health framework paired with a user-friendly visualization application as a way to summarize results of PubMed® searches in this field of knowledge.
  16. Wright, L.W.; Nardini, H.K.G.; Aronson, A.R.; Rindflesch, T.C.: Hierarchical concept indexing of full-text documents in the Unified Medical Language System Information sources Map (1999) 0.00 
    0.0012620769 = product of:
  0.0075724614 = sum of:
    0.0075724614 = weight(_text_:in in 2111) [ClassicSimilarity], result of:
      0.0075724614 = score(doc=2111,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.12752387 = fieldWeight in 2111, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=2111)
  0.16666667 = coord(1/6)
    Abstract
    Full-text documents are a vital and rapidly growing part of online biomedical information. A single large document can contain as much information as a small database, but normally lacks the tight structure and consistent indexing of a database. Retrieval systems will often miss highly relevant parts of a document if the document as a whole appears irrelevant. Access to full-text information is further complicated by the need to search separately many disparate information resources. This research explores how these problems can be addressed by the combined use of 2 techniques: 1) natural language processing for automatic concept-based indexing of full text, and 2) methods for exploiting the structure and hierarchy of full-text documents. We describe methods for applying these techniques to a large collection of full-text documents drawn from the Health Services / Technology Assessment Text (HSTAT) database at the NLM and examine how this hierarchical concept indexing can assist both document- and source-level retrieval in the context of NLM's Information Source Map project
  17. Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024) 0.00 
    0.0012620769 = product of:
  0.0075724614 = sum of:
    0.0075724614 = weight(_text_:in in 1205) [ClassicSimilarity], result of:
      0.0075724614 = score(doc=1205,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.12752387 = fieldWeight in 1205, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=1205)
  0.16666667 = coord(1/6)
    Abstract
    Detecting science-technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.
  18. Vlachidis, A.; Binding, C.; Tudhope, D.; May, K.: Excavating grey literature : a case study on the rich indexing of archaeological documents via natural language-processing techniques and knowledge-based resources (2010) 0.00 
    0.0011898974 = product of:
  0.0071393843 = sum of:
    0.0071393843 = weight(_text_:in in 3948) [ClassicSimilarity], result of:
      0.0071393843 = score(doc=3948,freq=8.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.120230645 = fieldWeight in 3948, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.03125 = fieldNorm(doc=3948)
  0.16666667 = coord(1/6)
    Abstract
    Purpose - This paper sets out to discuss the use of information extraction (IE), a natural language-processing (NLP) technique to assist "rich" semantic indexing of diverse archaeological text resources. The focus of the research is to direct a semantic-aware "rich" indexing of diverse natural language resources with properties capable of satisfying information retrieval from online publications and datasets associated with the Semantic Technologies for Archaeological Resources (STAR) project. Design/methodology/approach - The paper proposes use of the English Heritage extension (CRM-EH) of the standard core ontology in cultural heritage, CIDOC CRM, and exploitation of domain thesauri resources for driving and enhancing an Ontology-Oriented Information Extraction process. The process of semantic indexing is based on a rule-based Information Extraction technique, which is facilitated by the General Architecture of Text Engineering (GATE) toolkit and expressed by Java Annotation Pattern Engine (JAPE) rules. Findings - Initial results suggest that the combination of information extraction with knowledge resources and standard conceptual models is capable of supporting semantic-aware term indexing. Additional efforts are required for further exploitation of the technique and adoption of formal evaluation methods for assessing the performance of the method in measurable terms. Originality/value - The value of the paper lies in the semantic indexing of 535 unpublished online documents often referred to as "Grey Literature", from the Archaeological Data Service OASIS corpus (Online AccesS to the Index of archaeological investigationS), with respect to the CRM ontological concepts E49.Time Appellation and P19.Physical Object.
    Footnote
    Beitrag in einem Special Issue: Content architecture: exploiting and managing diverse resources: proceedings of the first national conference of the United Kingdom chapter of the International Society for Knowedge Organization (ISKO)
  19. Nielsen, R.D.; Ward, W.; Martin, J.H.; Palmer, M.: Extracting a representation from text for semantic analysis (2008) 0.00 
    0.0011898974 = product of:
  0.0071393843 = sum of:
    0.0071393843 = weight(_text_:in in 3365) [ClassicSimilarity], result of:
      0.0071393843 = score(doc=3365,freq=2.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.120230645 = fieldWeight in 3365, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0625 = fieldNorm(doc=3365)
  0.16666667 = coord(1/6)
    Abstract
    We present a novel fine-grained semantic representation of text and an approach to constructing it. This representation is largely extractable by today's technologies and facilitates more detailed semantic analysis. We discuss the requirements driving the representation, suggest how it might be of value in the automated tutoring domain, and provide evidence of its validity.
  20. Pepper, S.; Arnaud, P.J.L.: Absolutely PHAB : toward a general model of associative relations (2020) 0.00 
    0.0010517307 = product of:
  0.006310384 = sum of:
    0.006310384 = weight(_text_:in in 103) [ClassicSimilarity], result of:
      0.006310384 = score(doc=103,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.10626988 = fieldWeight in 103, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=103)
  0.16666667 = coord(1/6)
    Abstract
    There have been many attempts at classifying the semantic modification relations (R) of N + N compounds but this work has not led to the acceptance of a definitive scheme, so that devising a reusable classification is a worthwhile aim. The scope of this undertaking is extended to other binominal lexemes, i.e. units that contain two thing-morphemes without explicitly stating R, like prepositional units, N + relational adjective units, etc. The 25-relation taxonomy of Bourque (2014) was tested against over 15,000 binominal lexemes from 106 languages and extended to a 29-relation scheme ("Bourque2") through the introduction of two new reversible relations. Bourque2 is then mapped onto Hatcher's (1960) four-relation scheme (extended by the addition of a fifth relation, similarity , as "Hatcher2"). This results in a two-tier system usable at different degrees of granularities. On account of its semantic proximity to compounding, metonymy is then taken into account, following Janda's (2011) suggestion that it plays a role in word formation; Peirsman and Geeraerts' (2006) inventory of 23 metonymic patterns is mapped onto Bourque2, confirming the identity of metonymic and binominal modification relations. Finally, Blank's (2003) and Koch's (2001) work on lexical semantics justifies the addition to the scheme of a third, superordinate level which comprises the three Aristotelean principles of similarity, contiguity and contrast.

Authors

Years

Languages

  • e 18
  • d 3

Types

Subjects

Classifications