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Athukorala, K.; Glowacka, D.; Jacucci, G.; Oulasvirta, A.; Vreeken, J.: Is exploratory search different? : a comparison of information search behavior for exploratory and lookup tasks (2016)
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Kasprzik, A.; Kett, J.: Vorschläge für eine Weiterentwicklung der Sacherschließung und Schritte zur fortgesetzten strukturellen Aufwertung der GND (2018)
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Ru, C.; Tang, J.; Li, S.; Xie, S.; Wang, T.: Using semantic similarity to reduce wrong labels in distant supervision for relation extraction (2018)
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Fernández-Reyes, F.C.; Hermosillo-Valadez, J.; Montes-y-Gómez, M.: ¬A prospect-guided global query expansion strategy using word embeddings (2018)
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-
Bradford, R.B.: Relationship discovery in large text collections using Latent Semantic Indexing (2006)
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- Source
- Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism, and Security, SIAM Data Mining Conference, Bethesda, MD, 20-22 April, 2006. [http://www.siam.org/meetings/sdm06/workproceed/Link%20Analysis/15.pdf]
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Brunetti, J.M.; Roberto García, R.: User-centered design and evaluation of overview components for semantic data exploration (2014)
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- Date
- 20. 1.2015 18:30:22
-
Gillitzer, B.: Yewno (2017)
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- Date
- 22. 2.2017 10:16:49
-
Thenmalar, S.; Geetha, T.V.: Enhanced ontology-based indexing and searching (2014)
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- Date
- 20. 1.2015 18:30:22
-
Bilal, D.; Kirby, J.: Differences and similarities in information seeking : children and adults as Web users (2002)
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-
Tudhope, D.; Binding, C.; Blocks, D.; Cunliffe, D.: FACET: thesaurus retrieval with semantic term expansion (2002)
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- Abstract
- There are many advantages for Digital Libraries in indexing with classifications or thesauri, but some current disincentive in the lack of flexible retrieval tools that deal with compound descriptors. This demonstration of a research prototype illustrates a matching function for compound descriptors, or multi-concept subject headings, that does not rely on exact matching but incorporates term expansion via thesaurus semantic relationships to produce ranked results that take account of missing and partially matching terms. The matching function is based on a measure of semantic closeness between terms.The work is part of the EPSRC funded FACET project in collaboration with the UK National Museum of Science and Industry (NMSI) which includes the National Railway Museum. An export of NMSI's Collections Database is used as the dataset for the research. The J. Paul Getty Trust's Art and Architecture Thesaurus (AAT) is the main thesaurus in the project. The AAT is a widely used thesaurus (over 120,000 terms). Descriptors are organised in 7 facets representing separate conceptual classes of terms.The FACET application is a multi tiered architecture accessing a SQL Server database, with an OLE DB connection. The thesauri are stored as relational tables in the Server's database. However, a key component of the system is a parallel representation of the underlying semantic network as an in-memory structure of thesaurus concepts (corresponding to preferred terms). The structure models the hierarchical and associative interrelationships of thesaurus concepts via weighted poly-hierarchical links. Its primary purpose is real-time semantic expansion of query terms, achieved by a spreading activation semantic closeness algorithm. Queries with associated results are stored persistently using XML format data. A Visual Basic interface combines a thesaurus browser and an initial term search facility that takes into account equivalence relationships. Terms are dragged to a direct manipulation Query Builder which maintains the facet structure.
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Layfield, C.; Azzopardi, J,; Staff, C.: Experiments with document retrieval from small text collections using Latent Semantic Analysis or term similarity with query coordination and automatic relevance feedback (2017)
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Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002)
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