-
Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005)
0.40
0.39945948 = product of:
0.49932435 = sum of:
0.032562904 = product of:
0.097688705 = sum of:
0.097688705 = weight(_text_:3a in 701) [ClassicSimilarity], result of:
0.097688705 = score(doc=701,freq=2.0), product of:
0.2607266 = queryWeight, product of:
8.478011 = idf(docFreq=24, maxDocs=44218)
0.030753274 = queryNorm
0.3746787 = fieldWeight in 701, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
8.478011 = idf(docFreq=24, maxDocs=44218)
0.03125 = fieldNorm(doc=701)
0.33333334 = coord(1/3)
0.097688705 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
0.097688705 = score(doc=701,freq=2.0), product of:
0.2607266 = queryWeight, product of:
8.478011 = idf(docFreq=24, maxDocs=44218)
0.030753274 = queryNorm
0.3746787 = fieldWeight in 701, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
8.478011 = idf(docFreq=24, maxDocs=44218)
0.03125 = fieldNorm(doc=701)
0.097688705 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
0.097688705 = score(doc=701,freq=2.0), product of:
0.2607266 = queryWeight, product of:
8.478011 = idf(docFreq=24, maxDocs=44218)
0.030753274 = queryNorm
0.3746787 = fieldWeight in 701, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
8.478011 = idf(docFreq=24, maxDocs=44218)
0.03125 = fieldNorm(doc=701)
0.012565169 = weight(_text_:information in 701) [ClassicSimilarity], result of:
0.012565169 = score(doc=701,freq=18.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.23274568 = fieldWeight in 701, product of:
4.2426405 = tf(freq=18.0), with freq of:
18.0 = termFreq=18.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.03125 = fieldNorm(doc=701)
0.046531465 = weight(_text_:retrieval in 701) [ClassicSimilarity], result of:
0.046531465 = score(doc=701,freq=28.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.5001983 = fieldWeight in 701, product of:
5.2915025 = tf(freq=28.0), with freq of:
28.0 = termFreq=28.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.03125 = fieldNorm(doc=701)
0.097688705 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
0.097688705 = score(doc=701,freq=2.0), product of:
0.2607266 = queryWeight, product of:
8.478011 = idf(docFreq=24, maxDocs=44218)
0.030753274 = queryNorm
0.3746787 = fieldWeight in 701, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
8.478011 = idf(docFreq=24, maxDocs=44218)
0.03125 = fieldNorm(doc=701)
0.097688705 = weight(_text_:2f in 701) [ClassicSimilarity], result of:
0.097688705 = score(doc=701,freq=2.0), product of:
0.2607266 = queryWeight, product of:
8.478011 = idf(docFreq=24, maxDocs=44218)
0.030753274 = queryNorm
0.3746787 = fieldWeight in 701, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
8.478011 = idf(docFreq=24, maxDocs=44218)
0.03125 = fieldNorm(doc=701)
0.01690999 = product of:
0.03381998 = sum of:
0.03381998 = weight(_text_:evaluation in 701) [ClassicSimilarity], result of:
0.03381998 = score(doc=701,freq=4.0), product of:
0.12900078 = queryWeight, product of:
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.030753274 = queryNorm
0.2621688 = fieldWeight in 701, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.03125 = fieldNorm(doc=701)
0.5 = coord(1/2)
0.8 = coord(8/10)
- Abstract
- By the explosion of possibilities for a ubiquitous content production, the information overload problem reaches the level of complexity which cannot be managed by traditional modelling approaches anymore. Due to their pure syntactical nature traditional information retrieval approaches did not succeed in treating content itself (i.e. its meaning, and not its representation). This leads to a very low usefulness of the results of a retrieval process for a user's task at hand. In the last ten years ontologies have been emerged from an interesting conceptualisation paradigm to a very promising (semantic) modelling technology, especially in the context of the Semantic Web. From the information retrieval point of view, ontologies enable a machine-understandable form of content description, such that the retrieval process can be driven by the meaning of the content. However, the very ambiguous nature of the retrieval process in which a user, due to the unfamiliarity with the underlying repository and/or query syntax, just approximates his information need in a query, implies a necessity to include the user in the retrieval process more actively in order to close the gap between the meaning of the content and the meaning of a user's query (i.e. his information need). This thesis lays foundation for such an ontology-based interactive retrieval process, in which the retrieval system interacts with a user in order to conceptually interpret the meaning of his query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user's information need and to deliver the relevant content proactively. Moreover, the notion of content relevance for a user's query evolves from a content dependent artefact to the multidimensional context-dependent structure, strongly influenced by the user's preferences. This cooperation process is realized as the so-called Librarian Agent Query Refinement Process. In order to clarify the impact of an ontology on the retrieval process (regarding its complexity and quality), a set of methods and tools for different levels of content and query formalisation is developed, ranging from pure ontology-based inferencing to keyword-based querying in which semantics automatically emerges from the results. Our evaluation studies have shown that the possibilities to conceptualize a user's information need in the right manner and to interpret the retrieval results accordingly are key issues for realizing much more meaningful information retrieval systems.
- Content
- Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.
-
Mayr, P.; Mutschke, P.; Petras, V.: Reducing semantic complexity in distributed digital libraries : Treatment of term vagueness and document re-ranking (2008)
0.04
0.043909937 = product of:
0.14636645 = sum of:
0.0090681305 = weight(_text_:information in 1909) [ClassicSimilarity], result of:
0.0090681305 = score(doc=1909,freq=6.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.16796975 = fieldWeight in 1909, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=1909)
0.015545071 = weight(_text_:retrieval in 1909) [ClassicSimilarity], result of:
0.015545071 = score(doc=1909,freq=2.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.16710453 = fieldWeight in 1909, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0390625 = fieldNorm(doc=1909)
0.121753246 = weight(_text_:ranking in 1909) [ClassicSimilarity], result of:
0.121753246 = score(doc=1909,freq=12.0), product of:
0.16634533 = queryWeight, product of:
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.030753274 = queryNorm
0.7319307 = fieldWeight in 1909, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.0390625 = fieldNorm(doc=1909)
0.3 = coord(3/10)
- Abstract
- Purpose - The general science portal "vascoda" merges structured, high-quality information collections from more than 40 providers on the basis of search engine technology (FAST) and a concept which treats semantic heterogeneity between different controlled vocabularies. First experiences with the portal show some weaknesses of this approach which come out in most metadata-driven Digital Libraries (DLs) or subject specific portals. The purpose of the paper is to propose models to reduce the semantic complexity in heterogeneous DLs. The aim is to introduce value-added services (treatment of term vagueness and document re-ranking) that gain a certain quality in DLs if they are combined with heterogeneity components established in the project "Competence Center Modeling and Treatment of Semantic Heterogeneity". Design/methodology/approach - Two methods, which are derived from scientometrics and network analysis, will be implemented with the objective to re-rank result sets by the following structural properties: the ranking of the results by core journals (so-called Bradfordizing) and ranking by centrality of authors in co-authorship networks. Findings - The methods, which will be implemented, focus on the query and on the result side of a search and are designed to positively influence each other. Conceptually, they will improve the search quality and guarantee that the most relevant documents in result sets will be ranked higher. Originality/value - The central impact of the paper focuses on the integration of three structural value-adding methods, which aim at reducing the semantic complexity represented in distributed DLs at several stages in the information retrieval process: query construction, search and ranking and re-ranking.
- Theme
- Information Gateway
-
Krause, J.: Shell Model, Semantic Web and Web Information Retrieval (2006)
0.03
0.027431583 = product of:
0.09143861 = sum of:
0.014808194 = weight(_text_:information in 6061) [ClassicSimilarity], result of:
0.014808194 = score(doc=6061,freq=16.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.27429342 = fieldWeight in 6061, product of:
4.0 = tf(freq=16.0), with freq of:
16.0 = termFreq=16.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=6061)
0.026924854 = weight(_text_:retrieval in 6061) [ClassicSimilarity], result of:
0.026924854 = score(doc=6061,freq=6.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.28943354 = fieldWeight in 6061, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0390625 = fieldNorm(doc=6061)
0.049705554 = weight(_text_:ranking in 6061) [ClassicSimilarity], result of:
0.049705554 = score(doc=6061,freq=2.0), product of:
0.16634533 = queryWeight, product of:
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.030753274 = queryNorm
0.29880944 = fieldWeight in 6061, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.0390625 = fieldNorm(doc=6061)
0.3 = coord(3/10)
- Abstract
- The middle of the 1990s are coined by the increased enthusiasm for the possibilities of the WWW, which has only recently deviated - at least in relation to scientific information - for the differentiated measuring of its advantages and disadvantages. Web Information Retrieval originated as a specialized discipline with great commercial significance (for an overview see Lewandowski 2005). Besides the new technological structure that enables the indexing and searching (in seconds) of unimaginable amounts of data worldwide, new assessment processes for the ranking of search results are being developed, which use the link structures of the Web. They are the main innovation with respect to the traditional "mother discipline" of Information Retrieval. From the beginning, link structures of Web pages are applied to commercial search engines in a wide array of variations. From the perspective of scientific information, link topology based approaches were in essence trying to solve a self-created problem: on the one hand, it quickly became clear that the openness of the Web led to an up-tonow unknown increase in available information, but this also caused the quality of the Web pages searched to become a problem - and with it the relevance of the results. The gatekeeper function of traditional information providers, which narrows down every user query to focus on high-quality sources was lacking. Therefore, the recognition of the "authoritativeness" of the Web pages by general search engines such as Google was one of the most important factors for their success.
- Source
- Information und Sprache: Beiträge zu Informationswissenschaft, Computerlinguistik, Bibliothekswesen und verwandten Fächern. Festschrift für Harald H. Zimmermann. Herausgegeben von Ilse Harms, Heinz-Dirk Luckhardt und Hans W. Giessen
-
Franklin, R.A.: Re-inventing subject access for the semantic web (2003)
0.03
0.025742413 = product of:
0.08580804 = sum of:
0.0062825847 = weight(_text_:information in 2556) [ClassicSimilarity], result of:
0.0062825847 = score(doc=2556,freq=2.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.116372846 = fieldWeight in 2556, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=2556)
0.018654086 = weight(_text_:retrieval in 2556) [ClassicSimilarity], result of:
0.018654086 = score(doc=2556,freq=2.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.20052543 = fieldWeight in 2556, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=2556)
0.06087137 = sum of:
0.035871506 = weight(_text_:evaluation in 2556) [ClassicSimilarity], result of:
0.035871506 = score(doc=2556,freq=2.0), product of:
0.12900078 = queryWeight, product of:
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.030753274 = queryNorm
0.278072 = fieldWeight in 2556, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.046875 = fieldNorm(doc=2556)
0.024999864 = weight(_text_:22 in 2556) [ClassicSimilarity], result of:
0.024999864 = score(doc=2556,freq=2.0), product of:
0.107692726 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.030753274 = queryNorm
0.23214069 = fieldWeight in 2556, 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=2556)
0.3 = coord(3/10)
- Abstract
- First generation scholarly research on the Web lacked a firm system of authority control. Second generation Web research is beginning to model subject access with library science principles of bibliographic control and cataloguing. Harnessing the Web and organising the intellectual content with standards and controlled vocabulary provides precise search and retrieval capability, increasing relevance and efficient use of technology. Dublin Core metadata standards permit a full evaluation and cataloguing of Web resources appropriate to highly specific research needs and discovery. Current research points to a type of structure based on a system of faceted classification. This system allows the semantic and syntactic relationships to be defined. Controlled vocabulary, such as the Library of Congress Subject Headings, can be assigned, not in a hierarchical structure, but rather as descriptive facets of relating concepts. Web design features such as this are adding value to discovery and filtering out data that lack authority. The system design allows for scalability and extensibility, two technical features that are integral to future development of the digital library and resource discovery.
- Date
- 30.12.2008 18:22:46
- Source
- Online information review. 27(2003) no.2, S.94-101
-
Oliveira Machado, L.M.; Souza, R.R.; Simões, M. da Graça: Semantic web or web of data? : a diachronic study (1999 to 2017) of the publications of Tim Berners-Lee and the World Wide Web Consortium (2019)
0.02
0.024227323 = product of:
0.08075774 = sum of:
0.0090681305 = weight(_text_:information in 5300) [ClassicSimilarity], result of:
0.0090681305 = score(doc=5300,freq=6.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.16796975 = fieldWeight in 5300, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=5300)
0.02198405 = weight(_text_:retrieval in 5300) [ClassicSimilarity], result of:
0.02198405 = score(doc=5300,freq=4.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.23632148 = fieldWeight in 5300, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0390625 = fieldNorm(doc=5300)
0.049705554 = weight(_text_:ranking in 5300) [ClassicSimilarity], result of:
0.049705554 = score(doc=5300,freq=2.0), product of:
0.16634533 = queryWeight, product of:
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.030753274 = queryNorm
0.29880944 = fieldWeight in 5300, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.0390625 = fieldNorm(doc=5300)
0.3 = coord(3/10)
- Abstract
- The web has been, in the last decades, the place where information retrieval achieved its maximum importance, given its ubiquity and the sheer volume of information. However, its exponential growth made the retrieval task increasingly hard, relying in its effectiveness on idiosyncratic and somewhat biased ranking algorithms. To deal with this problem, a "new" web, called the Semantic Web (SW), was proposed, bringing along concepts like "Web of Data" and "Linked Data," although the definitions and connections among these concepts are often unclear. Based on a qualitative approach built over a literature review, a definition of SW is presented, discussing the related concepts sometimes used as synonyms. It concludes that the SW is a comprehensive and ambitious construct that includes the great purpose of making the web a global database. It also follows the specifications developed and/or associated with its operationalization and the necessary procedures for the connection of data in an open format on the web. The goals of this comprehensive SW are the union of two outcomes still tenuously connected: the virtually unlimited possibility of connections between data-the web domain-with the potentiality of the automated inference of "intelligent" systems-the semantic component.
- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.7, S.701-714
-
Mayfield, J.; Finin, T.: Information retrieval on the Semantic Web : integrating inference and retrieval
0.02
0.02208383 = product of:
0.073612764 = sum of:
0.010365736 = weight(_text_:information in 4330) [ClassicSimilarity], result of:
0.010365736 = score(doc=4330,freq=4.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.1920054 = fieldWeight in 4330, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0546875 = fieldNorm(doc=4330)
0.048663773 = weight(_text_:retrieval in 4330) [ClassicSimilarity], result of:
0.048663773 = score(doc=4330,freq=10.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.5231199 = fieldWeight in 4330, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0546875 = fieldNorm(doc=4330)
0.014583254 = product of:
0.029166508 = sum of:
0.029166508 = weight(_text_:22 in 4330) [ClassicSimilarity], result of:
0.029166508 = score(doc=4330,freq=2.0), product of:
0.107692726 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.030753274 = queryNorm
0.2708308 = fieldWeight in 4330, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0546875 = fieldNorm(doc=4330)
0.5 = coord(1/2)
0.3 = coord(3/10)
- Abstract
- One vision of the Semantic Web is that it will be much like the Web we know today, except that documents will be enriched by annotations in machine understandable markup. These annotations will provide metadata about the documents as well as machine interpretable statements capturing some of the meaning of document content. We discuss how the information retrieval paradigm might be recast in such an environment. We suggest that retrieval can be tightly bound to inference. Doing so makes today's Web search engines useful to Semantic Web inference engines, and causes improvements in either retrieval or inference to lead directly to improvements in the other.
- Date
- 12. 2.2011 17:35:22
-
Zenz, G.; Zhou, X.; Minack, E.; Siberski, W.; Nejdl, W.: Interactive query construction for keyword search on the Semantic Web (2012)
0.02
0.021796418 = product of:
0.072654724 = sum of:
0.007404097 = weight(_text_:information in 430) [ClassicSimilarity], result of:
0.007404097 = score(doc=430,freq=4.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.13714671 = fieldWeight in 430, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=430)
0.015545071 = weight(_text_:retrieval in 430) [ClassicSimilarity], result of:
0.015545071 = score(doc=430,freq=2.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.16710453 = fieldWeight in 430, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0390625 = fieldNorm(doc=430)
0.049705554 = weight(_text_:ranking in 430) [ClassicSimilarity], result of:
0.049705554 = score(doc=430,freq=2.0), product of:
0.16634533 = queryWeight, product of:
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.030753274 = queryNorm
0.29880944 = fieldWeight in 430, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.0390625 = fieldNorm(doc=430)
0.3 = coord(3/10)
- Abstract
- With the advance of the semantic Web, increasing amounts of data are available in a structured and machine-understandable form. This opens opportunities for users to employ semantic queries instead of simple keyword-based ones to accurately express the information need. However, constructing semantic queries is a demanding task for human users [11]. To compose a valid semantic query, a user has to (1) master a query language (e.g., SPARQL) and (2) acquire sufficient knowledge about the ontology or the schema of the data source. While there are systems which support this task with visual tools [21, 26] or natural language interfaces [3, 13, 14, 18], the process of query construction can still be complex and time consuming. According to [24], users prefer keyword search, and struggle with the construction of semantic queries although being supported with a natural language interface. Several keyword search approaches have already been proposed to ease information seeking on semantic data [16, 32, 35] or databases [1, 31]. However, keyword queries lack the expressivity to precisely describe the user's intent. As a result, ranking can at best put query intentions of the majority on top, making it impossible to take the intentions of all users into consideration.
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Kara, S.: ¬An ontology-based retrieval system using semantic indexing (2012)
0.02
0.020342728 = product of:
0.06780909 = sum of:
0.0125651695 = weight(_text_:information in 3829) [ClassicSimilarity], result of:
0.0125651695 = score(doc=3829,freq=8.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.23274569 = fieldWeight in 3829, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=3829)
0.03730817 = weight(_text_:retrieval in 3829) [ClassicSimilarity], result of:
0.03730817 = score(doc=3829,freq=8.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.40105087 = fieldWeight in 3829, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=3829)
0.017935753 = product of:
0.035871506 = sum of:
0.035871506 = weight(_text_:evaluation in 3829) [ClassicSimilarity], result of:
0.035871506 = score(doc=3829,freq=2.0), product of:
0.12900078 = queryWeight, product of:
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.030753274 = queryNorm
0.278072 = fieldWeight in 3829, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.046875 = fieldNorm(doc=3829)
0.5 = coord(1/2)
0.3 = coord(3/10)
- Abstract
- In this thesis, we present an ontology-based information extraction and retrieval system and its application to soccer domain. In general, we deal with three issues in semantic search, namely, usability, scalability and retrieval performance. We propose a keyword-based semantic retrieval approach. The performance of the system is improved considerably using domain-specific information extraction, inference and rules. Scalability is achieved by adapting a semantic indexing approach. The system is implemented using the state-of-the-art technologies in SemanticWeb and its performance is evaluated against traditional systems as well as the query expansion methods. Furthermore, a detailed evaluation is provided to observe the performance gain due to domain-specific information extraction and inference. Finally, we show how we use semantic indexing to solve simple structural ambiguities.
- Source
- Information Systems. 37(2012) no. 4, S.294-305
-
Vocht, L. De: Exploring semantic relationships in the Web of Data : Semantische relaties verkennen in data op het web (2017)
0.02
0.020047983 = product of:
0.05011996 = sum of:
0.0069258986 = weight(_text_:information in 4232) [ClassicSimilarity], result of:
0.0069258986 = score(doc=4232,freq=14.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.128289 = fieldWeight in 4232, product of:
3.7416575 = tf(freq=14.0), with freq of:
14.0 = termFreq=14.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.01953125 = fieldNorm(doc=4232)
0.0077725356 = weight(_text_:retrieval in 4232) [ClassicSimilarity], result of:
0.0077725356 = score(doc=4232,freq=2.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.08355226 = fieldWeight in 4232, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.01953125 = fieldNorm(doc=4232)
0.024852777 = weight(_text_:ranking in 4232) [ClassicSimilarity], result of:
0.024852777 = score(doc=4232,freq=2.0), product of:
0.16634533 = queryWeight, product of:
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.030753274 = queryNorm
0.14940472 = fieldWeight in 4232, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.01953125 = fieldNorm(doc=4232)
0.010568744 = product of:
0.021137487 = sum of:
0.021137487 = weight(_text_:evaluation in 4232) [ClassicSimilarity], result of:
0.021137487 = score(doc=4232,freq=4.0), product of:
0.12900078 = queryWeight, product of:
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.030753274 = queryNorm
0.1638555 = fieldWeight in 4232, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.01953125 = fieldNorm(doc=4232)
0.5 = coord(1/2)
0.4 = coord(4/10)
- Abstract
- After the launch of the World Wide Web, it became clear that searching documentson the Web would not be trivial. Well-known engines to search the web, like Google, focus on search in web documents using keywords. The documents are structured and indexed to ensure keywords match documents as accurately as possible. However, searching by keywords does not always suice. It is oen the case that users do not know exactly how to formulate the search query or which keywords guarantee retrieving the most relevant documents. Besides that, it occurs that users rather want to browse information than looking up something specific. It turned out that there is need for systems that enable more interactivity and facilitate the gradual refinement of search queries to explore the Web. Users expect more from the Web because the short keyword-based queries they pose during search, do not suffice for all cases. On top of that, the Web is changing structurally. The Web comprises, apart from a collection of documents, more and more linked data, pieces of information structured so they can be processed by machines. The consequently applied semantics allow users to exactly indicate machines their search intentions. This is made possible by describing data following controlled vocabularies, concept lists composed by experts, published uniquely identifiable on the Web. Even so, it is still not trivial to explore data on the Web. There is a large variety of vocabularies and various data sources use different terms to identify the same concepts.
This PhD-thesis describes how to effectively explore linked data on the Web. The main focus is on scenarios where users want to discover relationships between resources rather than finding out more about something specific. Searching for a specific document or piece of information fits in the theoretical framework of information retrieval and is associated with exploratory search. Exploratory search goes beyond 'looking up something' when users are seeking more detailed understanding, further investigation or navigation of the initial search results. The ideas behind exploratory search and querying linked data merge when it comes to the way knowledge is represented and indexed by machines - how data is structured and stored for optimal searchability. Queries and information should be aligned to facilitate that searches also reveal connections between results. This implies that they take into account the same semantic entities, relevant at that moment. To realize this, we research three techniques that are evaluated one by one in an experimental set-up to assess how well they succeed in their goals. In the end, the techniques are applied to a practical use case that focuses on forming a bridge between the Web and the use of digital libraries in scientific research. Our first technique focuses on the interactive visualization of search results. Linked data resources can be brought in relation with each other at will. This leads to complex and diverse graphs structures. Our technique facilitates navigation and supports a workflow starting from a broad overview on the data and allows narrowing down until the desired level of detail to then broaden again. To validate the flow, two visualizations where implemented and presented to test-users. The users judged the usability of the visualizations, how the visualizations fit in the workflow and to which degree their features seemed useful for the exploration of linked data.
The ideas behind exploratory search and querying linked data merge when it comes to the way knowledge is represented and indexed by machines - how data is structured and stored for optimal searchability. eries and information should be aligned to facilitate that searches also reveal connections between results. This implies that they take into account the same semantic entities, relevant at that moment. To realize this, we research three techniques that are evaluated one by one in an experimental set-up to assess how well they succeed in their goals. In the end, the techniques are applied to a practical use case that focuses on forming a bridge between the Web and the use of digital libraries in scientific research.
Our first technique focuses on the interactive visualization of search results. Linked data resources can be brought in relation with each other at will. This leads to complex and diverse graphs structures. Our technique facilitates navigation and supports a workflow starting from a broad overview on the data and allows narrowing down until the desired level of detail to then broaden again. To validate the flow, two visualizations where implemented and presented to test-users. The users judged the usability of the visualizations, how the visualizations fit in the workflow and to which degree their features seemed useful for the exploration of linked data. There is a difference in the way users interact with resources, visually or textually, and how resources are represented for machines to be processed by algorithms. This difference complicates bridging the users' intents and machine executable queries. It is important to implement this 'translation' mechanism to impact the search as favorable as possible in terms of performance, complexity and accuracy. To do this, we explain a second technique, that supports such a bridging component. Our second technique is developed around three features that support the search process: looking up, relating and ranking resources. The main goal is to ensure that resources in the results are as precise and relevant as possible. During the evaluation of this technique, we did not only look at the precision of the search results but also investigated how the effectiveness of the search evolved while the user executed certain actions sequentially.
When we speak about finding relationships between resources, it is necessary to dive deeper in the structure. The graph structure of linked data where the semantics give meaning to the relationships between resources enable the execution of pathfinding algorithms. The assigned weights and heuristics are base components of such algorithms and ultimately define (the order) which resources are included in a path. These paths explain indirect connections between resources. Our third technique proposes an algorithm that optimizes the choice of resources in terms of serendipity. Some optimizations guard the consistence of candidate-paths where the coherence of consecutive connections is maximized to avoid trivial and too arbitrary paths. The implementation uses the A* algorithm, the de-facto reference when it comes to heuristically optimized minimal cost paths. The effectiveness of paths was measured based on common automatic metrics and surveys where the users could indicate their preference for paths, generated each time in a different way. Finally, all our techniques are applied to a use case about publications in digital libraries where they are aligned with information about scientific conferences and researchers. The application to this use case is a practical example because the different aspects of exploratory search come together. In fact, the techniques also evolved from the experiences when implementing the use case. Practical details about the semantic model are explained and the implementation of the search system is clarified module by module. The evaluation positions the result, a prototype of a tool to explore scientific publications, researchers and conferences next to some important alternatives.
-
Brunetti, J.M.; Roberto García, R.: User-centered design and evaluation of overview components for semantic data exploration (2014)
0.02
0.018982917 = product of:
0.06327639 = sum of:
0.010259418 = weight(_text_:information in 1626) [ClassicSimilarity], result of:
0.010259418 = score(doc=1626,freq=12.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.19003606 = fieldWeight in 1626, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.03125 = fieldNorm(doc=1626)
0.012436057 = weight(_text_:retrieval in 1626) [ClassicSimilarity], result of:
0.012436057 = score(doc=1626,freq=2.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.13368362 = fieldWeight in 1626, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.03125 = fieldNorm(doc=1626)
0.040580913 = sum of:
0.023914335 = weight(_text_:evaluation in 1626) [ClassicSimilarity], result of:
0.023914335 = score(doc=1626,freq=2.0), product of:
0.12900078 = queryWeight, product of:
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.030753274 = queryNorm
0.18538132 = fieldWeight in 1626, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.03125 = fieldNorm(doc=1626)
0.016666576 = weight(_text_:22 in 1626) [ClassicSimilarity], result of:
0.016666576 = score(doc=1626,freq=2.0), product of:
0.107692726 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.030753274 = queryNorm
0.15476047 = fieldWeight in 1626, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.03125 = fieldNorm(doc=1626)
0.3 = coord(3/10)
- Abstract
- Purpose - The growing volumes of semantic data available in the web result in the need for handling the information overload phenomenon. The potential of this amount of data is enormous but in most cases it is very difficult for users to visualize, explore and use this data, especially for lay-users without experience with Semantic Web technologies. The paper aims to discuss these issues. Design/methodology/approach - The Visual Information-Seeking Mantra "Overview first, zoom and filter, then details-on-demand" proposed by Shneiderman describes how data should be presented in different stages to achieve an effective exploration. The overview is the first user task when dealing with a data set. The objective is that the user is capable of getting an idea about the overall structure of the data set. Different information architecture (IA) components supporting the overview tasks have been developed, so they are automatically generated from semantic data, and evaluated with end-users. Findings - The chosen IA components are well known to web users, as they are present in most web pages: navigation bars, site maps and site indexes. The authors complement them with Treemaps, a visualization technique for displaying hierarchical data. These components have been developed following an iterative User-Centered Design methodology. Evaluations with end-users have shown that they get easily used to them despite the fact that they are generated automatically from structured data, without requiring knowledge about the underlying semantic technologies, and that the different overview components complement each other as they focus on different information search needs. Originality/value - Obtaining semantic data sets overviews cannot be easily done with the current semantic web browsers. Overviews become difficult to achieve with large heterogeneous data sets, which is typical in the Semantic Web, because traditional IA techniques do not easily scale to large data sets. There is little or no support to obtain overview information quickly and easily at the beginning of the exploration of a new data set. This can be a serious limitation when exploring a data set for the first time, especially for lay-users. The proposal is to reuse and adapt existing IA components to provide this overview to users and show that they can be generated automatically from the thesaurus and ontologies that structure semantic data while providing a comparable user experience to traditional web sites.
- Date
- 20. 1.2015 18:30:22
- Source
- Aslib journal of information management. 66(2014) no.5, S.519-536
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Brambilla, M.; Ceri, S.: Designing exploratory search applications upon Web data sources (2012)
0.02
0.018737976 = product of:
0.062459916 = sum of:
0.010259418 = weight(_text_:information in 428) [ClassicSimilarity], result of:
0.010259418 = score(doc=428,freq=12.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.19003606 = fieldWeight in 428, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.03125 = fieldNorm(doc=428)
0.012436057 = weight(_text_:retrieval in 428) [ClassicSimilarity], result of:
0.012436057 = score(doc=428,freq=2.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.13368362 = fieldWeight in 428, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.03125 = fieldNorm(doc=428)
0.03976444 = weight(_text_:ranking in 428) [ClassicSimilarity], result of:
0.03976444 = score(doc=428,freq=2.0), product of:
0.16634533 = queryWeight, product of:
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.030753274 = queryNorm
0.23904754 = fieldWeight in 428, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.03125 = fieldNorm(doc=428)
0.3 = coord(3/10)
- Abstract
- Search is the preferred method to access information in today's computing systems. The Web, accessed through search engines, is universally recognized as the source for answering users' information needs. However, offering a link to a Web page does not cover all information needs. Even simple problems, such as "Which theater offers an at least three-stars action movie in London close to a good Italian restaurant," can only be solved by searching the Web multiple times, e.g., by extracting a list of the recent action movies filtered by ranking, then looking for movie theaters, then looking for Italian restaurants close to them. While search engines hint to useful information, the user's brain is the fundamental platform for information integration. An important trend is the availability of new, specialized data sources-the so-called "long tail" of the Web of data. Such carefully collected and curated data sources can be much more valuable than information currently available in Web pages; however, many sources remain hidden or insulated, in the lack of software solutions for bringing them to surface and making them usable in the search context. A new class of tailor-made systems, designed to satisfy the needs of users with specific aims, will support the publishing and integration of data sources for vertical domains; the user will be able to select sources based on individual or collective trust, and systems will be able to route queries to such sources and to provide easyto-use interfaces for combining them within search strategies, at the same time, rewarding the data source owners for each contribution to effective search. Efforts such as Google's Fusion Tables show that the technology for bringing hidden data sources to surface is feasible.
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Ning, X.; Jin, H.; Wu, H.: RSS: a framework enabling ranked search on the semantic web (2008)
0.02
0.015105951 = product of:
0.075529754 = sum of:
0.005235487 = weight(_text_:information in 2069) [ClassicSimilarity], result of:
0.005235487 = score(doc=2069,freq=2.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.09697737 = fieldWeight in 2069, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=2069)
0.07029427 = weight(_text_:ranking in 2069) [ClassicSimilarity], result of:
0.07029427 = score(doc=2069,freq=4.0), product of:
0.16634533 = queryWeight, product of:
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.030753274 = queryNorm
0.42258036 = fieldWeight in 2069, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.0390625 = fieldNorm(doc=2069)
0.2 = coord(2/10)
- Abstract
- The semantic web not only contains resources but also includes the heterogeneous relationships among them, which is sharply distinguished from the current web. As the growth of the semantic web, specialized search techniques are of significance. In this paper, we present RSS-a framework for enabling ranked semantic search on the semantic web. In this framework, the heterogeneity of relationships is fully exploited to determine the global importance of resources. In addition, the search results can be greatly expanded with entities most semantically related to the query, thus able to provide users with properly ordered semantic search results by combining global ranking values and the relevance between the resources and the query. The proposed semantic search model which supports inference is very different from traditional keyword-based search methods. Moreover, RSS also distinguishes from many current methods of accessing the semantic web data in that it applies novel ranking strategies to prevent returning search results in disorder. The experimental results show that the framework is feasible and can produce better ordering of semantic search results than directly applying the standard PageRank algorithm on the semantic web.
- Source
- Information processing and management. 44(2008) no.2, S.893-909
-
Metadata and semantics research : 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings (2016)
0.01
0.014712521 = product of:
0.049041733 = sum of:
0.012695382 = weight(_text_:information in 3283) [ClassicSimilarity], result of:
0.012695382 = score(doc=3283,freq=6.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.23515764 = fieldWeight in 3283, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0546875 = fieldNorm(doc=3283)
0.0217631 = weight(_text_:retrieval in 3283) [ClassicSimilarity], result of:
0.0217631 = score(doc=3283,freq=2.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.23394634 = fieldWeight in 3283, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0546875 = fieldNorm(doc=3283)
0.014583254 = product of:
0.029166508 = sum of:
0.029166508 = weight(_text_:22 in 3283) [ClassicSimilarity], result of:
0.029166508 = score(doc=3283,freq=2.0), product of:
0.107692726 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.030753274 = queryNorm
0.2708308 = fieldWeight in 3283, 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=3283)
0.5 = coord(1/2)
0.3 = coord(3/10)
- Abstract
- This book constitutes the refereed proceedings of the 10th Metadata and Semantics Research Conference, MTSR 2016, held in Göttingen, Germany, in November 2016. The 26 full papers and 6 short papers presented were carefully reviewed and selected from 67 submissions. The papers are organized in several sessions and tracks: Digital Libraries, Information Retrieval, Linked and Social Data, Metadata and Semantics for Open Repositories, Research Information Systems and Data Infrastructures, Metadata and Semantics for Agriculture, Food and Environment, Metadata and Semantics for Cultural Collections and Applications, European and National Projects.
- Series
- Communications in computer and information science; 672
-
Kiryakov, A.; Popov, B.; Terziev, I.; Manov, D.; Ognyanoff, D.: Semantic annotation, indexing, and retrieval (2004)
0.01
0.014311615 = product of:
0.047705382 = sum of:
0.0059232777 = weight(_text_:information in 700) [ClassicSimilarity], result of:
0.0059232777 = score(doc=700,freq=4.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.10971737 = fieldWeight in 700, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.03125 = fieldNorm(doc=700)
0.024872115 = weight(_text_:retrieval in 700) [ClassicSimilarity], result of:
0.024872115 = score(doc=700,freq=8.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.26736724 = fieldWeight in 700, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.03125 = fieldNorm(doc=700)
0.01690999 = product of:
0.03381998 = sum of:
0.03381998 = weight(_text_:evaluation in 700) [ClassicSimilarity], result of:
0.03381998 = score(doc=700,freq=4.0), product of:
0.12900078 = queryWeight, product of:
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.030753274 = queryNorm
0.2621688 = fieldWeight in 700, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.03125 = fieldNorm(doc=700)
0.5 = coord(1/2)
0.3 = coord(3/10)
- Abstract
- The Semantic Web realization depends on the availability of a critical mass of metadata for the web content, associated with the respective formal knowledge about the world. We claim that the Semantic Web, at its current stage of development, is in a state of a critical need of metadata generation and usage schemata that are specific, well-defined and easy to understand. This paper introduces our vision for a holistic architecture for semantic annotation, indexing, and retrieval of documents with regard to extensive semantic repositories. A system (called KIM), implementing this concept, is presented in brief and it is used for the purposes of evaluation and demonstration. A particular schema for semantic annotation with respect to real-world entities is proposed. The underlying philosophy is that a practical semantic annotation is impossible without some particular knowledge modelling commitments. Our understanding is that a system for such semantic annotation should be based upon a simple model of real-world entity classes, complemented with extensive instance knowledge. To ensure the efficiency, ease of sharing, and reusability of the metadata, we introduce an upper-level ontology (of about 250 classes and 100 properties), which starts with some basic philosophical distinctions and then goes down to the most common entity types (people, companies, cities, etc.). Thus it encodes many of the domain-independent commonsense concepts and allows straightforward domain-specific extensions. On the basis of the ontology, a large-scale knowledge base of entity descriptions is bootstrapped, and further extended and maintained. Currently, the knowledge bases usually scales between 105 and 106 descriptions. Finally, this paper presents a semantically enhanced information extraction system, which provides automatic semantic annotation with references to classes in the ontology and to instances. The system has been running over a continuously growing document collection (currently about 0.5 million news articles), so it has been under constant testing and evaluation for some time now. On the basis of these semantic annotations, we perform semantic based indexing and retrieval where users can mix traditional information retrieval (IR) queries and ontology-based ones. We argue that such large-scale, fully automatic methods are essential for the transformation of the current largely textual web into a Semantic Web.
-
Sah, M.; Wade, V.: Personalized concept-based search on the Linked Open Data (2015)
0.01
0.013734295 = product of:
0.06867147 = sum of:
0.012436057 = weight(_text_:retrieval in 2511) [ClassicSimilarity], result of:
0.012436057 = score(doc=2511,freq=2.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.13368362 = fieldWeight in 2511, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.03125 = fieldNorm(doc=2511)
0.056235414 = weight(_text_:ranking in 2511) [ClassicSimilarity], result of:
0.056235414 = score(doc=2511,freq=4.0), product of:
0.16634533 = queryWeight, product of:
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.030753274 = queryNorm
0.33806428 = fieldWeight in 2511, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.03125 = fieldNorm(doc=2511)
0.2 = coord(2/10)
- Abstract
- In this paper, we present a novel personalized concept-based search mechanism for the Web of Data based on results categorization. The innovation of the paper comes from combining novel categorization and personalization techniques, and using categorization for providing personalization. In our approach, search results (Linked Open Data resources) are dynamically categorized into Upper Mapping and Binding Exchange Layer (UMBEL) concepts using a novel fuzzy retrieval model. Then, results with the same concepts are grouped together to form categories, which we call conceptlenses. Such categorization enables concept-based browsing of the retrieved results aligned to users' intent or interests. When the user selects a concept lens for exploration, results are immediately personalized. In particular, all concept lenses are personally re-organized according to their similarity to the selected lens. Within the selected concept lens; more relevant results are included using results re-ranking and query expansion, as well as relevant concept lenses are suggested to support results exploration. This allows dynamic adaptation of results to the user's local choices. We also support interactive personalization; when the user clicks on a result, within the interacted lens, relevant lenses and results are included using results re-ranking and query expansion. Extensive evaluations were performed to assess our approach: (i) Performance of our fuzzy-based categorization approach was evaluated on a particular benchmark (~10,000 mappings). The evaluations showed that we can achieve highly acceptable categorization accuracy and perform better than the vector space model. (ii) Personalized search efficacy was assessed using a user study with 32 participants in a tourist domain. The results revealed that our approach performed significantly better than a non-adaptive baseline search. (iii) Dynamic personalization performance was evaluated, which illustrated that our personalization approach is scalable. (iv) Finally, we compared our system with the existing LOD search engines, which showed that our approach is unique.
-
Wang, H.; Liu, Q.; Penin, T.; Fu, L.; Zhang, L.; Tran, T.; Yu, Y.; Pan, Y.: Semplore: a scalable IR approach to search the Web of Data (2009)
0.01
0.01318585 = product of:
0.06592925 = sum of:
0.0062825847 = weight(_text_:information in 1638) [ClassicSimilarity], result of:
0.0062825847 = score(doc=1638,freq=2.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.116372846 = fieldWeight in 1638, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=1638)
0.059646662 = weight(_text_:ranking in 1638) [ClassicSimilarity], result of:
0.059646662 = score(doc=1638,freq=2.0), product of:
0.16634533 = queryWeight, product of:
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.030753274 = queryNorm
0.35857132 = fieldWeight in 1638, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.4090285 = idf(docFreq=537, maxDocs=44218)
0.046875 = fieldNorm(doc=1638)
0.2 = coord(2/10)
- Abstract
- The Web of Data keeps growing rapidly. However, the full exploitation of this large amount of structured data faces numerous challenges like usability, scalability, imprecise information needs and data change. We present Semplore, an IR-based system that aims at addressing these issues. Semplore supports intuitive faceted search and complex queries both on text and structured data. It combines imprecise keyword search and precise structured query in a unified ranking scheme. Scalable query processing is supported by leveraging inverted indexes traditionally used in IR systems. This is combined with a novel block-based index structure to support efficient index update when data changes. The experimental results show that Semplore is an efficient and effective system for searching the Web of Data and can be used as a basic infrastructure for Web-scale Semantic Web search engines.
-
Faaborg, A.; Lagoze, C.: Semantic browsing (2003)
0.01
0.013102811 = product of:
0.043676034 = sum of:
0.0073296824 = weight(_text_:information in 1026) [ClassicSimilarity], result of:
0.0073296824 = score(doc=1026,freq=2.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.13576832 = fieldWeight in 1026, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0546875 = fieldNorm(doc=1026)
0.0217631 = weight(_text_:retrieval in 1026) [ClassicSimilarity], result of:
0.0217631 = score(doc=1026,freq=2.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.23394634 = fieldWeight in 1026, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0546875 = fieldNorm(doc=1026)
0.014583254 = product of:
0.029166508 = sum of:
0.029166508 = weight(_text_:22 in 1026) [ClassicSimilarity], result of:
0.029166508 = score(doc=1026,freq=2.0), product of:
0.107692726 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.030753274 = queryNorm
0.2708308 = fieldWeight in 1026, 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=1026)
0.5 = coord(1/2)
0.3 = coord(3/10)
- Abstract
- We have created software applications that allow users to both author and use Semantic Web metadata. To create and use a layer of semantic content on top of the existing Web, we have (1) implemented a user interface that expedites the task of attributing metadata to resources on the Web, and (2) augmented a Web browser to leverage this semantic metadata to provide relevant information and tasks to the user. This project provides a framework for annotating and reorganizing existing files, pages, and sites on the Web that is similar to Vannevar Bushrsquos original concepts of trail blazing and associative indexing.
- Source
- Research and advanced technology for digital libraries : 7th European Conference, proceedings / ECDL 2003, Trondheim, Norway, August 17-22, 2003
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
-
Fernández, M.; Cantador, I.; López, V.; Vallet, D.; Castells, P.; Motta, E.: Semantically enhanced Information Retrieval : an ontology-based approach (2011)
0.01
0.012126153 = product of:
0.04042051 = sum of:
0.0059232777 = weight(_text_:information in 230) [ClassicSimilarity], result of:
0.0059232777 = score(doc=230,freq=4.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.10971737 = fieldWeight in 230, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.03125 = fieldNorm(doc=230)
0.01758724 = weight(_text_:retrieval in 230) [ClassicSimilarity], result of:
0.01758724 = score(doc=230,freq=4.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.18905719 = fieldWeight in 230, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.03125 = fieldNorm(doc=230)
0.01690999 = product of:
0.03381998 = sum of:
0.03381998 = weight(_text_:evaluation in 230) [ClassicSimilarity], result of:
0.03381998 = score(doc=230,freq=4.0), product of:
0.12900078 = queryWeight, product of:
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.030753274 = queryNorm
0.2621688 = fieldWeight in 230, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.1947007 = idf(docFreq=1811, maxDocs=44218)
0.03125 = fieldNorm(doc=230)
0.5 = coord(1/2)
0.3 = coord(3/10)
- Abstract
- Currently, techniques for content description and query processing in Information Retrieval (IR) are based on keywords, and therefore provide limited capabilities to capture the conceptualizations associated with user needs and contents. Aiming to solve the limitations of keyword-based models, the idea of conceptual search, understood as searching by meanings rather than literal strings, has been the focus of a wide body of research in the IR field. More recently, it has been used as a prototypical scenario (or even envisioned as a potential "killer app") in the Semantic Web (SW) vision, since its emergence in the late nineties. However, current approaches to semantic search developed in the SW area have not yet taken full advantage of the acquired knowledge, accumulated experience, and technological sophistication achieved through several decades of work in the IR field. Starting from this position, this work investigates the definition of an ontology-based IR model, oriented to the exploitation of domain Knowledge Bases to support semantic search capabilities in large document repositories, stressing on the one hand the use of fully fledged ontologies in the semantic-based perspective, and on the other hand the consideration of unstructured content as the target search space. The major contribution of this work is an innovative, comprehensive semantic search model, which extends the classic IR model, addresses the challenges of the massive and heterogeneous Web environment, and integrates the benefits of both keyword and semantic-based search. Additional contributions include: an innovative rank fusion technique that minimizes the undesired effects of knowledge sparseness on the yet juvenile SW, and the creation of a large-scale evaluation benchmark, based on TREC IR evaluation standards, which allows a rigorous comparison between IR and SW approaches. Conducted experiments show that our semantic search model obtained comparable and better performance results (in terms of MAP and P@10 values) than the best TREC automatic system.
-
Scheir, P.; Pammer, V.; Lindstaedt, S.N.: Information retrieval on the Semantic Web : does it exist? (2007)
0.01
0.011983174 = product of:
0.059915867 = sum of:
0.016389668 = weight(_text_:information in 4329) [ClassicSimilarity], result of:
0.016389668 = score(doc=4329,freq=10.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.3035872 = fieldWeight in 4329, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0546875 = fieldNorm(doc=4329)
0.0435262 = weight(_text_:retrieval in 4329) [ClassicSimilarity], result of:
0.0435262 = score(doc=4329,freq=8.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.46789268 = fieldWeight in 4329, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0546875 = fieldNorm(doc=4329)
0.2 = coord(2/10)
- Abstract
- Plenty of contemporary attempts to search exist that are associated with the area of Semantic Web. But which of them qualify as information retrieval for the Semantic Web? Do such approaches exist? To answer these questions we take a look at the nature of the Semantic Web and Semantic Desktop and at definitions for information and data retrieval. We survey current approaches referred to by their authors as information retrieval for the Semantic Web or that use Semantic Web technology for search.
- Source
- Lernen - Wissen - Adaption : workshop proceedings / LWA 2007, Halle, September 2007. Martin Luther University Halle-Wittenberg, Institute for Informatics, Databases and Information Systems. Hrsg.: Alexander Hinneburg
-
Studer, R.; Studer, H.-P.; Studer, A.: Semantisches Knowledge Retrieval (2001)
0.01
0.011651632 = product of:
0.05825816 = sum of:
0.0125651695 = weight(_text_:information in 4322) [ClassicSimilarity], result of:
0.0125651695 = score(doc=4322,freq=8.0), product of:
0.05398669 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.030753274 = queryNorm
0.23274569 = fieldWeight in 4322, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=4322)
0.04569299 = weight(_text_:retrieval in 4322) [ClassicSimilarity], result of:
0.04569299 = score(doc=4322,freq=12.0), product of:
0.093026035 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.030753274 = queryNorm
0.49118498 = fieldWeight in 4322, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=4322)
0.2 = coord(2/10)
- Abstract
- Dieses Whitepaper befasst sich mit der Integration semantischer Technologien in bestehende Ansätze des Information Retrieval und die damit verbundenen weitreichenden Auswirkungen auf Effizienz und Effektivität von Suche und Navigation in Dokumenten. Nach einer Einbettung in die Problematik des Wissensmanagement aus Sicht der Informationstechnik folgt ein Überblick zu den Methoden des Information Retrieval. Anschließend werden die semantischen Technologien "Wissen modellieren - Ontologie" und "Neues Wissen ableiten - Inferenz" vorgestellt. Ein Integrationsansatz wird im Folgenden diskutiert und die entstehenden Mehrwerte präsentiert. Insbesondere ergeben sich Erweiterungen hinsichtlich einer verfeinerten Suchunterstützung und einer kontextbezogenen Navigation sowie die Möglichkeiten der Auswertung von regelbasierten Zusammenhängen und einfache Integration von strukturierten Informationsquellen. Das Whitepaper schließt mit einem Ausblick auf die zukünftige Entwicklung des WWW hin zu einem Semantic Web und die damit verbundenen Implikationen für semantische Technologien.
- Content
- Inhalt: 1. Einführung - 2. Wissensmanagement - 3. Information Retrieval - 3.1. Methoden und Techniken - 3.2. Information Retrieval in der Anwendung - 4. Semantische Ansätze - 4.1. Wissen modellieren - Ontologie - 4.2. Neues Wissen inferieren - 5. Knowledge Retrieval in der Anwendung - 6. Zukunftsaussichten - 7. Fazit
