-
Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004)
0.06
0.0619644 = sum of:
0.037528638 = product of:
0.11258591 = sum of:
0.11258591 = weight(_text_:objects in 2419) [ClassicSimilarity], result of:
0.11258591 = score(doc=2419,freq=2.0), product of:
0.3195352 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.060118705 = queryNorm
0.35234275 = fieldWeight in 2419, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.046875 = fieldNorm(doc=2419)
0.33333334 = coord(1/3)
0.024435764 = product of:
0.04887153 = sum of:
0.04887153 = weight(_text_:22 in 2419) [ClassicSimilarity], result of:
0.04887153 = score(doc=2419,freq=2.0), product of:
0.21052547 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.060118705 = queryNorm
0.23214069 = fieldWeight in 2419, 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=2419)
0.5 = coord(1/2)
- Abstract
- The digital library system Daffodil is targeted at strategic support of users during the information search process. For searching, exploring and managing digital library objects it provides user-customisable information seeking patterns over a federation of heterogeneous digital libraries. In this paper evaluation results with respect to retrieval effectiveness, efficiency and user satisfaction are presented. The analysis focuses on strategic support for the scientific work-flow. Daffodil supports the whole work-flow, from data source selection over information seeking to the representation, organisation and reuse of information. By embedding high level search functionality into the scientific work-flow, the user experiences better strategic system support due to a more systematic work process. These ideas have been implemented in Daffodil followed by a qualitative evaluation. The evaluation has been conducted with 28 participants, ranging from information seeking novices to experts. The results are promising, as they support the chosen model.
- Date
- 16.11.2008 16:22:48
-
Lehtokangas, R.; Järvelin, K.: Consistency of textual expression in newspaper articles : an argument for semantically based query expansion (2001)
0.06
0.05587824 = product of:
0.11175648 = sum of:
0.11175648 = product of:
0.22351296 = sum of:
0.22351296 = weight(_text_:news in 4485) [ClassicSimilarity], result of:
0.22351296 = score(doc=4485,freq=12.0), product of:
0.31512353 = queryWeight, product of:
5.2416887 = idf(docFreq=635, maxDocs=44218)
0.060118705 = queryNorm
0.7092868 = fieldWeight in 4485, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
5.2416887 = idf(docFreq=635, maxDocs=44218)
0.0390625 = fieldNorm(doc=4485)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- This article investigates how consistent different newspapers are in their choice of words when writing about the same news events. News articles on the same news events were taken from three Finnish newspapers and compared in regard to their central concepts and words representing the concepts in the news texts. Consistency figures were calculated for each set of three articles (the total number of sets was sixty). Inconsistency in words and concepts was found between news articles from different newspapers. The mean value of consistency calculated on the basis of words was 65 per cent; this however depended on the article length. For short news wires consistency was 83 per cent while for long articles it was only 47 per cent. At the concept level, consistency was considerably higher, ranging from 92 per cent to 97 per cent between short and long articles. The articles also represented three categories of topic (event, process and opinion). Statistically significant differences in consistency were found in regard to length but not in regard to the categories of topic. We argue that the expression inconsistency is a clear sign of a retrieval problem and that query expansion based on semantic relationships can significantly improve retrieval performance on free-text sources.
-
Bradford, R.B.: Relationship discovery in large text collections using Latent Semantic Indexing (2006)
0.05
0.052790023 = product of:
0.10558005 = sum of:
0.10558005 = sum of:
0.07299902 = weight(_text_:news in 1163) [ClassicSimilarity], result of:
0.07299902 = score(doc=1163,freq=2.0), product of:
0.31512353 = queryWeight, product of:
5.2416887 = idf(docFreq=635, maxDocs=44218)
0.060118705 = queryNorm
0.2316521 = fieldWeight in 1163, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.2416887 = idf(docFreq=635, maxDocs=44218)
0.03125 = fieldNorm(doc=1163)
0.03258102 = weight(_text_:22 in 1163) [ClassicSimilarity], result of:
0.03258102 = score(doc=1163,freq=2.0), product of:
0.21052547 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.060118705 = queryNorm
0.15476047 = fieldWeight in 1163, 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=1163)
0.5 = coord(1/2)
- Abstract
- This paper addresses the problem of information discovery in large collections of text. For users, one of the key problems in working with such collections is determining where to focus their attention. In selecting documents for examination, users must be able to formulate reasonably precise queries. Queries that are too broad will greatly reduce the efficiency of information discovery efforts by overwhelming the users with peripheral information. In order to formulate efficient queries, a mechanism is needed to automatically alert users regarding potentially interesting information contained within the collection. This paper presents the results of an experiment designed to test one approach to generation of such alerts. The technique of latent semantic indexing (LSI) is used to identify relationships among entities of interest. Entity extraction software is used to pre-process the text of the collection so that the LSI space contains representation vectors for named entities in addition to those for individual terms. In the LSI space, the cosine of the angle between the representation vectors for two entities captures important information regarding the degree of association of those two entities. For appropriate choices of entities, determining the entity pairs with the highest mutual cosine values yields valuable information regarding the contents of the text collection. The test database used for the experiment consists of 150,000 news articles. The proposed approach for alert generation is tested using a counterterrorism analysis example. The approach is shown to have significant potential for aiding users in rapidly focusing on information of potential importance in large text collections. The approach also has value in identifying possible use of aliases.
- 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]
-
Gödert, W.: Navigation und Konzepte für ein interaktives Retrieval im OPAC : Oder: Von der Informationserschließung zur Wissenserkundung (2004)
0.04
0.03649951 = product of:
0.07299902 = sum of:
0.07299902 = product of:
0.14599805 = sum of:
0.14599805 = weight(_text_:news in 2195) [ClassicSimilarity], result of:
0.14599805 = score(doc=2195,freq=2.0), product of:
0.31512353 = queryWeight, product of:
5.2416887 = idf(docFreq=635, maxDocs=44218)
0.060118705 = queryNorm
0.4633042 = fieldWeight in 2195, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.2416887 = idf(docFreq=635, maxDocs=44218)
0.0625 = fieldNorm(doc=2195)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Source
- AKMB-news. 10(2004) H.1, S.27-30
-
Boyack, K.W.; Wylie,B.N.; Davidson, G.S.: Information Visualization, Human-Computer Interaction, and Cognitive Psychology : Domain Visualizations (2002)
0.03
0.028797824 = product of:
0.057595648 = sum of:
0.057595648 = product of:
0.115191296 = sum of:
0.115191296 = weight(_text_:22 in 1352) [ClassicSimilarity], result of:
0.115191296 = score(doc=1352,freq=4.0), product of:
0.21052547 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.060118705 = queryNorm
0.54716086 = fieldWeight in 1352, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.078125 = fieldNorm(doc=1352)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 22. 2.2003 17:25:39
22. 2.2003 18:17:40
-
Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
0.03
0.028508391 = product of:
0.057016782 = sum of:
0.057016782 = product of:
0.114033565 = sum of:
0.114033565 = weight(_text_:22 in 2134) [ClassicSimilarity], result of:
0.114033565 = score(doc=2134,freq=2.0), product of:
0.21052547 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.060118705 = queryNorm
0.5416616 = fieldWeight in 2134, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.109375 = fieldNorm(doc=2134)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 30. 3.2001 13:32:22
-
Kulyukin, V.A.; Settle, A.: Ranked retrieval with semantic networks and vector spaces (2001)
0.03
0.025019092 = product of:
0.050038185 = sum of:
0.050038185 = product of:
0.15011455 = sum of:
0.15011455 = weight(_text_:objects in 6934) [ClassicSimilarity], result of:
0.15011455 = score(doc=6934,freq=2.0), product of:
0.3195352 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.060118705 = queryNorm
0.46979034 = fieldWeight in 6934, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.0625 = fieldNorm(doc=6934)
0.33333334 = coord(1/3)
0.5 = coord(1/2)
- Abstract
- The equivalence of semantic networks with spreading activation and vector spaces with dot product is investigated under ranked retrieval. Semantic networks are viewed as networks of concepts organized in terms of abstraction and packaging relations. It is shown that the two models can be effectively constructed from each other. A formal method is suggested to analyze the models in terms of their relative performance in the same universe of objects
-
Tseng, Y.-H.: Solving vocabulary problems with interactive query expansion (1998)
0.02
0.022812195 = product of:
0.04562439 = sum of:
0.04562439 = product of:
0.09124878 = sum of:
0.09124878 = weight(_text_:news in 5159) [ClassicSimilarity], result of:
0.09124878 = score(doc=5159,freq=2.0), product of:
0.31512353 = queryWeight, product of:
5.2416887 = idf(docFreq=635, maxDocs=44218)
0.060118705 = queryNorm
0.28956512 = fieldWeight in 5159, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.2416887 = idf(docFreq=635, maxDocs=44218)
0.0390625 = fieldNorm(doc=5159)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- One of the major causes of search failures in information retrieval systems is vocabulary mismatch. Presents a solution to the vocabulary problem through 2 strategies known as term suggestion (TS) and term relevance feedback (TRF). In TS, collection specific terms are extracted from the text collection. These terms and their frequencies constitute the keyword database for suggesting terms in response to users' queries. One effect of this term suggestion is that it functions as a dynamic directory if the query is a general term that contains broad meaning. In term relevance feedback, terms extracted from the top ranked documents retrieved from the previous query are shown to users for relevance feedback. In the experiment, interactive TS provides very high precision rates while achieving similar recall rates as n-gram matching. Local TRF achieves improvement in both precision and recall rate in a full text news database and degrades slightly in recall rate in bibliographic databases due to the very limited source of information for feedback. In terms of Rijsbergen's combined measure of recall and precision, both TS and TRF achieve better performance than n-gram matching, which implies that the greater improvement in precision rate compensates the slight degradation in recall rate for TS and TRF
-
Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016)
0.02
0.020363137 = product of:
0.040726274 = sum of:
0.040726274 = product of:
0.08145255 = sum of:
0.08145255 = weight(_text_:22 in 2751) [ClassicSimilarity], result of:
0.08145255 = score(doc=2751,freq=2.0), product of:
0.21052547 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.060118705 = queryNorm
0.38690117 = fieldWeight in 2751, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.078125 = fieldNorm(doc=2751)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 1. 2.2016 18:25:22
-
Kozikowski, P. et al.: Support of part-whole relations in query answering (2016)
0.02
0.020363137 = product of:
0.040726274 = sum of:
0.040726274 = product of:
0.08145255 = sum of:
0.08145255 = weight(_text_:22 in 2754) [ClassicSimilarity], result of:
0.08145255 = score(doc=2754,freq=2.0), product of:
0.21052547 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.060118705 = queryNorm
0.38690117 = fieldWeight in 2754, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.078125 = fieldNorm(doc=2754)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 1. 2.2016 18:25:22
-
Marx, E. et al.: Exploring term networks for semantic search over RDF knowledge graphs (2016)
0.02
0.020363137 = product of:
0.040726274 = sum of:
0.040726274 = product of:
0.08145255 = sum of:
0.08145255 = weight(_text_:22 in 3279) [ClassicSimilarity], result of:
0.08145255 = score(doc=3279,freq=2.0), product of:
0.21052547 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.060118705 = queryNorm
0.38690117 = fieldWeight in 3279, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.078125 = fieldNorm(doc=3279)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Source
- Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
-
Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016)
0.02
0.020363137 = product of:
0.040726274 = sum of:
0.040726274 = product of:
0.08145255 = sum of:
0.08145255 = weight(_text_:22 in 3280) [ClassicSimilarity], result of:
0.08145255 = score(doc=3280,freq=2.0), product of:
0.21052547 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.060118705 = queryNorm
0.38690117 = fieldWeight in 3280, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.078125 = fieldNorm(doc=3280)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Source
- Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
-
Sacco, G.M.: Dynamic taxonomies and guided searches (2006)
0.02
0.020158477 = product of:
0.040316954 = sum of:
0.040316954 = product of:
0.08063391 = sum of:
0.08063391 = weight(_text_:22 in 5295) [ClassicSimilarity], result of:
0.08063391 = score(doc=5295,freq=4.0), product of:
0.21052547 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.060118705 = queryNorm
0.38301262 = fieldWeight in 5295, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0546875 = fieldNorm(doc=5295)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 22. 7.2006 17:56:22
-
Drexel, G.: Knowledge engineering for intelligent information retrieval (2001)
0.02
0.018764319 = product of:
0.037528638 = sum of:
0.037528638 = product of:
0.11258591 = sum of:
0.11258591 = weight(_text_:objects in 4043) [ClassicSimilarity], result of:
0.11258591 = score(doc=4043,freq=2.0), product of:
0.3195352 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.060118705 = queryNorm
0.35234275 = fieldWeight in 4043, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.046875 = fieldNorm(doc=4043)
0.33333334 = coord(1/3)
0.5 = coord(1/2)
- Abstract
- This paper presents a clustered approach to designing an overall ontological model together with a general rule-based component that serves as a mapping device. By observational criteria, a multi-lingual team of experts excerpts concepts from general communication in the media. The team, then, finds equivalent expressions in English, German, French, and Spanish. On the basis of a set of ontological and lexical relations, a conceptual network is built up. Concepts are thought to be universal. Objects unique in time and space are identified by names and will be explained by the universals as their instances. Our approach relies on multi-relational descriptions of concepts. It provides a powerful tool for documentation and conceptual language learning. First and foremost, our multi-lingual, polyhierarchical ontology fills the gap of semantically-based information retrieval by generating enhanced and improved queries for internet search
-
Prieto-Díaz, R.: ¬A faceted approach to building ontologies (2002)
0.02
0.018764319 = product of:
0.037528638 = sum of:
0.037528638 = product of:
0.11258591 = sum of:
0.11258591 = weight(_text_:objects in 2259) [ClassicSimilarity], result of:
0.11258591 = score(doc=2259,freq=2.0), product of:
0.3195352 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.060118705 = queryNorm
0.35234275 = fieldWeight in 2259, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.046875 = fieldNorm(doc=2259)
0.33333334 = coord(1/3)
0.5 = coord(1/2)
- Abstract
- An ontology is "an explicit conceptualization of a domain of discourse, and thus provides a shared and common understanding of the domain." We have been producing ontologies for millennia to understand and explain our rationale and environment. From Plato's philosophical framework to modern day classification systems, ontologies are, in most cases, the product of extensive analysis and categorization. Only recently has the process of building ontologies become a research topic of interest. Today, ontologies are built very much ad-hoc. A terminology is first developed providing a controlled vocabulary for the subject area or domain of interest, then it is organized into a taxonomy where key concepts are identified, and finally these concepts are defined and related to create an ontology. The intent of this paper is to show that domain analysis methods can be used for building ontologies. Domain analysis aims at generic models that represent groups of similar systems within an application domain. In this sense, it deals with categorization of common objects and operations, with clear, unambiguous definitions of them and with defining their relationships.
-
Efthimiadis, E.N.: End-users' understanding of thesaural knowledge structures in interactive query expansion (1994)
0.02
0.01629051 = product of:
0.03258102 = sum of:
0.03258102 = product of:
0.06516204 = sum of:
0.06516204 = weight(_text_:22 in 5693) [ClassicSimilarity], result of:
0.06516204 = score(doc=5693,freq=2.0), product of:
0.21052547 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.060118705 = queryNorm
0.30952093 = fieldWeight in 5693, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=5693)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 30. 3.2001 13:35:22
-
Calegari, S.; Sanchez, E.: Object-fuzzy concept network : an enrichment of ontologies in semantic information retrieval (2008)
0.02
0.015636934 = product of:
0.031273868 = sum of:
0.031273868 = product of:
0.0938216 = sum of:
0.0938216 = weight(_text_:objects in 2393) [ClassicSimilarity], result of:
0.0938216 = score(doc=2393,freq=2.0), product of:
0.3195352 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.060118705 = queryNorm
0.29361898 = fieldWeight in 2393, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.0390625 = fieldNorm(doc=2393)
0.33333334 = coord(1/3)
0.5 = coord(1/2)
- Abstract
- This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases.
-
Schaefer, A.; Jordan, M.; Klas, C.-P.; Fuhr, N.: Active support for query formulation in virtual digital libraries : a case study with DAFFODIL (2005)
0.02
0.015636934 = product of:
0.031273868 = sum of:
0.031273868 = product of:
0.0938216 = sum of:
0.0938216 = weight(_text_:objects in 4296) [ClassicSimilarity], result of:
0.0938216 = score(doc=4296,freq=2.0), product of:
0.3195352 = queryWeight, product of:
5.315071 = idf(docFreq=590, maxDocs=44218)
0.060118705 = queryNorm
0.29361898 = fieldWeight in 4296, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.315071 = idf(docFreq=590, maxDocs=44218)
0.0390625 = fieldNorm(doc=4296)
0.33333334 = coord(1/3)
0.5 = coord(1/2)
- Abstract
- Daffodil is a front-end to federated, heterogeneous digital libraries targeting at strategic support of users during the information seeking process. This is done by offering a variety of functions for searching, exploring and managing digital library objects. However, the distributed search increases response time and the conceptual model of the underlying search processes is inherently weaker. This makes query formulation harder and the resulting waiting times can be frustrating. In this paper, we investigate the concept of proactive support during the user's query formulation. For improving user efficiency and satisfaction, we implemented annotations, proactive support and error markers on the query form itself. These functions decrease the probability for syntactical or semantical errors in queries. Furthermore, the user is able to make better tactical decisions and feels more confident that the system handles the query properly. Evaluations with 30 subjects showed that user satisfaction is improved, whereas no conclusive results were received for efficiency.
-
Fieldhouse, M.; Hancock-Beaulieu, M.: ¬The design of a graphical user interface for a highly interactive information retrieval system (1996)
0.01
0.014254196 = product of:
0.028508391 = sum of:
0.028508391 = product of:
0.057016782 = sum of:
0.057016782 = weight(_text_:22 in 6958) [ClassicSimilarity], result of:
0.057016782 = score(doc=6958,freq=2.0), product of:
0.21052547 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.060118705 = queryNorm
0.2708308 = fieldWeight in 6958, 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=6958)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Source
- Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
-
Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998)
0.01
0.014254196 = product of:
0.028508391 = sum of:
0.028508391 = product of:
0.057016782 = sum of:
0.057016782 = weight(_text_:22 in 1319) [ClassicSimilarity], result of:
0.057016782 = score(doc=1319,freq=2.0), product of:
0.21052547 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.060118705 = queryNorm
0.2708308 = fieldWeight in 1319, 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=1319)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 1. 8.1996 22:08:06
