Figure 4 7: Comparison of seven different retrieval models
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Figure 2.1: Basic Information Retrieval Process assumed relevant and returned to the user. Table 2.1: Different types of referents Information Retrieval and Conceptual Graphs: Basic Concepts Figure 2.5: Conceptual graph of sentence (2.2) form of a CG representing the English sentence: Any statement expressed in any one of these notations can be automatically translated Figure2.6: Derivation graph of a conceptual graph v proceed in the same direction [2]. collections are presented. Figure 2.7: Recall-Precision graph them can not be avoided. Table 3.1: Vector representation of Sample Documents after stemming Figure 3.1: Sample documents Figure 3.2: Results of application of indexing steps and the second triple correspond to the weighting strategy used for the query. Table 3.2: Calculating weight with different options for the three weighting factors Chapter 3. Basic Framework: Document & query representation, matching & similarity Measure Figure 3.3: Steps in preprocessing documents document, in the collection, or some combination of both. Figure 3.4: Representation in two-dimensional vector space and the query are represented as 6), 02 and 03 respectively. in this space using term weights as coordinates. The angles between these documents The simplest way of comparing document and query is by counting the number of document and the query have been shown. Documents dl, d2 and d3 are representec Figure 3.5: The meaning triangle [155] (Sowa, 1984) actually a formalized representation of the belief that people remember particular Figure 3.6: General Architecture of Conceptual graph Construction context and the knowledge base. Table 3.3: Syntactic patterns for noun phrase Chapter 3 Basic Framework: Document & query representation, matching & similarity Measure Figure 3.8: Sample Document text scientific information . The American chemical society has begun an analysis of the rele of the Figure 3.9: Conceptual graph for the sample document Document Representation and Similarity Measures Figure 4.1: Retrieval Model Fig. 4.1 shows our retrieval model. Two models are used in conjunction. Table 4.1: Retrieval results of various models (Averaged over all the queries, Techniques for Improving Effectiveness in Information Retrieval Figure 4.3: Recall-Precision curve for Model 1 vs. Model 3 Figure 4.4: Recall-Precision curve for Model 1 vs. Model 4 Techniques for Improving Effectiveness in Information Retrieval Figure 4.5: Recall-Precision curve for Model 1 vs. Model 5 Figure 4.6: Comparison of Recall-precision behavior of model 6 with model 1 & 7 Techniques for Improving Effectiveness in Information Retrieval Figure 4.8: Recall-Precision curve for CACM collection (averaged over 64 queries) for BM25 scheme and “modified vector” model Techniques for Improving Effectiveness in Information Retrieval Figure. 4.9: Part of Concept hierarchy Figure 4.10: Intersection of conceptual graphs G1 and G2 These values have been set empirically. Techniques for Improving Effectiveness in Information Retrieval Figure 4.11: Recall-Precision curve for query 63 of the CACM-3204 collection Table 4.4: Percentage increase in precision Figure 4.12: Recall-Precision curve for query 30 of the CACM-3204 collection Techniques for Improving Effectiveness in Information Retrieval Figure 4.13: Recall-precision curve for CACM query 19 Figure 4.14: Average Recall-Precision curve for the subset of CACM queries Techniques for Improving Effectiveness in Information Retrieval Figure 4.16: Ranks of documents after first and second stage onsideration for query | have been listed in fig.4.15 ranking have been achieved for other queries also. Figure 4.17: Ranking after first and second stage of retrieval for query 2 and 3. documents being considered can be found in Table 4.6. Similar improvements in Figure 4.18: Ranking of first ten documents returned by LYCOS after second stage Techniques for Improving Effectiveness in Information Retrieval Table 5.1: Average 11AvgP score for a subset of CACM queries shows average recall precision curve for this comparison. Figure 5.2. Average Recall-precision curves for a subset of CACM queries Automatic Query Expansion through Conceptual Graph Figure 5.3: Average Recall-Precision curves for a subset of CACM queries for techniques (a), (b) and blind expansion Figure 6.4: Recall-precision curve for CACM collection for vector model and our model using CGSim I Capturing Semantics through Relation Matching Figure 6.5: Recall-precision curve for CACM collection for vector model and our model using CGSim IT Table 6.2: Comparison of retrieval results for a subset of CACM queries Figure 6.6: Recall-precision behavior of a subset of CACM queries with CGSim II Capturing Semantics through Relation Matching Figure 6.7: Recall-precision curve for CACM collection for vector model and our model using CGSim ITI Capturing Semantics through Relation Matching Figure 6.8: Recall-precision curve for CACM collection for vector model and our model using CGSim IV Format of untagged and tagged file(%% marks a comment and is ignored by the Il. 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