Figure 5 – uploaded by TANVEER IRSHAD SIDDIQUI

Figure 2.1: Basic Information Retrieval Process  assumed relevant and returned to the user.

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].

Figure 2.7: Recall-Precision graph  them can not be avoided.

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 &amp; query representation, matching &amp; similarity Measure

Figure 3.3: Steps in preprocessing documents  document, in the collection, or some combination of both.

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 &amp; query representation, matching &amp; similarity Measure

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.

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 &amp; 7  Techniques for Improving Effectiveness in Information Retrieval

Figure 4.7: Comparison of seven different retrieval models

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.

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

Figure 4.18: Ranking of first ten documents returned by LYCOS after second stage  Techniques for Improving Effectiveness in Information Retrieval

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

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