The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas

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The numbers in bold are the best results. Table 4 shows mean average precision @10 for SIF and other four alternative algorithms based on LDA. Table 5 presents performance of alternative approaches for ontology evolutions.

TTF performance in selected topics. Table 7 shows agreement between annotators (including EDAM) and average agreement of each annotator. Table 8 shows countries with the highest number of downloads.

Table 9 presents countries with the highest number of topic requests. Algorithm 1 is the Klink-2 algorithm used to generate CSO. Figure 1 shows workflow of the CSO Classifier.

Figure 2 is the STM interface. Figure 3 shows portion of the graph view showing the taxonomies of the topics associated with the input conference and one of the recommended editorial products. Figure 4 shows average F-measure between each expert/algorithm and all the other experts for the SW topic.

The red line represents the average F-measure of the experts. Figure 5 is the overview of the Computer Science Ontology Portal. Figure 6 is the homepage of the Computer Science Ontology Portal.

Figure 7 is the screenshot of the resource page related to the topic “Semantic Web” (Overview). Figure 8 shows screenshot of the resource page related to the topic “Semantic Web” (Compact). Figure 9 si the screenshot of the resource page related to the topic “Semantic Web” (Detailed).

Figure 10 shows the form for providing feedback about the topic “ontology mapping”. Figure 11 is the form for suggesting new relationships about the topic “ontology mapping”. Figure 12 shows My Contribution page where users can review their own feedback.

Figure 13 is the screenshot of the Editorial Panel available in the CSO Portal. Figure 14 shows one of the several paths connecting Deep Learning with the field of Blockchain. Figure 15 shows top 25 topics requested (logarithmic scale).