## 6 Experiments
### Experimental Results  
Previously [7], we conducted experiments with the STEP methodology using the NCBO Annotator as a concept extraction technique. In this study, we have incorporated topic modeling and n-gram modeling into STEP, in order to enhance the accuracy of generated profiles by minimizing the effects of domain-specific annotation tools on the final results. This section presents our experiments and the comparative analysis performed among expertise profiles generated by the following approaches: (i) expertise profiles created by the generic STEP methodology, where concepts are extracted from micro-contributions using the NCBO Annotator tool; _i.e._, the original approach [7]; (ii) expertise profiles created by integrating Topic Modeling with the Concept Extraction module in STEP; _i.e._, the topic modeling approach and (iii) expertise profiles created by integrating N-gramModeling with the Concept Extraction module in STEP; _i.e._, the n-gram modeling approach. All methods were performed on micro-contributions for the 29 authors selected from the Molecular and Cellular Biology and Genetics Wiki projects. In addition, we have included comparisons among profiles generated by the above-mentioned approaches and profiles created by two traditional IR-inspired expertise-finding systems. As previously mentioned, the baseline consists of expertise profiles created by the designated authors upon joining the corresponding projects. In terms of efficiency measures, we have considered _F-score_, _Precision_ and _Recall_ as defined in the context of Information Retrieval. In the context of our experiments, the value of _F-score_ provides a measure of the accuracy of profiles generated by each approach through considering both Precision and Recall. F-score can be interpreted as a weighted average of the precision and recall, with its best value at 1 and worst score at 0. For a given expertise profile, _Precision_ is the number of correct concepts (concepts matching the baseline) divided by the number of all returned concepts (total number of concepts in the generated profile) and _Recall_ is the number of correct concepts (concepts matching the baseline) divided by the number of concepts that should have been returned (total number of concepts in the baseline). As described in Section 4, virtual concepts included in expertise profiles are associated with a weight. Figure 2 tracks the values of F-Score for different concept weight thresholds. If we do not set a concept weight threshold (all virtual concepts are included in generated profiles) or set thresholds less than 0.5 (virtual concepts with weight \(<\) 0.5 are included in generated profiles), the original approach achieves the highest F-score. This is due to the fact that profiles generated by topic modeling and n-gram modeling approaches contain more noise since they include additional concepts representing the topics and n-grams derived from experts' micro-contributions. Subsequently increasing the concept weight threshold from 0.5 to 1.2 results in consistently higher F-score values achieved by topic modeling and n-gram modeling approaches, with the highest F-score achieved by n-gram modeling at concept weight threshold of 1 (31.94%). The enhanced accuracy of profiles generated by topic and n-gram modeling approaches, is partly due to the presence of concepts representing the topics and n-grams derived by these approaches, as well as a reduction of noise in the profiles as a result of increasing the concept weight threshold. Figure 2: F-score at different concept weight thresholds. Increasing the concept weight threshold from 1.2 to 1.5, results in the decline of the value of F-score for all approaches, with n-gram modeling achieving the highest accuracy at all thresholds in this range. The decline in the value of F-score is due to the exclusion of a large number of concepts from generated profiles as a result of higher thresholds, achieving a mining value for the topic modeling and n-gram modeling approaches at a weight threshold of 1.6.