Using Similarity Metrics for Matching Lifelong Learners
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The document discusses using similarity metrics to match lifelong learners based on their timelines of educational and professional experiences. It presents a model to represent user profiles, learning profiles, and timelines as RDF triples. Various encoding schemes and similarity metrics are evaluated for comparing timelines, including Levenshtein distance and Jaro-Wunsch. The goal is to search for "people like me" by ranking users based on timeline similarity.
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Using Similarity Metrics for Matching Lifelong Learners
- 1. Using Similarity Metrics for Matching Lifelong Learners Nicolas Van Labeke Alexandra Poulovassilis George Magoulas
- 12. Tokenisation of Timelines CL-10.1.0.0-3.1.0.0 DG-10.1.0.0-3.1.0.0 WK-4.0.0.0-7.2.1.2 WK-11.0.0.0-3.1.3.2 WK-3.0.0.0-4.1.3.6 MV-0.0.0.0-0.0.0.0 UN-6.4.0.0-6.3.0.0 CL-10-3 DG-10-3 WK-4-7 WK-11-3 WK-3-4 MV-0-0 UN-6-6 CL-- DG-- WK-- WK-- WK-- UN-- Expressivity
- 13. Similarity Metrics SimMetrics JAVA package – http://www.dcs.shef.ac.uk/~sam/simmetrics.html Levenshtein Needleman – Wunsch Jaro Matching Coefficient Euclidean Distance Block Distance Jaccard Similarity Cosine Similarity Dice Similarity Overlap Coefficient
- 14. Encoding of some timelines Cl-00 Un-00 Mv-00 Wk-10 One of the episodes of the source timeline is substituted by a variant of an existing episode. SB v Cl-00 Un-00 Mv-00 Un-00 One of the episodes of the source timeline is substituted by an existing episode. SB e Cl-00 Un-00 Mv-00 Bs-00 One of the episodes of the source timeline is substituted by a new one ( different from all existing ones ). SB n Cl-00 Mv-00 Wk-00 One of the episodes of the source timeline is removed. RM u Cl-00 Un-00 Mv-00 The last episode is removed from the source timeline. RM w Cl-00 Un-00 Mv-00 Wk-00 Bs-00 A new episode ( different from all existing ones ) is added to the timeline. AD n Cl-00 Un-00 Mv-00 Wk-00 Wk-00 A new work episode ( similar to an existing one ) is added to the timeline. AD e Un-00 Wk-00 Cl-00 Mv-00 A timeline containing the same episodes as the source but in a totally different order (i.e. no episode is at the same position in the string). Re Cl-00 Un-00 Mv-00 Wk-00 A timeline similar to the source. Id Cl-00 Un-00 Mv-00 Wk-00 The original timeline used as the source for the similarity measure Source Encoding Description ID
- 15. Comparison of Metrics 0.75 1 0.75 1 1 1 1 1 1 Overlap Coefficient 0.75 0.86 0.75 0.86 0.86 0.89 1 1 1 Dice Similarity 0.75 0.87 0.75 0.87 0.87 0.89 1 1 1 Cosine Similarity 0.6 0.75 0.6 0.75 0.75 0.8 1 1 1 Jaccard Similarity 0.75 0.75 0.75 0.86 0.86 0.89 0.89 1 1 Block Distance 0.75 0.75 0.75 0.8 0.8 0.84 0.84 1 1 Euclidean Distance 0.75 0.75 0.75 0.75 0.75 0.8 0.8 1 1 Matching Coefficient 0.83 0.83 0.83 0.92 0.92 0.93 0.93 0.72 1 Jaro 0.88 0.75 0.75 0.75 0.75 0.8 0.8 0 1 Needleman - Wunsch 0.75 0.75 0.75 0.75 0.75 0.8 0.8 0 1 Levenshtein SB v SB e SB n RM u RM w AD n AD e RE ID
Editor's Notes
- Access to resources and facilities Share information about pathways Reflect on current and future pathways
- String metrics (also known as similarity metrics ) are a class of textual based metrics resulting in a similarity or dissimilarity ( distance ) score between two pairs of text strings for approximate matching or comparison and in fuzzy string searching . For example the strings "Sam" and "Samuel" can be considered (although not the same) to a degree similar. A string metric provides a floating point number indicating an algorithm-specific indication of similarity. The most widely known (although rudimentary) string metric is Levenshtein Distance (also known as Edit Distance), which operates between two input strings, returning a score equivalent to the number of transpositions , substitutions and deletions needed in order to transform one input string into another. Simplistic string metrics such as Levenshtein distance have expanded to include phonetic, token , grammatical and character-based methods of statistical comparisons .