In this paper, we report our participation in the Task 2: Triple Scoring of WSDM Cup challenge 2017. In this task, we were provided with triples of type-like relations which were given human-annotated relevance scores ranging from 0 to 7, with 7 being the most relevant and 0 being the least relevant. The task focuses on two such relations: profession and nationality. We built a system which could automatically predict the relevance scores for unseen triples. Our model is primarily a supervised machine learning based one in which we use well-designed features which are used to a make a Logistic Ordinal Regression based classification model. The proposed system achieves an overall accuracy score of 0.73 and Kendalls tau score of 0.36.
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