  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 Kendall's tau score of 0.36.
