  How people think, feel, and behave, primarily is a representation of their
personality characteristics. By being conscious of personality characteristics
of individuals whom we are dealing with or decided to deal with, one can
competently ameliorate the relationship, regardless of its type. With the rise
of Internet-based communication infrastructures (social networks, forums,
etc.), a considerable amount of human communications take place there. The most
prominent tool in such communications, is the language in written and spoken
form that adroitly encodes all those essential personality characteristics of
individuals. Text-based Automatic Personality Prediction (APP) is the automated
forecasting of the personality of individuals based on the generated/exchanged
text contents. This paper presents a novel knowledge graph-enabled approach to
text-based APP that relies on the Big Five personality traits. To this end,
given a text a knowledge graph which is a set of interlinked descriptions of
concepts, was built through matching the input text's concepts with DBpedia
knowledge base entries. Then, due to achieving more powerful representation the
graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon,
and MRC psycholinguistic database information. Afterwards, the knowledge graph
which is now a knowledgeable alternative for the input text was embedded to
yield an embedding matrix. Finally, to perform personality predictions the
resulting embedding matrix was fed to four suggested deep learning models
independently, which are based on convolutional neural network (CNN), simple
recurrent neural network (RNN), long short term memory (LSTM) and bidirectional
long short term memory (BiLSTM). The results indicated a considerable
improvements in prediction accuracies in all of the suggested classifiers.
