Textual content around us is growing on a daily basis. Numerous articles are being written as we speak on online newspapers, blogs, or social media. Similarly, recent advances in the AI field, like language models or traditional classic AI approaches, are utilizing all the above to improve their learned representation to tackle NLP challenges with human-like accuracy. It is commonly accepted that it is crucial to have access to well-written text from valid sources to tackle challenges like text summarization, question-answering, machine translation, or even pronoun resolution. For instance, to summarize well, one needs to select the most important sentences in order to concatenate them to form the summary. However, what happens if we do not have access to well-formed English sentences or even non-valid sentences? Despite the importance of having access to well-written sentences, figuring out ways to validate them is still an open area of research. To address this problem, we present a simplified way to validate English sentences through a novel neural-symbolic approach. Lately, neural-symbolic approaches have triggered an increasing interest towards tackling various NLP challenges, as they are demonstrating their effectiveness as a central component in various AI systems. Through combining Classic with Modern AI, which involves the blending of grammatical and syntactical rules with language models, we effectively tackle the Corpus of Linguistic Acceptability (COLA), a task that shows whether or not a sequence of words is an English grammatical sentence. Among others, undertaken experiments effectively show that blending symbolic and non-symbolic systems helps the former provide insights about the latter's accuracy results.

Recently, low-dimensional vector space representations of knowledge graphs (KGs) have been applied to find answers to conjunctive queries (CQs) over incomplete KGs. However, the current methods only focus on inductive reasoning, i.e. answering CQs by predicting facts based on patterns learned from the data, and lack the ability of deductive reasoning by applying external domain knowledge. Such (expert or commonsense) domain knowledge is an invaluable resource which can be used to advance machine intelligence. To address this shortcoming, we introduce a neural-symbolic method for ontology-mediated CQ answering over incomplete KGs that operates in the embedding space. More specifically, we propose various data augmentation strategies to generate training queries using query-rewriting based methods and then exploit a novel loss function for training the model. The experimental results demonstrate the effectiveness of our training strategies and the new loss function, i.e., our method significantly outperforms the baseline in the settings that require both inductive and deductive reasoning.