Saeid Hosseini

Mohammad Hossein Khojasteh, Najmeh Torabian, Ali Farjami et al.

Link prediction is an important task in addressing the incompleteness problem of knowledge graphs (KG). Previous link prediction models suffer from issues related to either performance or explanatory capability. Furthermore, models that are capable of generating explanations, often struggle with erroneous paths or reasoning leading to the correct answer. To address these challenges, we introduce a novel Neural-Symbolic model named FaSt-FLiP (stands for Fast and Slow Thinking with Filtered rules for Link Prediction task), inspired by two distinct aspects of human cognition: "commonsense reasoning" and "thinking, fast and slow." Our objective is to combine a logical and neural model for enhanced link prediction. To tackle the challenge of dealing with incorrect paths or rules generated by the logical model, we propose a semi-supervised method to convert rules into sentences. These sentences are then subjected to assessment and removal of incorrect rules using an NLI (Natural Language Inference) model. Our approach to combining logical and neural models involves first obtaining answers from both the logical and neural models. These answers are subsequently unified using an Inference Engine module, which has been realized through both algorithmic implementation and a novel neural model architecture. To validate the efficacy of our model, we conducted a series of experiments. The results demonstrate the superior performance of our model in both link prediction metrics and the generation of more reliable explanations.

Sayna Esmailzadeh, Saeid Hosseini, Mohammad Reza Kangavari et al.

Leveraging short-text contents to estimate the occupation of microblog authors has significant gains in many applications. Yet challenges abound. Firstly brief textual contents come with excessive lexical noise that makes the inference problem challenging. Secondly, cognitive-semantics are not evident, and important linguistic features are latent in short-text contents. Thirdly, it is hard to measure the correlation between the cognitive short-text semantics and the features pertaining various occupations. We argue that the multi-aspect cognitive features are needed to correctly associate short-text contents to a particular job and discover suitable people for the careers. To this end, we devise a novel framework that on the one hand, can infer short-text contents and exploit cognitive features, and on the other hand, fuses various adopted novel algorithms, such as curve fitting, support vector, and boosting modules to better predict the occupation of the authors. The final estimation module manufactures the $R^w$-tree via coherence weight to tune the best outcome in the inferring process. We conduct comprehensive experiments on real-life Twitter data. The experimental results show that compared to other rivals, our cognitive multi-aspect model can achieve a higher performance in the career estimation procedure, where it is inevitable to neglect the contextual semantics of users.

Sara Kamran, Raziyeh Zall, Mohammad Reza Kangavari et al.

The latent knowledge in the emotions and the opinions of the individuals that are manifested via social networks are crucial to numerous applications including social management, dynamical processes, and public security. Affective computing, as an interdisciplinary research field, linking artificial intelligence to cognitive inference, is capable to exploit emotion-oriented knowledge from brief contents. The textual contents convey hidden information such as personality and cognition about corresponding authors that can determine both correlations and variations between users. Emotion recognition from brief contents should embrace the contrast between authors where the differences in personality and cognition can be traced within emotional expressions. To tackle this challenge, we devise a framework that, on the one hand, infers latent individual aspects, from brief contents and, on the other hand, presents a novel ensemble classifier equipped with dynamic dropout convnets to extract emotions from textual context. To categorize short text contents, our proposed method conjointly leverages cognitive factors and exploits hidden information. We utilize the outcome vectors in a novel embedding model to foster emotion-pertinent features that are collectively assembled by lexicon inductions. Experimental results show that compared to other competitors, our proposed model can achieve a higher performance in recognizing emotion from noisy contents.