This article presents a model of general-purpose computing on a semantic network substrate. The concepts presented are applicable to any semantic network representation. However, due to the standards and technological infrastructure devoted to the Semantic Web effort, this article is presented from this point of view. In the proposed model of computing, the application programming interface, th...

A proper semantic representation of textual information underlies many natural language processing tasks. In this paper, a novel semantic annotator is presented to generate conceptual features for text documents. A comprehensive conceptual network is automatically constructed with the aid of Wikipedia which has been represented as a Markov chain. Furthermore, semantic annotator gets a fragment ...

  One of the most famous methods for recommendation is user-based Collaborative Filtering (CF). This system compares active user’s items rating with historical rating records of other users to find similar users and recommending items which seems interesting to these similar users and have not been rated by the active user. As a way of computing recommendations, the ultimate goal of the user-ba...

Document level sentiment classification remains a challenge: encoding the intrinsic relations between sentences in the semantic meaning of a document. To address this, we introduce a neural network model to learn vector-based document representation in a unified, bottom-up fashion. The model first learns sentence representation with convolutional neural network or long short-term memory. Afterw...

The purpose of this study was twofold: (a) to assess the retention of two word types (synonyms and homonyms) in the short term memory, and (b) to investigate the effect of these word types on word learning by asking learners to learn their Persian meanings. A total of 73 Iranian language learners studying English translation participated in the study. For the first purpose, 36 freshmen from an ...

We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over-predi...

In this paper, we present the Bag-of-Attributes (BoA) model for video representation aiming at video event retrieval. The BoA model is based on a semantic feature space for representing videos, resulting in high-level video feature vectors. For creating a semantic space, i.e., the attribute space, we can train a classifier using a labeled image dataset, obtaining a classification model that can...

This research tests the hypothesis that comprehension of human events will engage an extended semantic representation system, independent of the input modality (sentence vs. picture). To investigate this, we examined brain activation and connectivity in 19 subjects who read sentences and viewed pictures depicting everyday events, in a combined fMRI and DTI study. Conjunction of activity in unde...

In this paper, we present our lightweight semantic publish/ subscribe system μC-SemPS that is targeted towards battery-powered micro-controllers and wireless sensor nodes in ubiquitous computing environments. The key challenge that we address is to minimize the overall energy consumption for subscription matching and delivery. Our system relies on an efficient representation of semantic subscri...