Recent studies suggest that a complex, distributed neural network underpins semantic cognition. This article reviews our contribution to this emerging picture and traces the putative roles of each region within this network. Neuropsychological studies indicate that semantic cognition draws on at least two interacting components: semantic representations [degraded in semantic dementia (SD)] and ...

In this paper, we propose a method for multi-term query expansions based on WordNet. In our approach, Hypernym/Hyponymy and Synonym relations in WordNet is used as the basic expansion rules. Then we use WordNet Lexical Chains and WordNet semantic similarity to assign terms in the same query into different groups with respect to their semantic similarities. For each group, we expand the highest ...

Linked Open Datasets about scholarly publications enable the development and integration of sophisticated end-user services; however, richer datasets are still needed. The first goal of this Challenge was to investigate novel approaches to obtain such semantic data. In particular, we were seeking methods and tools to extract information from scholarly publications, to publish it as LOD, and to ...

Most explorations of the epistemic implications of Semantic AntiIndividualism (SAI) focus on issues of self-knowledge (first-person authority) and/or external-world skepticism. Less explored has been SAI’s implications for the epistemology of reasoning. In this paper I argue that SAI has some nontrivial implications on this score. I bring these out by reflecting on a problem first raised by Bog...

Twitter is the second largest social network after Facebook and currently 140 millions Tweets are posted on average each day. Tweets are messages with a maximum number of 140 characters and cover all imaginable stories ranging from simple activity updates over news coverage to opinions on arbitrary topics. In this work we argue that Twitter is a valuable data source for e-Participation related ...

Given two sentences, participating systems assign a semantic similarity score in the range of 0-5. We applied two different techniques for the task: one is based on lexical semantic net (corresponding to run 1) and the other is based on deep learning semantic model (corresponding to run 2). We also combined these two runs linearly (corresponding to run 3). Our results indicate that the two tech...

This paper proposes a novel approach to generate abstractive summary for multiple documents by extracting semantic information from texts. The concept of Basic Semantic Unit (BSU) is defined to describe the semantics of an event or action. A semantic link network on BSUs is constructed to capture the semantic information of texts. Summary structure is planned with sentences generated based on t...

In a novel experimental task, individuals are asked to navigate from a start word to a goal word through a semantic network. In this forced-choice task, individuals perform with a high success rate (73%) and frequently navigate to the target in the minimal number of required steps (22%). We utilize these experimental results to explore different search and decision strategies. Our descriptive m...