Integrating knowledge representation approaches, such as ontologies, with the field of automated planning is still an open research challenge. To explore this issue, we present a semantic model to address the knowledge representation of planning domains. More specifically, we show an ontological approach to represent HTN (Hierarchical Task Network) in OWL (Web Ontology Language) ontologies. We ...

We are developing a spoken dialogue system that accepts speaker-independent continuous utterances and responds to them. Two approaches are adopted and compared. Syntax-driven approach first applies syntactic analysis to constrain the input and passes syntactically accepted sentence candidates to semantic analysis. Keyword-driven approach performs keyword spotting and generates a lattice of keyw...

The difficulty of exchanging information between heterogeneous medical databases remains one of the chief obstacles in achieving a unified patient medical record. Although methods have been developed to address differences in data formats, system software, and communication protocols, automated data exchange between disparate systems still remains an elusive goal. The Medical Information Acquis...

0020-0255/$ see front matter 2012 Elsevier Inc http://dx.doi.org/10.1016/j.ins.2012.02.053 ⇑ Corresponding author. Tel.: +1 340 693 1376; fa E-mail address: [email protected] (K. Alexandridi This study introduces semantic network analysis of natural language processing in collective social settings. It utilizes the spreading-activation theory of human long-term memories from social psychology to ...

In this short paper, we present the knowledge representation model called Extended Semantic Network. The basic idea of this proposal is to imagine data representation techniques which can reason beyond the classical techniques in information retrieval systems. It is argued that by employing hybrid techniques one can address the good recall with powerful deduction problems. Our objective here is...

Sentences are important semantic units of natural language. A generic, distributional representation of sentences that can capture the latent semantics is beneficial to multiple downstream applications. We observe a simple geometry of sentences – the word representations of a given sentence (on average 10.23 words in all SemEval datasets with a standard deviation 4.84) roughly lie in a low-rank...

To understand the meanings of words and objects, we need to have knowledge about these items themselves plus executive mechanisms that compute and manipulate semantic information in a task-appropriate way. The neural basis for semantic control remains controversial. Neuroimaging studies have focused on the role of the left inferior frontal gyrus (LIFG), whereas neuropsychological research sugge...

In this study, we compared four expert graders with latent semantic analysis (LSA) to assess short summaries of an expository text. As is well known, there are technical difficulties for LSA to establish a good semantic representation when analyzing short texts. In order to improve the reliability of LSA relative to human graders, we analyzed three new algorithms by two holistic methods used in...

In this paper we present a factoid question answering system for participation in Task 4 of the QALD-7 shared task. Our system is an end-to-end neural architecture for learning a semantic representation of the input question. It iteratively generates representations and uses a convolutional neural network (CNN) model to score them at each step. We take the semantic representation with the highe...