Human-defined concepts are fundamental building-blocks in constructing knowledge bases such as ontologies. Statistical learning techniques provide an alternative automated approach to concept definition, driven by data rather than prior knowledge. In this paper we propose a probabilistic modeling framework that combines both human-defined concepts and data-driven topics in a principled manner. ...

Today, collaboration and the exchange of information are increasing steadily and players need to agree on the meaning of words. The first task is therefore to define the domain’s terminology. However, terminology building remains a demanding and time-consuming task, even in specialised domains where standards already exist. While reaching a consensus on the definition of terms written in natura...

Recent literature has pointed out pedagogical obstacles associated with the use of computational environments in the learning of mathematics. In this paper, we focus on the pedagogical role of computer’s inherent limitations on the development of learners’ concept images of derivative and limit. In particular, we intend to discuss how the approach to these concepts can be properly designed to p...

We give details of our methods in the ImageCLEF 2013 Scalable Concept Image Annotation task. For the textual feature, we propose a method for selecting text closely related to an image from its webpage. In addition, to consider the meaning of the concept, we propose to use WordNet for getting words related to the concept. For visual features, we use Fisher Vector (FV), which is regarded as an e...

In this paper, we present our work in emotion cause extraction. Since there is no open dataset available, the lack of annotated resources has limited the research in this area. Thus, we first present a dataset we built using SINA city news. The annotation is based on the scheme of the W3C Emotion Markup Language. Second, we propose a 7-tuple definition to describe emotion cause events. Based on...

In this paper we describe our image annotation system participated in the ImageCLEF 2013 scalable concept image annotation task. The system leverages multiple base classifiers, including singlefeature and multi-feature kNN classifiers and histogram intersection kernel SVMs, all of which are learned from the provided 250K web images and provided features with no extra manual verification. These ...

FCA is a mathematical formalism having many applications in data mining and knowledge discovery. Originally it deals with binary data tables. However, there is a number of extensions that enrich standard FCA. In this paper we consider two important extensions: fuzzy FCA and pattern structures, and discuss the relation between them. In particular we introduce a scaling procedure that enables rep...

In this paper, the extension principle is used in order to fuzzify location and scale estimators when used on fuzzy numbers. First, fuzzy nonlinear means, such as arithmetic, harmonic, geometric and fuzzy L p mean are deened as extensions of the corresponding crisp means. Fuzzy maximum and minimum operators are used to deene order statistics of fuzzy numbers. Fuzzy L location and scale estimato...

The Spiral Model [Boehm,19S6; Belz,19S6] provides a candidate approach to determining the requirements, architecture, and design of a software process. The Spiral Model activity of determining mission objectives and constraints addresses the requirements for the process: the nature of the product required; budget and schedule constraints; organizational and procedural (e.g. contracting) constra...

Purpose – The purpose of this paper is to present descriptive characteristics of the historical development of concept networks. The linguistic principles, mechanisms and motivations behind the evolution of concept networks are discussed. Implications emanating from the idea of the historical development of concept networks are discussed in relation to knowledge representation and organization ...