SCOE 2012 Abstracts


Short Papers
Paper Nr: 1
Title:

Attribute Value Ontology - Using Semantics in Data Mining

Authors:

Tomasz Łukaszewski, Joanna Józefowska and Agnieszka Ławrynowicz

Abstract: We propose a new concept to represent attribute values as an ontology that allows modeling different levels of abstraction. In this way more or less precise values may be used instead of missing or erroneous data. The goal is to use this representation in order to improve analysis of imperfect data. The proposed attribute value ontology (AVO) allows to upgrade the precision of information not only from positive observations but also from negative ones. We show how to classify a new example using a set of training examples described in the same or more precise way. Another advantage of the proposed approach is providing an efficient way to avoid the effect of overfitting.

Paper Nr: 2
Title:

Ontology-guided Social Media Analysis - System Architecture

Authors:

Alexander Semenov and Jari Veijalainen

Abstract: Social media sites have appeared to the cyber space during the last 5-7 years and have attracted hundreds of millions of users. The sites are often viewed as instances of Web 2.0 technologies and support easy uploading and downloading of user generated contents. This content contains valuable real time information about the state of affairs in various parts of the world that is often public or at least semipublic. Many governments, businesses, and individuals are interested in this information for various reasons. In this paper we describe how ontologies can be used in constructing monitoring software that would extract useful information from social media sites and store it over time for further analysis. Ontologies can be used at least in two roles in this context. First, the crawler accessing a site must know the “native ontology” of the site in order to be able to parse the pages returned by the site in question, extract the relevant information (such as friends of a user) and store it into the persistent generic (graph) model instance at the monitoring site. Second, ontologies can be used in data analysis to capture and filter the collected data to find information and phenomena of interest. This includes influence analysis, grouping of users etc. In this paper we mainly discuss the construction of the ontology-guided crawler.

Paper Nr: 3
Title:

Towards an Arabic Ontology - Defining Morpho-lexical Patterns for Semantic Relation Extraction

Authors:

Mohamed Mahdi Boudabous, Fatiha Sadat and Lamia Hadrich Belguith

Abstract: In this paper, we propose a method for defining morpho-lexical patterns used to detect semantic relation between Arabic nouns. This method is based on study corpus built from online encyclopedia. This corpus consists of a set of articles selected on the basis of a database containing pairs of terms linked by semantic relations. Defined patterns are then implemented using NooJ platform. The pattern evaluation result is very encouraging. We obtained 79% as F-Measure rate.

Paper Nr: 4
Title:

A Pragmatic Approach to Conceptual Negotiation Support

Authors:

Cristóvão Sousa, Carla Pereira and António Lucas Soares

Abstract: Collaborative conceptualisation processes are pervasive to most technical and professional activities, but are seldom addressed explicitly due to the lack of theoretical and practical methods and tools to support it. However, it seems not to be a popular research topic in knowledge representation or its sub-areas such as ontology engineering. Our view is that collaboration between stakeholders for specifying an ontology should be addressed at the conceptual, semi-formal level, in order to foster a collective learning of the domain and reaching agreements about its representation. We developed a method to support conceptual integration based in the conceptual blending theory - ColBlend - and implemented it in a collaborative modelling environment. This "conceptual modelling environment - conceptME" supports teams of specialist and facilitators in eliciting conceptual structures with the help of collaborative model editing and terminology services. Conceptual integration and agreements are achieved through the ColBlend method. This paper overviews ColBlend and ConceptME and describes in detail a test case.