Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

Browse

Filters

2008 - 200922010 - 20122

Settings

Author: search.filters.author.Tapucu, DilekScopus Q: search.filters.scopusQuartile.N/A

Search Results

Now showing 1 - 4 of 4
  • Conference Object
    Citation - Scopus: 5
    Metamodeling Approach To Preference Management in the Semantic Web
    (Association for the Advancement of Artificial Intelligence, 2008) Tapucu, Dilek; Can, Özgü; Bursa, Okan; Ünalır, Murat Osman
    Preference is a superiority state to determine the preferable or the superior of one entity, property or constraint to another from a specified selection set. Preference issue is heavily studied in Semantic Web research area. The existing preference management approaches only consider the importance of concepts for capturing users' interests. This paper presents a metamodeling approach to preference management. Preference meta model consists of concepts and semantic relations to represent users' interests. Users may have the same type preferences in different domains. Thus, metamodeling must be used to define similar preferences for interoperability in different domains. In this paper, preference meta model defines a general storage structure to manage different types of preferences for personalized applications. Copyright © 2008, Association for the Advancement of Artificial Intelligence.
  • Conference Object
    Citation - WoS: 18
    Citation - Scopus: 25
    Adaptation and Use of Subjectivity Lexicons for Domain Dependent Sentiment Classification
    (Institute of Electrical and Electronics Engineers Inc., 2012) Dehkharghani, Rahim; Yanıkoğlu, Berrin; Tapucu, Dilek; Saygın, Yücel
    Sentiment analysis refers to the automatic extraction of sentiments from a natural language text. We study the effect of subjectivity-based features on sentiment classification on two lexicons and also propose new subjectivity-based features for sentiment classification. The subjectivity-based features we experiment with are based on the average word polarity and the new features that we propose are based on the occurrence of subjective words in review texts. Experimental results on hotel and movie reviews show an overall accuracy of about 84% and 71% in hotel and movie review domains respectively; improving the baseline using just the average word polarities by about 2% points. © 2012 IEEE.
  • Conference Object
    Citation - WoS: 22
    Citation - Scopus: 29
    Learning Domain-Specific Polarity Lexicons
    (Institute of Electrical and Electronics Engineers Inc., 2012) Demiröz, Gülşen; Yanıkoğlu, Berrin; Tapucu, Dilek; Saygın, Yücel
    Sentiment analysis aims to automatically estimate the sentiment in a given text as positive or negative. Polarity lexicons, often used in sentiment analysis, indicate how positive or negative each term in the lexicon is. However, since creating domain-specific polarity lexicons is expensive and time-consuming, researchers often use a general purpose or domain-independent lexicon. In this work, we address the problem of adapting a general purpose polarity lexicon to a specific domain and propose a simple yet effective adaptation algorithm. We experimented with two sets of reviews from the hotel and movie domains and observed that while our adaptation techniques changed the polarity values for only a small set of words, the overall test accuracy increased significantly: 77% to 83% in the hotel dataset and 61% to 66% in the movie dataset. © 2012 IEEE.
  • Conference Object
    Citation - Scopus: 3
    An Extension of Ontology Based Databases To Handle Preferences
    (INSTICC, 2009) Tapucu, Dilek; Ait-Ameur, Yamine; Jean, Stephane; Ünalır, Murat Osman
    Ontologies have been defined to make explicit the semantics of data. With the emergence of the SemanticWeb, the amount of ontological data (or instances) available has increased. To manage such data, Ontology Based DataBases (OBDBs), that store ontologies and their instance data in the same repository have been proposed. These databases are associated with exploitation languages supporting description, querying, etc. on both ontologies and data. However, usually queries return a big amount of data that may be sorted in order to find the relevant ones. Moreover, in the current, few approaches considering user preferences when querying have been developed. Yet this problem is fundamental for many applications especially in the e-commerce domain. In this paper, we first propose an extension of an existing OBDB, called OntoDB through extension of their ontology model in order to support semantic description of preferences. Secondly, an extension of an ontology based query language, called OntoQL defined on OntoDB for querying ontological data with preferences is presented. Finally, an implementation of the proposed extensions are described.