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Collaborating Authors

National and Kapodistrian University of Athens


Semantic Web and Ignorance: Dempster-Shafer Description Logics

AAAI Conferences

Information incompleteness, or ignorance, is an issue that we have to consider in Semantic Web applications. Dempster-Shafer theory has been traditionally applied in information incompleteness situations. On the other hand, logic plays a major role in the Semantic Web community. In this paper, we propose a framework that applies Dempster-Shafer theory in a Description Logic Knowledge Base environment. We name our model a Dempster-Shafer DL Knowledge Base.


Karanikola

AAAI Conferences

Information incompleteness, or ignorance, is an issue that we have to consider in Semantic Web applications. Dempster-Shafer theory has been traditionally applied in information incompleteness situations. On the other hand, logic plays a major role in the Semantic Web community. In this paper, we propose a framework that applies Dempster-Shafer theory in a Description Logic Knowledge Base environment. We name our model a Dempster-Shafer DL Knowledge Base.


Nikolaou

AAAI Conferences

We present a new reasoner for RCC-8 constraint networks, called gp-rcc8, that is based on the patchwork property of path-consistent tractable RCC-8 networks and graph partitioning. We compare gp-rcc8 with state of the art reasoners that are based on constraint propagation and backtracking search as well as one that is based on graph partitioning and SAT solving. Our evaluation considers very large real-world RCC-8 networks and medium-sized synthetic ones, and shows that gp-rcc8 outperforms the other reasoners for these networks, while it is less efficient for smaller networks.


Fast Consistency Checking of Very Large Real-World RCC-8 Constraint Networks Using Graph Partitioning

AAAI Conferences

We present a new reasoner for RCC-8 constraint networks, called gp-rcc8, that is based on the patchwork property of path-consistent tractable RCC-8 networks and graph partitioning. We compare gp-rcc8 with state of the art reasoners that are based on constraint propagation and backtracking search as well as one that is based on graph partitioning and SAT solving. Our evaluation considers very large real-world RCC-8 networks and medium-sized synthetic ones, and shows that gp-rcc8 outperforms the other reasoners for these networks, while it is less efficient for smaller networks.


A Reasoner for the RCC-5 and RCC-8 Calculi Extended with Constants

AAAI Conferences

The problem of checking the consistency of spatial calculi that contain both unknown and known entities (constants, i.e., real geometries) has recently been studied. Until now, all the approaches are theoretical and no implementation has been proposed. In this paper we present the first reasoner that takes as input RCC-5 or RCC-8 networks with variables and constants and decides their consistency. We investigate the performance of the reasoner experimentally using real-world networks and show that we can achieve significantly better times by geometry simplification and parallelization.


Mikros

AAAI Conferences

The aim of this study is to explore authorship attribution methods in Greek tweets. We have developed the first Modern Greek Twitter corpus (GTC) consisted of 12,973 tweets crawled from 10 Greek popular users. We used this corpus in order to study the effectiveness of a specific document representation called Author's Multilevel N-gram Profile (AMNP) and the impact of different methods on training data construction for the task of authorship attribution. In order to address the above research questions we used GTC to create 4 different datasets which contained merged tweets in texts of different sizes (100, 75, 50 and 25 words). Results were evaluated using authorship attribution accuracy both in 10-fold cross-validation and in an external test set compiled from actual tweets. AMNP representation achieved significant better accuracies than single feature groups across all text sizes.


Authorship Attribution in Greek Tweets Using Author's Multilevel N-Gram Profiles

AAAI Conferences

The aim of this study is to explore authorship attribution methods in Greek tweets. We have developed the first Modern Greek Twitter corpus (GTC) consisted of 12,973 tweets crawled from 10 Greek popular users. We used this corpus in order to study the effectiveness of a specific document representation called Author's Multilevel N-gram Profile (AMNP) and the impact of different methods on training data construction for the task of authorship attribution. In order to address the above research questions we used GTC to create 4 different datasets which contained merged tweets in texts of different sizes (100, 75, 50 and 25 words). Results were evaluated using authorship attribution accuracy both in 10-fold cross-validation and in an external test set compiled from actual tweets. AMNP representation achieved significant better accuracies than single feature groups across all text sizes.



Reports of the AAAI 2012 Conference Workshops

AI Magazine

The AAAI-12 Workshop program was held Sunday and Monday, July 22–23, 2012 at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada. The AAAI-12 workshop program included 9 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages, AI for Data Center Management and Cloud Computing, Cognitive Robotics, Grounding Language for Physical Systems, Human Computation, Intelligent Techniques for Web Personalization and Recommendation, Multiagent Pathfinding, Neural-Symbolic Learning and Reasoning, Problem Solving Using Classical Planners, Semantic Cities. This article presents short summaries of those events.


Action-Based Imperative Programming with YAGI

AAAI Conferences

Many tasks for autonomous agents or robots are best de- scribed by a specification of the environment and a specifi- cation of the available actions the agent or robot can perform. Combining such a specification with the possibility to imper- atively program a robot or agent is what we call the action- based imperative programming. One of the most successful such approaches is Golog. In this paper, we draft a proposal for a new robot program- ming language YAGI, which is based on the action-based imperative programming paradigm. Our goal is to design a small, portable stand-alone YAGI interpreter. We combine the benefits of a principled domain specification with a clean, small and simple programming language, which does not ex- ploit any side-effects from the implementation language. We discuss general requirements of action-based programming languages and outline YAGI, our action-based language ap- proach which particularly aims at embeddability.