Minsu Jang

Bossam: An Extended Rule Engine for OWL Inferencing

In this paper, we describe our effort to build an inference engine for OWL reasoning based on the rule engine paradigm. Rule engines are very practical and effective for their representational simplicity and optimized performance, but their limited expressiveness and web unfriendliness restrict their usability for OWL reasoning. We enumerate and succinctly describe extended features implemented in our rule engine, Bossam, and show that these features are necessary to promote the effectiveness of any ordinary rule engine’s OWL reasoning capability. URI referencing and URI-based procedural attachment enhance web-friendliness. OWL importing, support for classical negation and relieved range restrictedness help correctly capture the semantics of OWL. Remote binding enables collaborated reasoning among multiple Bossam engines, which enhances the engine’s usability on the distributed semantic web environment. By applying our engine to the W3C’s OWL test cases, we got a plausible 70% average success rate for the three OWL species. Our contribution with this paper is to suggest a set of extended features that can enhance the reasoning capabilities of ordinary rule engines on the semantic web.

MoA: OWL ontology merging and alignment tool for the semantic web

Ontology merging and alignment is one of the effective methods for ontology sharing and reuse on the Semantic Web. A number of ontology merging and alignment tools have been developed, many of those tools depend mainly on concept (dis)similarity measure derived from linguistic cues. We present in this paper a linguistic information based approach to ontology merging and alignment. Our approach is based on two observations: majority of concept names used in ontology are composed of multiple-word combinations, and ontologies designed independently are, in most cases, organized in very different hierarchical structure even though they describe overlapping domains. These observations led us to a merging and alignment algorithm that utilizes both the local and global meaning of a concept. We devised our proposed algorithm in MoA, an OWL DL ontology merging and alignment tool. We tested MoA on 3 ontology pairs, and human experts followed 93% of the MoAs suggestions.

Design and Implementation of a Ubiquitous Robotic Space

This paper describes a concerted effort to design and implement a robotic service framework. The proposed framework is comprised of three conceptual spaces: physical, semantic, and virtual spaces, collectively referred to as a ubiquitous robotic space. We implemented a prototype robotic security application in an office environment, which confirmed that the proposed framework is an efficient tool for developing a robotic service employing IT infrastructure, particularly for integrating heterogeneous technologies and robotic platforms.

Ubiquitous robot simulation framework and its applications

We describe in this paper a framework, called URSF, for simulating ubiquitous computing environment and ubiquitous robots. URSF provides in/out channel with which ubiquitous robot platforms can be plugged. Once connected to the framework, ubiquitous robot platform can percept and affect the world simulated in the framework. The simulated world is built by composing a simulation space and placing in it operational components such as appliances, sensors, persons, robots etc. Each operational component continuously generates context data, which are fed to the plugged platform. The platform builds world model by interpreting the context data stream. Operational components in the simulation space expose services through semantic Web service scheme, through which plugged platform can affect the simulated world. Semantic Web service scheme enables automated discovery of services dynamically coming and going in the simulation space. The framework can be used to observe how robots interact with environment via ubiquitous network. We discuss two advanced usages of URSF: intelligent robot manipulation and mixed robot-reality manifestation.

Building Semantic Robot Space based on the Semantic Web

The robot space is a ubiquitous environment in which the networked robot plays the role of main mediator that accepts the sensor data from the environment, interprets the data to understand the situation, and decides the actions to perform. Semantic robot space provides the high-level data model of the robot space that enables context-aware service execution. We designed the semantic robot space based on the semantic Web technology, which provided crucial advantages in architecting dynamic reconfiguration mechanism. The standard nature of the technology leverages the interaction among stake-holders in building robot spaces, which makes it possible to establish public registry-based construction of the semantic robot space. We conclude that semantic robot space based on the semantic Web not only embellishes the intelligence of robots but also reduces the cost of installing and maintaining robot spaces.

An Ontological Approach for Natural Language Command Interpretation and Its Application in Robotics

We present an ontological approach for interpreting natural language commands as well as an application to intelligent robots. In our approach, we first extract meaningful keywords from the user commands by adopting lexico-semantic pattern matching. A structured representation is then built by extracting and arranging meta-data for each keyword. The data is then transformed into an ontological knowledge representation. We implemented a prototype interpretation system, and applied it to the voice user interface of an intelligent mobile robot. The system was tested with several commands for querying weather or traffic information and controlling the robot and various digital home devices. The present experiments with an ontological based interpretation scheme demonstrate that the coverage of user commands can be easily extended, as the representation is logical and declarative

Building an automated engagement recognizer based on video analysis

This paper presents a process to build a classifier in a data-driven way for recognizing engagement of children in a robot-based math quiz game. The process consists of collecting video recordings from HRI experiments; annotating the social signals and engagement states via video analysis; extracting feature vectors from the annotations and training classifiers. We conducted an experiment with 7 participants of

Design of an adaptive accelerometer-based handwriting recognition system based on metacognitive framework

10\sim ~11

Dynamic Time Warping-Based K-Means Clustering for Accelerometer-Based Handwriting Recognition

years of age using an android robot EveR-4. With three coders annotating the video recordings and extracting features by snapshot model with 1-second time window, we achieved 84.83% recall performance.Categories and Subject DescriptorsI.2 [Computing Methodologies]: Artificial Intelligence; D.2.2 [Software Engineering]: Design Tools and Techniques-User interfacesGeneral TermsEXPERIMENTATION

Automated Question Answering Using Semantic Web Services

We present in this paper a software system that can provide highly adaptive accelerometer-based gesture recognition on commercial products. The algorithm features efficient motion detection, template matching-based motion pattern recognition, and template set optimization techniques. We apply our algorithm for recognizing 26 uppercase English alphabets with promising results.