Hyun-Kyu Park

Large-scale incremental OWL/RDFS reasoning over fuzzy RDF data

Ontological RDF data are extracted from multiple sources on the web through mapping and alignment for various purposes, but extracting and reasoning about ontologies from different sources causes information ambiguity and uncertainty. A reasonable solution to this problem is to annotate extracted ontology data with truth values to determine the reliability of information. However, the recent growth in data has brought forth difficulties in ascertaining the credibility of numerous ontologies during OWL/RDFS reasoning. In this paper, we present a distributed and incremental reasoning approach for RDF data with uncertainty. We focused on RDFS and OWL pD* semantics and developed methods for incremental OWL reasoning with uncertainty. We also introduced parallel algorithms that resolve the scalable reasoning problem. To evaluate the efficiency of the proposed system, we conducted OWL/RDFS reasoning over fuzzy LUBM3000 and achieved a performance three times higher than that achieved with the fastest reasoning system.

An Approach to a Learning Prediction Model for Recognition of Daily Life Pattern based on Event Calculus

기계 학습 알고리즘의 발전에 따라 다양한 영역의 데이터에 대한 분석 및 결과를 예측하는 연구들이 진행되고 있다. 그러나 기존의 데이터 의존적인 기계 학습 기반의 의도 인지 방법론은 노이즈 처리에 대한 어려움이 존재하고, 복합적으로 발생할 수 있는 행위 의도에 대한 인지에서 한계점을 가진다. 본 한계점을 극복하기 위해 본 논문에서는 이벤트 연산(Event Calculus)을 기반으로 3단계의 행위 의도인지 방법론을 제안한다. 첫 번째 단계는 시퀀스 데이터가 어떤 의도인지를 판별하는 의도 추론 단계이다. 두 번째 단계는 새롭게 추론된 행위 의도를 기반으로 이전부터 유지됐던 행위 의도와의 병행 가능 여부를 판단하는 충돌 해결(Conflict Resolution) 단계이다. 마지막으로 많은 노이즈로 인해 발생되는 오류를 추론된 행위 의도들에 반영하는 노이즈 감소(Noise Reduction) 단계로 진행된다. 이벤트 연산 기법에 대한 성능 평가를 위해 실제 수집한 데이터를 재구축한 혼합 가우시안 모델과 휴리스틱 규칙 기반의 범용 데이터 생성 기법을 제안한다. 5개의 의도로 이루어진 약 13시간의 시퀀스 데이터 300개를 사용하여 이벤트 연산의 성능을 측정하였고, 각 의도에 대해 이벤트 연산의 예측 결과와 실제 확률 모델이 평균 89.3%의 일치 도를 보였다.

Spark based Scalable RDFS Ontology Reasoning over Big Triples with Confidence Values

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.

Ontology and Sequential Rule Based Streaming Media Event Recognition

As the number of various types of media data such as UCC (User Created Contents) increases, research is actively being carried out in many different fields so as to provide meaningful media services. Amidst these studies, a semantic web-based media classification approach has been proposed; however, it encounters some limitations in video classification because of its underlying ontology derived from meta-information such as video tag and title. In this paper, we define recognized objects in a video and activity that is composed of video objects in a shot, and introduce a reasoning approach based on description logic. We define sequential rules for a sequence of shots in a video and describe how to classify it. For processing the large amount of increasing media data, we utilize Spark streaming, and a distributed in-memory big data processing framework, and describe how to classify media data in parallel. To evaluate the efficiency of the proposed approach, we conducted an experiment using a large amount of media ontology extracted from Youtube videos.