Je-Min Kim

Scalable OWL-Horst ontology reasoning using SPARK

In this paper, we present an approach to perform reasoning for scalable OWL ontologies in a Hadoop-based distributed computing cluster. Rule-based reasoning is typically used for a scalable OWL-Horst reasoning; typically, the system repeatedly performs many operations involving semantic axioms for big ontology triples until no further inferred data exists. Thus, the reasoning systems suffer from performance limitations when ontology reasoning is performed via disk-based MapReduce approaches. To overcome this drawback, we propose an approach that loads triples to memory in computer nodes that are connected by SPARK - a memory-based cluster computing platform - and executes ontology reasoning. To implement an OWL Horst ontology reasoning system, we first define a set of algorithms such that they divide large triples into Resilient Distributed Datasets (RDDs), taking into account the patterns and interdependencies of the reasoning rules. We then load each RDD into the memory of computers composing a distributed computing cluster and subsequently perform distributed reasoning by rule execution orders. To evaluate the proposed methods, we compare it to WebPIE using the LUBM set, which is formal dataset for evaluating ontology inferences and search speeds. The proposed approach shows throughput is improved by 200% (98k/sec) as compared to WebPIE (33k/sec) using the LUBM6000 (860 million triples, 109 gigabyte).

Distributed Table Join for Scalable RDFS Reasoning on Cloud Computing Environment

The Knowledge service system needs to infer a new knowledge from indicated knowledge to provide its effective service. Most of the Knowledge service system is expressed in terms of ontology. The volume of knowledge information in a real world is getting massive, so effective technique for massive data of ontology is drawing attention. This paper is to provide the method to infer massive data-ontology to the extent of RDFS, based on cloud computing environment, and evaluate its capability. RDFS inference suggested in this paper is focused on both the method applying MapReduce based on RDFS meta table, and the method of single use of cloud computing memory without using MapReduce under distributed file computing environment. Therefore, this paper explains basically the inference system structure of each technique, the meta table set-up according to RDFS inference rule, and the algorithm of inference strategy. In order to evaluate suggested method in this paper, we perform experiment with LUBM set which is formal data to evaluate ontology inference and search speed. In case LUBM6000, the RDFS inference technique based on meta table had required 13.75 minutes(inferring 1,042 triples per second) to conduct total inference, whereas the method applying the cloud computing memory had needed 7.24 minutes(inferring 1,979 triples per second) showing its speed twice faster.

OnCU system: ontology-based category utility approach for author name disambiguation

Author name disambiguation is essential for improving performance of document indexing, retrieval, and web search. Author name disambiguation resolves the conflict when multiple authors share the same name label. This paper introduces a novel approach which exploits ontologies and ontology-based category utility for author name disambiguation. Author name disambiguation determines the correct author from various candidate authors in the populated author ontology. Candidate authors are evaluated using proposed ontology-based category utility to resolve disambiguation. Ontology-based category utility has been proposed to exploit semantic information in ontology for semantic analysis for disambiguation. The ontology-based category utility increases the number of disambiguation by about 10% compared with that of category utility, and increases the overall amount of accuracy by around 98%.

Enhanced Search Method for Ontology Classification

The Web ontology language (OWL) has become a W3C recommendation to publish and share ontologies on the semantic web. In order to derive hidden information (classification, satisfiability and realization) of OWL ontology, a number of OWL reasoners have been introduced. Most of reasoners use both top-down and bottom-up search for ontology classification. In this paper, we propose an enhanced method of optimizing the ontology classification process of ontology reasoning. One goal of this paper is to provide such a available algorithm for future implementers of ontology reasoning system. Building the optimization method that came off best into ontology reasoning system greatly enhanced its efficiency. Our work focuses on two key aspects: The first and foremost, we describe classical methods for ontology classification. As subsumption testing to classify ontology is costly, it is important to ensure that the classification process uses the smallest number of tests. Therefore, we consider enhanced method and evaluate their effect on four different types of test ontology. The result of the experiment was that the enhanced search method increases performance improvement 30% something like that compare with the classical method.

Scalable RDFS Reasoning using Logic Programming Approach in a Single Machine

시맨틱 웹상에서 RDFS로 표현된 데이터의 사용 증가로 인하여, 대용량 데이터의 추론에 대한 많은 요구가 생겨나고 있다. 많은 연구자들은 대용량 온톨로지 추론을 수행하기 위해서 하둡과 같은 고가의 분산 프레임워크를 활용한다. 그러나, 적절한 사이즈의 RDFS 트리플 추론을 위해서는 굳이 고가의 분산 환경 시스템을 사용하지 않고 단일 머신에서도 논리적 프로그래밍을 이용하면 분산 환경과 유사한 추론 성능을 얻을 수 있다. 본 논문에서는 단일 머신에 논리적 프로그래밍 방식을 적용한 대용량 RDFS 추론 기법을 제안하였고 다중 머신을 기반으로 한 분산 환경 시스템과 비교하여 2억개 정도의 트리플에 대한 RDFS 추론 시스템을 적용한 경우 분산환경과 비슷한 성능을 보이는 것을 실험적으로 증명하였다. 효율적인 추론을 위해 온톨로지 모델을 세부적으로 분리한 메타데이터 구조와 대용량 트리플의 색인 방안을 제안하고 이를 위해서 전체 트리플을 하나의 모델로 로딩하는 것이 아니라 각각 온톨로지 추론 규칙에 따라 적절한 트리플 집합을 선택하였다. 또한 논리 프로그래밍이 제공하는 Unification 알고리즘 기반의 트리플 매칭, 검색, Conjunctive 질의어 처리 기반을 활용하는 온톨로지 추론 방식을 제안한다. 제안된 기법이 적용된 추론 엔진을 LUBM1500(트리플 수 2억개) 에 대해서 실험한 결과 166K/sec의 추론 성능을 얻었는데 이는 8개의 노드(8 코아/노드)환경에서 맵 리듀스로 수행한 WebPIE의 185K/sec의 추론 속도와 유사함을 실험적으로 증명하였다. 따라서 단일 머신에서 수행되는 본 연구 결과는 트리플의 수가 2억개 정도까지는 분산환경시스템을 활용하지 않고도 분산환경 시스템과 비교해서 비슷한 성능을 보이는 것을 확인할 수 있었다.

Personalized Search Service in Semantic Web

The semantic web environment promise semantic search of heterogeneous data from distributed web page. Semantic search would resuit in an overwhelming number of results for users is increased, therefore elevating the need for appropriate personalized ranking schemes. Culture Finder helps semantic web agents obtain personalized culture information. It extracts meta data for each web page(culture news, culture performance, culture exhibition), perform semantic search and compute result ranking point to base user profile. In order to work efficient, Culture Finder uses five major technique: Machine learning technique for generating user profile from user search behavior and meta data repository, an efficient semantic search system for semantic web agent, query analysis for representing query and query result, personalized ranking method to provide suitable search result to user, upper ontology for generating meta data. In this paper, we also present the structure used in the Culture Finder to support personalized search service.

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.

MOnCa2: High-Level Context Reasoning Framework based on User Travel Behavior Recognition and Route Prediction for Intelligent Smartphone Applications

MOnCa2 is a framework for building intelligent smartphone applications based on smartphone sensors and ontology reasoning. In previous studies, MOnCa determined and inferred user situations based on sensor values represented by ontology instances. When this approach is applied, recognizing user space information or objects in user surroundings is possible, whereas determining the users physical context (travel behavior, travel destination) is impossible. In this paper, MOnCa2 is used to build recognition models for travel behavior and routes using smartphone sensors to analyze the users physical context, infer basic context regarding the users travel behavior and routes by adapting these models, and generate high-level context by applying ontology reasoning to the basic context for creating intelligent applications. This paper is focused on approaches that are able to recognize the users travel behavior using smartphone accelerometers, predict personal routes and destinations using GPS signals, and infer high-level context by applying realization.

A Scalable OWL Horst Lite Ontology Reasoning Approach based on Distributed Cluster Memories

Current ontology studies use the Hadoop distributed storage framework to perform map-reduce algorithm-based reasoning for scalable ontologies. In this paper, however, we propose a novel approach for scalable Web Ontology Language (OWL) Horst Lite ontology reasoning, based on distributed cluster memories. Rule-based reasoning, which is frequently used for scalable ontologies, iteratively executes triple-format ontology rules, until the inferred data no longer exists. Therefore, when the scalable ontology reasoning is performed on computer hard drives, the ontology reasoner suffers from performance limitations. In order to overcome this drawback, we propose an approach that loads the ontologies into distributed cluster memories, using Spark (a memory-based distributed computing framework), which executes the ontology reasoning. In order to implement an appropriate OWL Horst Lite ontology reasoning system on Spark, our method divides the scalable ontologies into blocks, loads each block into the cluster nodes, and subsequently handles the data in the distributed memories. We used the Lehigh University Benchmark, which is used to evaluate ontology inference and search speed, to experimentally evaluate the methods suggested in this paper, which we applied to LUBM8000 (1.1 billion triples, 155 gigabytes). When compared with WebPIE, a representative mapreduce algorithm-based scalable ontology reasoner, the proposed approach showed a throughput improvement of 320% (62k/s) over WebPIE (19k/s).

WordNet-Based Category Utility Approach for Author Name Disambiguation

ABSTRACT Author name disambiguation is essential for improving performance of document indexing, retrieval, and web search. Author name disambiguation resolves the conflict when multiple authors share the same name label. This paper introduces a novel approach which exploits ontologies and WordNet-based category utility for author name disambiguation. Our method utilizes author knowledge in the form of populated ontology that uses various types of properties: titles, abstracts and co-authors of papers and authors’ affiliation. Author ontology has been constructed in the artificial intelligence and semantic web areas semi-automatically using OWL API and heuristics. Author name disambiguation determines the correct author from various candidate authors in the populated author ontology. Candidate authors are evaluated using proposed WordNet-based category utility to resolve disambiguation. Category utility is a tradeoff between intra-class similarity and inter-class dissimilarity of author instances, where author instances are described in terms of attribute-value pairs. WordNet-based category utility has been proposed to exploit concept information in WordNet for semantic analysis for disambiguation. Experiments using the WordNet-based category utility increase the number of disambiguation by about 10% compared with that of category utility, and increase the overall amount of accuracy by around 98%. Keywords:Ontology, Metadata, Category Utility, Author Name Disambiguation