WordNet-based Similarity Measures Research Articles

Global Ontology Entities Embeddings

Ontologies are among the most widely used types of knowledge representation formalisms. The application of deep learning techniques in the field of ontology engineering has reinforced the need to learn and generate representations of ontological data. This allows ontologies to be exploited by such models, and thus automate various ontology engineering tasks, where most of the existing tools and machine learning approaches require a numerical feature vectors associated with each concept. This paper outlines a novel approach for learning global ontology entities embeddings by exploiting the structure and the various taxonomic and semantic relationships present in ontologies, taking into account all the information present in the ontological graph and carried by the OWL/RDF triples. Thus, producing global ontology entities embeddings capturing the global ontological graph semantics and similarities enclosed in the source ontology. Three different neural network models have been proposed based on two architectures: multi-input and multi-output, trained using the contrastive estimation technique. The evaluation on OWL/RDF ontologies and word semantic similarity tasks using various graph and WordNet based similarity measures, show that our approach yields competitive results outperforming the state-of-the-art ontology and word embedding models.

의미 유사도를 활용한 Distant Supervision 기반의 트리플 생성 성능 향상

기존의 패턴기반 트리플 생성 시스템은 distant supervision의 가정으로 인해 오류 패턴을 생성하여 트리플 생성 시스템의 성능을 저하시키는 문제점이 있다. 이 문제점을 해결하기 위해 본 논문에서는 패턴과 프로퍼티 사이의 의미 유사도 기반의 패턴 신뢰도를 측정하여 오류 패턴을 제거하는 방법을 제안한다. 의미 유사도 측정은 비지도 학습 방법인 워드임베딩과 워드넷 기반의 어휘 의미 유사도 측정 방법을 결합하여 사용한다. 또한 한국어 패턴과 영어 프로퍼티 사이의 언어 및 어휘 불일치 문제를 해결하기 위해 정준 상관 분석과 사전 기반의 번역을 사용한다. 실험 결과에 따르면 제안한 의미 유사도 기반의 패턴 신뢰도 측정 방법이 기존의 방법보다 10% 높은 정확률의 트리플 집합을 생성하여, 트리플 생성 성능 향상을 증명하였다.

Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model

Highly obfuscated plagiarism cases contain unseen and obfuscated texts, which pose difficulties when using existing plagiarism detection methods. A fuzzy semantic-based similarity model for uncovering obfuscated plagiarism is presented and compared with five state-of-the-art baselines. Semantic relatedness between words is studied based on the part-of-speech (POS) tags and WordNet-based similarity measures. Fuzzy-based rules are introduced to assess the semantic distance between source and suspicious texts of short lengths, which implement the semantic relatedness between words as a membership function to a fuzzy set. In order to minimize the number of false positives and false negatives, a learning method that combines a permission threshold and a variation threshold is used to decide true plagiarism cases. The proposed model and the baselines are evaluated on 99,033 ground-truth annotated cases extracted from different datasets, including 11,621 (11.7%) handmade paraphrases, 54,815 (55.4%) artificial plagiarism cases, and 32,578 (32.9%) plagiarism-free cases. We conduct extensive experimental verifications, including the study of the effects of different segmentations schemes and parameter settings. Results are assessed using precision, recall, F-measure and granularity on stratified 10-fold cross-validation data. The statistical analysis using paired t-tests shows that the proposed approach is statistically significant in comparison with the baselines, which demonstrates the competence of fuzzy semantic-based model to detect plagiarism cases beyond the literal plagiarism. Additionally, the analysis of variance (ANOVA) statistical test shows the effectiveness of different segmentation schemes used with the proposed approach.