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Semantic Similarity Analysis and Application in Knowledge Graphs Universidad Politécnica de Madrid Escuela Técnica Superior de Ingenieros de Telecomunicación SEMANTIC SIMILARITY ANALYSIS AND APPLICATION IN KNOWLEDGE GRAPHS Tesis Doctoral Ganggao Zhu Máster en Software y Sistemas 2017 Universidad Politécnica de Madrid Escuela Técnica Superior de Ingenieros de Telecomunicación SEMANTIC SIMILARITY ANALYSIS AND APPLICATION IN KNOWLEDGE GRAPHS Tesis Doctoral Ganggao Zhu Máster en Software y Sistemas 2017 Departamento de Ingeniería de Sistemas Telemáticos Escuela Técnica Superior de Ingenieros de Telecomunicación Universidad Politécnica de Madrid SEMANTIC SIMILARITY ANALYSIS AND APPLICATION IN KNOWLEDGE GRAPHS Autor: Ganggao Zhu Máster en Software y Sistemas Tutor: Carlos Ángel Iglesias Fernández Doctor Ingeniero de Telecomunicación 2017 Tribunal nombrado por el Magfco. y Excmo. Sr. Rector de la Universidad Politécnica de Madrid, el día de de . Presidente: Vocal: Vocal: Vocal: Secretario: Suplente: Suplente: Realizado el acto de defensa y lectura de la Tesis el día de de . en la E.T.S.I. Telecomunicación habiendo obtenido la calificación de . EL PRESIDENTE LOS VOCALES EL SECRETARIO A mi madre, a mi padre, gracias por todo. Acknowledgements Foremost, I would like to express my sincere gratitude to my adviser, Carlos A. Iglesias, for the continuous support of my Ph.D study and research, for his patience, motivation, enthusiasm, and immense knowledge. He provided me an excellent freedom atmosphere for doing research and implementing new ideas. His excellent guidance helped me in all the time of research and writing of this thesis. Besides, I would like to thank my fellow labmates for the discussions, lunch talk, pizza time, morning coffee game, working together before deadlines, and for all the fun we have had in the last five years. It was fantastic to have the opportunity to work with Álvaro, Miguel, Fernando, Óscar, Adam, Emilio, Geovanny, and Shengjing. Also, a very special gratitude goes out to all my good friends in Madrid, who were willing to help and give their best suggestions. It would have been a lonely time without Zhi, Bo, Xian, Jiayao, Rong, Jianguo, Qiong, Meijuan, Shan, Jie, Qi, Yu, Pengming, Sisi, Yongjun, Baojun, Irene, Angel and many others. It was great, fantastic, wonderful six years, having all the stimulating discussions, sleepless party nights, festival celebrations, Chinese food, games and all the fun. Last but not the least, I would like to thank my parents. They were always supporting me and encouraging me with their best wishes. Without their precious support it would not be possible for me to study abroad and to conduct this research. I am also grateful to my other family members and friends in China who have encouraged and supported me along the way. My deepest appreciation for all your encouragement and help! Resumen Las técnicas avanzadas de extracción de información y la creciente disponibilidad de datos vinculados han dado a luz a la noción de Grafo de Conocimiento (Knowledge Graph, KG) de gran escala. Con la creciente popularidad de KGs que contienen millones de conceptos y entidades, la investigación de herramientas fundamentales que estudian características semánticas de KGs es crítica para el desarrollo de aplicaciones basadas en KG, aparte del estudio de las técnicas de población de KG. Con este enfoque, esta tesis explora la similitud semántica en KGs teniendo en cuenta el concepto de taxonomía, concepto de distribución, la entidad descripciones y las categorías. La similitud semántica captura la cercanía de significados. A través del estudio de la red semántica de conceptos y entidades con relaciones significativas en KGs, hemos propuesto una nueva métrica de semántica WPath semántica, y un nuevo método de computación basado en información gráfica (IC). Con el WPath y el IC basado en gráfos, la similitud semántica de los conceptos se puede calcular directamente, basándose únicamente en el conocimiento estructural y el conocimiento estadístico contenido en KGs. Los experimentos en similitud de palabras han demostrado que la mejora de los métodos propuestos es es- tadísticamente significativa en comparación con los métodos convencionales. Por otra parte, observando que los conceptos suelen ser colocados con descripciones textuales, proponemos un nuevo enfoque de incorporación para formar el concepto y incorporación de palabras conjuntamente. El espacio vectorial compartido de conceptos y palabras ha proporcionado una computación de la similitud conveniente entre conceptos y palabras a través de simili- tud vectorial. De manera adicional, se ilustran algunas aplicaciones de modelos basados en el conocimiento, en corpus y en embeddings en la tarea de desambiguación y clasificación semántica, con el fin de demostrar la capacidad e idoneidad de diferentes métodos de simil- itud en aplicaciones específicas. Por último, la búsqueda de entidad semántica se utiliza como una demostración ilustrativa de un nivel más alto de la aplicación que consiste en similitud basado en el texto de concordancia, la desambiguación y la expansión de la con- sulta. Para implementar la demostración completa de la consulta de información centrada en la entidad, también proponemos un enfoque basado en reglas para construir y ejecutar automáticamente consultas SPARQL. I II Abstract The advanced information extraction techniques and increasing availability of linked data have given birth to the notion of large scale Knowledge Graph (KG). With the increasing popularity of KGs containing millions of concepts and entities, the research of fundamental tools studying semantic features of KGs is critical for the development of KG-based appli- cations, apart from the study of KG population techniques. With such focus, this thesis exploits semantic similarity in KGs taking into consideration of concept taxonomy, concept distribution, entity descriptions and categories. Semantic similarity captures the closeness of meanings. Through studying the semantic network of concepts and entities with meaningful relations in KGs, we proposed a novel WPath semantic similarity metric and new graph-based Information Content (IC) compu- tation method. With the WPath and graph-based IC, semantic similarity of concepts can be computed directly and only based on the structural and statistical knowledge contained in KG. The word similarity experiments have shown that the improvement of the proposed methods is statistical significant comparing to conventional methods. Moreover, observing that concepts are usually collocated with textual descriptions, we propose a novel embedding approach to train concept and word embedding jointly. The shared vector space of concepts and words, has provided convenient similarity computation between concepts and words through vector similarity. Furthermore, the applications of knowledge-based, corpus-based and embedding-based similarity methods are shown and compared in the task of semantic disambiguation and classification, in order to demonstrate the capability and suitability of different similarity methods in specific application. Finally, semantic entity search is used as an illustrative showcase to demonstrate higher level of the application consisting of text matching, disambiguation and query expansion. To implement the complete demonstration of entity-centric information querying, we also propose a rule-based approach for construct- ing and executing SPARQL queries automatically. In summary, the thesis exploits various similarity methods and illustrates their corre- sponding applications for KGs. The proposed similarity methods and presented similarity- based applications would help in facilitating the research and development of applications in KGs. III IV Contents Resumen I Abstract III Contents V 1 Introduction 1 1.1 Motivation . .2 1.2 Objectives . .6 1.3 Solution Outline . .7 1.4 Thesis Organization . .9 2 Foundations: Knowledge Graph and Similarity 11 2.1 Semantic Web and Knowledge Graph . 12 2.1.1 RDF, RDFS, OWL and SPARQL . 13 2.1.2 Knowledge Graph Formalization . 15 2.2 Semantic Similarity . 18 2.2.1 Distance and Similarity . 18 2.2.2 Terminology and Classification . 19 2.2.3 Knowledge-based Similarity Methods . 21 2.2.4 Corpus-based Similarity Methods . 28 2.3 Summary . 34 3 Semantic Similarity Framework 35 V CONTENTS CONTENTS 3.1 Introduction . 37 3.2 WPath Semantic Similarity Metric . 38 3.3 Graph-Based Information Content . 42 3.4 Semantic Similarity Evaluation . 44 3.4.1 Datasets and Implementation . 45 3.4.2 Metrics and Evaluation . 47 3.4.3 Result Analysis and Discussion . 53 3.5 Summary . 55 4 Semantic Disambiguation Framework 57 4.1 Word Sense Disambiguation . 58 4.1.1 Related Works . 59 4.1.2 The Synset2Vec Embedding . 61 4.1.3 Fine-Grained Sense Disambiguation . 65 4.1.4 Evaluation . 67 4.2 Named Entity Disambiguation . 71 4.2.1 Background . 74 4.2.2 Semantic Contextual Similarity for NED . 83 4.2.3 Evaluation . 93 4.3 Summary . 102 5 Semantic Classification and Entity Search 103 5.1 Similarity-Based Classification . 104 5.1.1 Unsupervised Classification . 105 5.1.2 Supervised Classification . 107 5.2 Semantic Entity Search . 111 5.2.1 Related Works . 113 VI CONTENTS CONTENTS 5.2.2 Entity Search Framework . 114 5.2.3 SPARQL Query Generation . 116 5.2.4 Evaluation . 118 5.3 Summary . 121 6 Conclusions and Future Research 123 6.1 Conclusions . 124 6.2 Future Research . 126 Bibliography 129 List of Figures 149 List of Tables 151 Glossary 153 Appendix A Publications 157 A.1 Journal Articles . 157 A.2 Conference Proceedings . 157 Appendix B Developed Tool 159 VII CONTENTS
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