Graph comparison

Documents semantic representations built from open Knowledge Graphs (KGs) have proven to be beneficial in tasks such as recommendation, user profiling, and document retrieval. Broadly speaking, a semantic representation of a document can be defined as a graph whose nodes represent concepts and whose edges represent semantic relationships between them. Fine-grained information about the concepts found in the KGs (e.g. DBpedia, YAGO, BabelNet) can be exploited to enrich and refine the representation. Although this kind of semantic representation is a graph, most applications that compare semantic representations reduce this graph to a "flattened" concept-weight representation and use existing well-known vector similarity measures. As a consequence, relevant information related to the graph structure is not exploited. In this project, different graph-based similarity measures are adapted to semantic representation graphs and are implemented and evaluated. Experiments performed on two data sets reveal better results when using our graph similarity measures than when using vector similarity measures. This report presents the conceptual background, the adapted measures, their development, implementation, and evaluation, and ends with some conclusions

• Autores: Cueto Ramírez, Felipe
• Tipo de recurso: Trabajo de grado de pregrado
• Fecha de publicación: 2018
• Institución: Universidad de los Andes
• Repositorio: Séneca: repositorio Uniandes
• Idioma: eng
• OAI Identifier: oai:repositorio.uniandes.edu.co:1992/40301
• Acceso en línea: http://hdl.handle.net/1992/40301
• Palabra clave: Algoritmos de grafos; Administración del conocimiento; Datos enlazados; Ingeniería
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/