Automatic interchange of knowledge between business ontologies

185. Alma-Delia Cuevas, Adolfo Guzman-Arenas (2009) Automatic interchange of knowledge between business ontologies. This technical paper will appear in Proceedings of Intelligent Decision Support Technologies 2009, Japan. Click here. This is a shorter version, suited for a Congress, of paper #183.
A person adds new knowledge to his/her mind, taking into account new information, additional details, better precision, synonyms, homonyms, redundancies, apparent contradictions, and inconsistencies between what he/she knows and new knowledge that he/she acquires. This way, he/she incrementally acquires information keeping at all times it consistent. This information can be perfectly represented by Ontologies. In contrast to human approach, algorithms of Ontologies fusion lack these features, merely being computer-aided editors where a person solves the details and inconsistencies. This article presents a method of Ontology Merging (OM), its algorithm and implementation to fuse or join two Ontologies (obtained from Web documents) in an automatic fashion (without human intervention), producing a third ontology, and taking into account the inconsistencies, contradictions, and redundancies between both Ontologies, thus delivering a result close to reality. OM produces better results, when they are compared against fusions manually carried out. The repeated use of OM allows acquisition of much more information about the same topic.

An extensive explanation of OM can be found in Alma-Delia Cuevas Ph. D. Thesis, click here. Ejemplos, explicaciones extensas, y consideraciones teóricas sobre OM y su desempeño se halla en la tesis de doctorado de Alma Delia Cuevas Rasgado. ABSTRACT: A person’s knowledge increases as more information is obtained from his environment; information sources play an important role in this process. One does not learn from zero, even an animal is born with innate knowledge. Learning happens by adding new concepts or linking them to already existing ones. Although information from outside sources can contradict or confound a person, he has the tools to solve somehow this problem. The knowledge accumulates in what we can call his ontology.
Ontologies can also be structured and defined in computers. This work focuses on ontology fusion; during the fusion the same cases arises as those occurring to a person. The difference is that machines have no common sense, so the challenges are to automate the fusion, to perform it in spite of problems (redundancies, descriptions at different detail levels), and that the result be as close as possible to the result obtained by a person.
Previous works [11, 13, 28 y 40] perform ontology fusion in a semiautomatic, computer-assisted manner. Others [25 y 34] fuse ontologies expressed in a formal notation, but are incapable of fusing mutually-inconsistent ontologies, as most of the real-life ontologies are.
This work presents a process for ontology merging which is automatic and robust. Automatic since the computer detects and solves the problems arising during the fusion and robust because merging occurs in spite of ontologies being mutually inconsistent and present information from different viewpoints. The efficiency of our algorithm is shown by converting by hand several documents in Internet to ontologies in our notation, and then automatically fusing them. Results show a slight error margin in comparison with manual fusion performed by an expert.
RESUMEN. El conocimiento de un ser humano se va acumulando conforme a lo que sucede en su entorno, las fuentes de información tienen un papel importante en este proceso; no se aprende de cero, inclusive un animal nace con conocimiento previo. El aprendizaje sucede agregando nuevos conceptos o asociándolos a los ya existentes. Aunque existe información del exterior que puede contradecir o confundir a un ser humano, éste cuenta con las herramientas que le permite resolverlo de alguna manera. A éste cúmulo de información se le puede llamar su ontología.
Las ontologías también se pueden estructurar y definir en las computadoras. Este trabajo se centra en la unión de ontologías entre computadoras, durante ésta unión pueden suceder los mismos casos que en una persona; la diferencia es que las máquinas carecen de sentido común y los desafíos son hacer la fusión de manera automática, que no se detenga ante los problemas (redundancias, distinto nivel de descripción…) que se presenten y que el resultado sea lo más cercano a la fusión natural de conocimiento del ser humano.
Existen trabajos [11, 13, 28 y 40] que realizan la unión de ontologías pero lo hacen de manera semiautomática, otros [25 y 34] unen ontologías expresadas en un lenguaje formal, pero son incapaces de unir ontologías mutuamente inconsistentes, como lo son la mayoría de las ontologías reales.
Este trabajo presenta un proceso de unión de ontologías de forma automático y robusto. Automático porque la computadora detecta y resuelve los problemas que se presentan durante el proceso de la unión y Robusto porque realiza la unión pese a que las ontologías son mutuamente inconsistentes o representan la información desde distintos ángulos. Se demuestra la eficiencia del algoritmo de fusión a través de varios ejemplos reales con documentos obtenidos de Internet cuyas ontologías se construyeron manualmente y se fusionaron de manera automática. Los resultados tuvieron un ligero margen de error en comparación con la fusión manual de un usuario experto en el tema del documento.