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Finding a particular scientific document amidst a sea of thousands of other documents can often seem like an insurmountable task. The Structure of Scientific Articles shows how linguistic theory can provide a solution by analyzing rhetorical structures to make information retrieval easier and faster.
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Resumen:

\n

 

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Among the different paradigms used by related works to compute semantic similarity, in recent years, information theoretic approaches have shown promising results by computing the information content (IC) of concepts from the knowledge provided by ontologies. These approaches, however, are hampered by the coverage offered by the single input ontology. In this paper, we propose extending IC-based similarity measures by considering multiple ontologies in an integrated way. Several strategies are proposed according to which ontology the evaluated terms belong. Our proposal has been evaluated by means of a widely used benchmark of medical terms and MeSH and SNOMED CT as ontologies. 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Revisión (más o menos) estadística de los artículos publicados en el área de ontology matching en la última década (desde 2003 a 2013)

\n

* Analizan el tipo de articulos publicados (reviews, matching techniques, matching systems, processing frameworks, practical applications and evaluation) en cuanto a número de artículos publicados y los agrupan en el tiempo.

\n

* A la vez que realizan este análisis presentan las técnicas más importantes en el campo del ontology matching a partir de la clasificación propuesta por Euzenat et al. en el libro Ontology Matching.

\n

* Presentan una lista de sistemas/algoritmos que podemos encontrar en la literatura, muchos de los cuales han participado en OAEI. Analizan la cantidad de artículos publicados para cada sistema así como el tiempo entre artículos. Los autores concluyen que la cantidad de publicaciones de cada sistema varía entre 1 y 9 y que el tiempo medio de desarrollo es de 2.6 años.

\n

* En el análisis de aplicaciones reales, los autores señalan que el número de aplicaciones reales llevadas a cabo no llegan al 30% del número de sistemas implementados. Parece un dato relevante que haya tan pocas aplicaciones reales.

\n

Una vez realizada la clasificación y análisis de los artículos publicados en el área entre 2003 y 2013 los autores se preguntan:

\n

* siguen los autores de los artículos investigando en el área de OM (parece que desde 2007 no hay nada nuevo) ?

\n

* por qué no se realizan aplicaciones reales?

\n

Para contestar a estas preguntas diseñaron una evaluación con los participantes de los concursos del OAEI. Contactaron por email con 288 participantes y recibieron 46 respuestas.

\n

El cuestionario estaba formado por 8 preguntas de repuesta abierta. De los 46 participantes, 5 rechazaron la participación, 1 contestó parcialmante y 13 hace tiempo que no estaban en el campo de OM. Finalmente se quedaron con 33 respuestas.

\n

Como conclusiones de las encuestas:

\n

- Los participantes de las encuestas tenían una amplia experiencia en el campo (la media es de 7 años)

\n

- los participantes coinciden con los autores en señalar que hay muchas soluciones teóricas pero pocas aplicaciones reales.

\n

- desde un punto de vista práctico los campos de aplicación en los que coincidían la mayoría de los participantes eran  the medical and life science domain

\n

Este artículo pretende ser (todavía no está publicado, está aceptado para ESWA) un punto de partida para la gente que se está iniciando en el campo del ontology matching y para abordar los principales retos del área.

\n

 

\n

 

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Resumen

\n

Artículo que presenta las métricas más conocidas en cuanto a similitud semántica en ontologías.

\n

No aporta nada nuevo con respecto a otros artículos ya leídos pero sirve para tener todas las métricas juntas.

\n

Lista de métricas que presenta:

\n\n

 

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Semantic similarity
\n
computes the likeness between words,
\n
Semantic similarity
\n
computes the likeness between words,
\n

Resumen

\n

Poco interesante por el dominio pero reúne las técnicas de similitud entre taxonomías más comunes.

\n

Semantic similarity computes the likeness between words,
understood as the degree of taxonomical proximity.

\n

Similarity measures based on the taxonomical structure

\n

In a taxonomy, the simplest way to estimate the distance (dis)
between two concepts c1 and c2 is by calculating the shortest path length (PL, i.e., the minimum number of links) connecting these concepts

\n

Wu and Palmer [25] (W&P) have been developed. Considering that the similarity (sim) between a pair of concepts in an upper level of the taxonomy is smaller than the similarity between a pair in a lower level, they proposed a path-based measure that also takes into account the depth of the concepts in the hierarchy.

\n

Leacock and Chodorow [26] (L&C) also proposed a measure that considers both the shortest path between two concepts (in fact, the number of nodes Np from c1 to c2 including themselves) and the maximum depth D of the taxonomy

\n

Li et al. [27] proposed a similarity measure that combines the
shortest path length and the depth of the ontology evaluated in a non-linear fashion

\n

Choi and Kim [28] also proposed a similarity measure applied to the Yahoo! category tree for solving the problem of topic distillation.
The measure is computed according to the difference on the
depth levels of two concepts and the distance of the shortest path between them

\n

Domain-independent --> WordNet’s large taxonomy, with a relatively homogeneous distribution of semantic links and good inter-domain coverage is the ideal environment for applying these measures.

\n

Information content (IC) measures the amount of information
provided by a given term based on its probability of appearance in a corpus.

\n

Resnik [33] presented a seminal work in which the similarity between a pair of concepts (c1 and c2) is estimated as the amount of taxonomical information they share. In a taxonomy, this information is represented by the least common subsumer of both terms (LCS(c1, c2)), which is the most specific taxonomical ancestor common to c1 and c2 in a given ontology

\n

The most commonly used extensions of Resnik’s measure are
those of Lin [34] and Jiang and Conrath [32].
Lin similarity depends on the relation between the information
content of the LCS of two concepts and the sum of the information content of the individual concepts.

\n

 

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