Shallow and Deep Semantic Similarity among Schema Elements
نویسنده
چکیده
Semantic similarity between schema elements is greatly influenced by the context in which the elements are defined and compared. This paper emphasizes on the role of context in establishing semantic similarity between schema elements resulting two different forms of semantic similarity, i.e., shallow similarity and deep similarity. Shallow similarity is based on the inherent meanings of the elements only, where as deep similarity is a context based semantic similarity. The proper description of semantic similarity is helpful in identifying the corresponding schema elements for the purpose of schema integration. A new taxonomy of semantic similarity presented in this paper also helps to identify the exact nature of correspondence among schema elements, which helps the integrator to determine exact treatment for the corresponding schema elements in schema integration.
منابع مشابه
An Improved Semantic Schema Matching Approach
Schema matching is a critical step in many applications, such as data warehouse loading, Online Analytical Process (OLAP), Data mining, semantic web [2] and schema integration. This task is defined for finding the semantic correspondences between elements of two schemas. Recently, schema matching has found considerable interest in both research and practice. In this paper, we present a new impr...
متن کاملSchema Interpretation: An Aid to the Schema Analysis in Federated Database Design
A new method for schema analysis is described in which reasoning is based upon the real–world semantics of schema elements. The method distinguishes between intrinsic and in–context semantics, which respectively provide a basis for shallow and deep semantic comparisons between element. Real–world semantics are represent as element interpretations which map elements into a pre–defined common con...
متن کاملGlobal Schema Generation Using Formal Ontologies
This paper deals with the problem of handling semantic heterogeneity during schema integration. Semantics refer to the meaning of data in contrast to syntax, which solely defines the structure of schema elements. We focus on the part of semantics related to the meanings of terms used to name schema elements. Our approach does not rely on the names of the schema elements or the structure of the ...
متن کاملA New Method for Improving Computational Cost of Open Information Extraction Systems Using Log-Linear Model
Information extraction (IE) is a process of automatically providing a structured representation from an unstructured or semi-structured text. It is a long-standing challenge in natural language processing (NLP) which has been intensified by the increased volume of information and heterogeneity, and non-structured form of it. One of the core information extraction tasks is relation extraction wh...
متن کاملPresentation of an efficient automatic short answer grading model based on combination of pseudo relevance feedback and semantic relatedness measures
Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2003