2,907 research outputs found
Automated schema matching techniques: an exploratory study
Manual schema matching is a problem for many database applications that use multiple data sources including data warehousing and e-commerce applications. Current research attempts to address this problem by developing algorithms to automate aspects of the schema-matching task. In this paper, an approach using an external dictionary facilitates automated discovery of the semantic meaning of database schema terms. An experimental study was conducted to evaluate the performance and accuracy of five schema-matching techniques with the proposed approach, called SemMA. The proposed approach and results are compared with two existing semi-automated schema-matching approaches and suggestions for future research are made
Using Element Clustering to Increase the Efficiency of XML Schema Matching
Schema matching attempts to discover semantic mappings between elements of two schemas. Elements are cross compared using various heuristics (e.g., name, data-type, and structure similarity). Seen from a broader perspective, the schema matching problem is a combinatorial problem with an exponential complexity. This makes the naive matching algorithms for large schemas prohibitively inefficient. In this paper we propose a clustering based technique for improving the efficiency of large scale schema matching. The technique inserts clustering as an intermediate step into existing schema matching algorithms. Clustering partitions schemas and reduces the overall matching load, and creates a possibility to trade between the efficiency and effectiveness. The technique can be used in addition to other optimization techniques. In the paper we describe the technique, validate the performance of one implementation of the technique, and open directions for future research
Multilingual Schema Matching for Wikipedia Infoboxes
Recent research has taken advantage of Wikipedia's multilingualism as a
resource for cross-language information retrieval and machine translation, as
well as proposed techniques for enriching its cross-language structure. The
availability of documents in multiple languages also opens up new opportunities
for querying structured Wikipedia content, and in particular, to enable answers
that straddle different languages. As a step towards supporting such queries,
in this paper, we propose a method for identifying mappings between attributes
from infoboxes that come from pages in different languages. Our approach finds
mappings in a completely automated fashion. Because it does not require
training data, it is scalable: not only can it be used to find mappings between
many language pairs, but it is also effective for languages that are
under-represented and lack sufficient training samples. Another important
benefit of our approach is that it does not depend on syntactic similarity
between attribute names, and thus, it can be applied to language pairs that
have distinct morphologies. We have performed an extensive experimental
evaluation using a corpus consisting of pages in Portuguese, Vietnamese, and
English. The results show that not only does our approach obtain high precision
and recall, but it also outperforms state-of-the-art techniques. We also
present a case study which demonstrates that the multilingual mappings we
derive lead to substantial improvements in answer quality and coverage for
structured queries over Wikipedia content.Comment: VLDB201
Nonparametric Bayesian Modeling for Automated Database Schema Matching
The problem of merging databases arises in many government and commercial
applications. Schema matching, a common first step, identifies equivalent
fields between databases. We introduce a schema matching framework that builds
nonparametric Bayesian models for each field and compares them by computing the
probability that a single model could have generated both fields. Our
experiments show that our method is more accurate and faster than the existing
instance-based matching algorithms in part because of the use of nonparametric
Bayesian models
Defining the XML schema matching problem for a personal schema based query answering system
In this report, we analyze the problem of personal schema matching. We define the ingredients of the XML schema matching problem using constraint logic programming. This allows us to thourougly investigate specific matching problems. We do not have the ambition to provide for a formalism that covers all kinds of schema matching problems. The target is specifically personal schema matching using XML. The report is organized as follows. Chapter 2 provides a detailed description of our research domain - the Personal Schema Query Answering System. In chapter 3, we introduce a framework for defining the XML schema matching problem. The XML schema matching problem is defined using this framework in chapter 4. An important component of the XML schema matching problem is the objective function, which is investigated in chapter 5. Chapter 6 presents the related research, with conclusions and further research being discussed in chapter 7. Throughout the report, we use expressions like 'schema matching', 'XML schema matching' and 'semantic XML schema matching'. Unless explicitly stated otherwise or strongly suggested by the context of the story, those expressions all refer to the same thing: semantic matching of XML schemas as used in personal schema querying. Furthermore, basic knowledge of the XML-schema language is assumed
Valentine: Evaluating Matching Techniques for Dataset Discovery
Data scientists today search large data lakes to discover and integrate
datasets. In order to bring together disparate data sources, dataset discovery
methods rely on some form of schema matching: the process of establishing
correspondences between datasets. Traditionally, schema matching has been used
to find matching pairs of columns between a source and a target schema.
However, the use of schema matching in dataset discovery methods differs from
its original use. Nowadays schema matching serves as a building block for
indicating and ranking inter-dataset relationships. Surprisingly, although a
discovery method's success relies highly on the quality of the underlying
matching algorithms, the latest discovery methods employ existing schema
matching algorithms in an ad-hoc fashion due to the lack of openly-available
datasets with ground truth, reference method implementations, and evaluation
metrics. In this paper, we aim to rectify the problem of evaluating the
effectiveness and efficiency of schema matching methods for the specific needs
of dataset discovery. To this end, we propose Valentine, an extensible
open-source experiment suite to execute and organize large-scale automated
matching experiments on tabular data. Valentine includes implementations of
seminal schema matching methods that we either implemented from scratch (due to
absence of open source code) or imported from open repositories. The
contributions of Valentine are: i) the definition of four schema matching
scenarios as encountered in dataset discovery methods, ii) a principled dataset
fabrication process tailored to the scope of dataset discovery methods and iii)
the most comprehensive evaluation of schema matching techniques to date,
offering insight on the strengths and weaknesses of existing techniques, that
can serve as a guide for employing schema matching in future dataset discovery
methods
Review implementation of linguistic approach in schema matching
Research related schema matching has been conducted since last decade. Few approach related schema matching has been conducted with various methods such as neuron network, feature selection, constrain based, instance based, linguistic, and so on. Some field used schema matching as basic model such as e-commerce, e-business and data warehousing. Implementation of linguistic approach itself has been used a long time with various problem such as to calculated entity similarity values in two or more schemas. The purpose of this paper was to provide an overview of previous studies related to the implementation of the linguistic approach in the schema matching and finding gap for the development of existing methods. Futhermore, this paper focused on measurement of similarity in linguistic approach in schema matching
XML Matchers: approaches and challenges
Schema Matching, i.e. the process of discovering semantic correspondences
between concepts adopted in different data source schemas, has been a key topic
in Database and Artificial Intelligence research areas for many years. In the
past, it was largely investigated especially for classical database models
(e.g., E/R schemas, relational databases, etc.). However, in the latest years,
the widespread adoption of XML in the most disparate application fields pushed
a growing number of researchers to design XML-specific Schema Matching
approaches, called XML Matchers, aiming at finding semantic matchings between
concepts defined in DTDs and XSDs. XML Matchers do not just take well-known
techniques originally designed for other data models and apply them on
DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical
structure of a DTD/XSD) to improve the performance of the Schema Matching
process. The design of XML Matchers is currently a well-established research
area. The main goal of this paper is to provide a detailed description and
classification of XML Matchers. We first describe to what extent the
specificities of DTDs/XSDs impact on the Schema Matching task. Then we
introduce a template, called XML Matcher Template, that describes the main
components of an XML Matcher, their role and behavior. We illustrate how each
of these components has been implemented in some popular XML Matchers. We
consider our XML Matcher Template as the baseline for objectively comparing
approaches that, at first glance, might appear as unrelated. The introduction
of this template can be useful in the design of future XML Matchers. Finally,
we analyze commercial tools implementing XML Matchers and introduce two
challenging issues strictly related to this topic, namely XML source clustering
and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure
DYMS (Dynamic Matcher Selector) – Scenario-based Schema Matcher Selector
Schema matching is one of the main challenges in different information system integration contexts. Over the past 20 years, different schema matching methods have been proposed and shown to be successful in various situations. Although numerous advanced matching algorithms have emerged, schema matching research remains a critical issue. Different algorithms are implemented to resolve different types of schema heterogeneities, including differences in design methodologies, naming conventions, and the level of specificity of schemas, amongst others. The algorithms are usually too generic regardless of the schema matching scenario. This situation indicates that a single matcher cannot be optimized for all matching scenarios. In this research, I proposed a dynamic matcher selector (DYMS) as a probable solution to the aforementioned problem. The proposed DYMS analyzes the schema matching scenario and selects the most appropriate matchers for a given scenario. Selecting matchers are weighted based on the parameter optimization process, which adopts the heuristic learning approach. The DYMS returns the alignment result of input schemas
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