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

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

Matching XML schemas by a new tree matching algorithm.

Schema matching is one of the key operations in XML-based information integration and exchanging applications. Automatically or semi-automatically matching XML schemas has attracted a lot of attentions in academia and industry due to the extensive adoption of XML techniques. One of the most difficult tasks in this problem is to identify structural relations between two XML schemas. This thesis builds an automatic XML schema matching system which generates two types of outputs: the element mappings and schema similarity. In this system, an XML schema is modeled as a tree. We have proposed a new tree matching algorithm to compute the structural relation by extracting the most similar common substructures. The algorithm has been designed to achieve a trade-off between matching optimality and time complexity. The experimental results show that this system can be used to match large XML schemas.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .W366. Source: Masters Abstracts International, Volume: 43-05, page: 1758. Adviser: Jianguo Lu. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Scalable XQuery type matching

XML Schema awareness has been an integral part of the XQuery language since its early design stages. Matching XML data against XML types is the main operation that backs up XQuery type expressions, such as typeswitch, instance of, or certain XPath operators. This interaction is particularly vital in data-centric XQuery applications, where data come with detailed type information from an XML Schema document. So far there has been little work on the optimization of those operations. This work presents an efficient implementation of the runtime aspects of XML Schema support. We propose type ranks as a novel and uniform way to implement all facets of type matching in the W3C XQuery Recommendation. As a concise encoding of the type hierarchy defined by an XML Schema document, type ranks minimize the cost of checking the runtime type of XQuery singleton items. By aggregating type ranks, we leverage the grouping capabilities of modern DBMS implementations to efficiently execute type matching on XQuery sequences. In addition, we improve the complexity bounds incurring with typeswitch expressions over existing approaches. Experiments on an off-the-shelf database system demonstrate the potential of our approach

Coreference detection in XML metadata

Preserving data quality is an important issue in data collection management. One of the crucial issues hereby is the detection of duplicate objects (called coreferent objects) which describe the same entity, but in different ways. In this paper we present a method for detecting coreferent objects in metadata, in particular in XML schemas. Our approach consists in comparing the paths from a root element to a given element in the schema. Each path precisely defines the context and location of a specific element in the schema. Path matching is based on the comparison of the different steps of which paths are composed. The uncertainty about the matching of steps is expressed with possibilistic truth values and aggregated using the Sugeno integral. The discovered coreference of paths can help for determining the coreference of different XML schemas

GXQuery: Extending XQuery for Querying Graph-structured XML Data

XML data can be naturally modeled as a graph. Existing query languages to XML can only express queries of matching XML document with a tree-structured schema with structural and value constraints without the consideration of graph features. The ability of such query languages cannot satisfy various requirements of querying graph-structured XML data. In this paper, GXQuery is presented as an extension of XQuery, an XML query language recommended byW3C, to express more flexible query on graph-structured XML. GXQuery expressions can match XML documentwith graph-structured schema with not only structural and value constraints, but also topological constraints

Generic Schema Matching with Cupid

Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past solutions, showing that a rich range of techniques is available. We then propose a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches. Some of our innovations are the integrated use of linguistic and structural matching, context-dependent matching of shared types, and a bias toward leaf structure where much of the schema content resides. After describing our algorithm, we present experimental results that compare Cupid to two other schema matching systems

Managing uncertainty of XML schema matching

Despite of advances in machine learning technologies, a schema matching result between two database schemas (e.g., those derived from COMA++) is likely to be imprecise. In particular, numerous instances of "possible mappings" between the schemas may be derived from the matching result. In this paper, we study the problem of managing possible mappings between two heterogeneous XML schemas. We observe that for XML schemas, their possible mappings have a high degree of overlap. We hence propose a novel data structure, called the block tree, to capture the commonalities among possible mappings. The block tree is useful for representing the possible mappings in a compact manner, and can be generated efficiently. Moreover, it supports the evaluation of probabilistic twig query (PTQ), which returns the probability of portions of an XML document that match the query pattern. For users who are interested only in answers with k-highest probabilities, we also propose the top-k PTQ, and present an efficient solution for it. The second challenge we have tackled is to efficiently generate possible mappings for a given schema matching. While this problem can be solved by existing algorithms, we show how to improve the performance of the solution by using a divide-andconquer approach. An extensive evaluation on realistic datasets show that our approaches significantly improve the efficiency of generating, storing, and querying possible mappings. © 2010 IEEE.published_or_final_versionThe IEEE 26th International Conference on Data Engineering (ICDE 2010), Long Beach, CA., 1-6 March 2010. In International Conference on Data Engineering. Proceedings, 2010, p. 297-30

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

Deterministic Regular Expressions with Back-References

Most modern libraries for regular expression matching allow back-references (i.e. repetition operators) that substantially increase expressive power, but also lead to intractability. In order to find a better balance between expressiveness and tractability, we combine these with the notion of determinism for regular expressions used in XML DTDs and XML Schema. This includes the definition of a suitable automaton model, and a generalization of the Glushkov construction