Resolving XML Semantic Ambiguity

ABSTRACT XML semantic-aware processing has become a motivating and important challenge in Web data management, data processing, and information retrieval. While XML data is semi-structured, yet it remains prone to lexical ambiguity, and thus requires dedicated semantic analysis and sense disambiguation processes to assign well-defined meaning to XML elements and attributes. This becomes crucial in an array of applications ranging over semantic-aware query rewriting, semantic document clustering and classification, schema matching, as well as blog analysis and event detection in social networks and tweets. Most existing approaches in this context: i) ignore the problem of identifying ambiguous XML nodes, ii) only partially consider their structural relations/context, iii) use syntactic information in processing XML data regardless of the semantics involved, and iv) are static in adopting fixed disambiguation constraints thus limiting user involvement. In this paper, we provide a new XML Semantic Disambiguation Framework titled XSDF designed to address each of the above motivations, taking as input: an XML document and a general purpose semantic network, and then producing as output a semantically augmented XML tree made of unambiguous semantic concepts. Experiments demonstrate the effectiveness of our approach in comparison with alternative methods. Categories and Subject Descriptors General Terms Algorithms, Measurement, Performance, Design, Experimentation. Keywords XML semantic-aware processing, a m b i g u i t y d e g r e e , s p h e r e neighborhood, XML context vector, semantic network, semantic disambiguation

SemIndex: Semantic-Aware Inverted Index

[email protected] paper focuses on the important problem of semanticaware search in textual (structured, semi-structured, NoSQL) databases. This problem has emerged as a required extension of the standard containment keyword based query to meet user needs in textual databases and IR applications. We provide here a new approach, called SemIndex, that extends the standard inverted index by constructing a tight coupling inverted index graph that combines two main resources: a general purpose semantic network, and a standard inverted index on a collection of textual data. We also provide an extended query model and related processing algorithms with the help of SemIndex. To investigate its effectiveness, we set up experiments to test the performance of SemIndex. Preliminary results have demonstrated the effectiveness, scalability and optimality of our approach.This study is partly funded by: Bourgogne Region program, CNRS, and STIC AmSud project Geo-Climate XMine, and LAU grant SOERC-1314T012.Revisión por pare

Almost Linear Semantic XML Keyword Search

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Semantic and Structure Based XML Similarity: An integrated Approach

Since the last decade, XML has gained growing importance as a major means for information management, and has become inevitable for complex data representation. Due to an unprecedented increasing use of the XML standard, developing efficient techniques for comparing XML-based documents becomes crucial in information retrieval (IR) research. A range of algorithms for comparing hierarchically structured data, e.g. XML documents, have been proposed in the literature. However, to our knowledge, most of them focus exclusively on comparing documents based on structural features, overlooking the semantics involved. In this paper, we deal with this problem and introduce a combined structural/semantic XML similarity approach. Our method integrates IR semantic similarity assessment in an edit distance algorithm, seeking to amend similarity judgments when comparing XML-based documents. Different from previous works, our approach comprises of an original edit distance operation cost model, introducing semantic relatedness of XML element/attribute labels, in traditional edit distance computations. A discussion about our similarity method’s properties, chiefly symmetricity and triangular inequality, with respect to existing measures in the literature is provided here. A prototype has been developed to evaluate the performance of our approach. Experimental results were noticeable. 1

Minimizing user effort in XML grammar matching

International audienceXML grammar matching has found considerable interest recently, due to the growing number of heterogeneous XML documents on the Web, and the need to integrate, search and retrieve XML documents originated from different data sources. In this study, we provide an approach for automatic XML grammar matching and comparison aiming to minimize the amount of user effort required to perform the match task. This requires (i) considering the various characteristics and constraints of XML grammars (in comparison with 'grammar simplifying' approaches), (ii) allowing a flexible combination of different matching criteria (in comparison with static approaches), and (iii) effectively considering the semi-structured nature of XML (in contrast with heuristic methods). To achieve this, we propose an extensible framework based on the concept of tree edit distance as an optimal technique to consider XML structure, integrating different matching criteria to capture all basic XML grammar characteristics, ranging over element semantic and syntactic similarities, cardinality and alternativeness constraints, as well as data-type correspondences and relative ordering. In addition, our framework is flexible, enabling the user to choose mapping cardinality (i.e., 1:1,1:n,n:1,n:n), in comparison with exiting static methods (usually constrained to 1:1). User constraints and feedback are equally considered in order to adjust matching results to the user's perception of correct matches. Experiments on real and synthetic XML grammars demonstrate the effectiveness and efficiency of our matching strategy in identifying mappings, in comparison with alternative methods

Resolving XML Semantic Ambiguity

International audienceXML semantic-aware processing has become a motivating and important challenge in Web data management, data processing, and information retrieval. While XML data is semi-structured, yet it remains prone to lexical ambiguity, and thus requires dedicated semantic analysis and sense disambiguation processes to assign well-defined meaning to XML elements and attributes. This becomes crucial in an array of applications ranging over semantic-aware query rewriting, semantic document clustering and classification, schema matching, as well as blog analysis and event detection in social networks and tweets. Most existing approaches in this context: i) ignore the problem of identifying ambiguous XML nodes, ii) only partially consider their structural relations/context, iii) use syntactic information in processing XML data regardless of the semantics involved, and iv) are static in adopting fixed disambiguation constraints thus limiting user involvement. In this paper, we provide a new XML Semantic Disambiguation Framework titled XSDF designed to address each of the above motivations, taking as input: an XML document and a general purpose semantic network, and then producing as output a semantically augmented XML tree made of unambiguous semantic concepts. Experiments demonstrate the effectiveness of our approach in comparison with alternative method

Semantic and Structure Based XML Similarity: The XS 3 Prototype

Due to the ever-increasing web availability of XML-based data, an efficient approach to compare XML documents becomes crucial in information retrieval. Such comparison of XML documents has applications i

XSDF: A System for XML Semantic Disambiguation

International audienceThis paper briefly describes and evaluates XSDF, a new XML Semantic Disambiguation Framework, taking as input: an XML document and a general purpose semantic network, and then producing as output a semantically augmented XML tree made of unambiguous semantic concepts. Experiments demonstrate the effectiveness of XSDF in comparison with alternative methods

Generic metadata representation framework for social-based event detection, description, and linkage

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