Query languages for data exchange

2009 ◽  
Author(s):  
Marcelo Arenas ◽  
Pablo Barceló ◽  
Juan Reutter
Keyword(s):  
Data Exchange ◽  
Query Languages

On the Usage of Structural Distance Metrics for Mining Hierarchical Structures

2011 ◽  
pp. 216-246
Author(s):  
Theodore Dalamagas ◽  
Tao Cheng ◽  
Timos Selis
Keyword(s):  
Data Exchange ◽  
Hierarchical Structures ◽  
Structural Similarity ◽  
Query Languages ◽  
Structural Aspect ◽  
Distance Metrics ◽  
Classification Methods ◽  
Standard Data ◽  
Structural Distance ◽  
The Web

The recent proliferation of XML-based standards and technologies demonstrates the need for effective management of hierarchical structures. Such structures are used, for example, to organize data in product catalogs, taxonomies of thematic categories, concept hierarchies, etc. Since the XML language has become the standard data exchange format on the Web, organizing data in hierarchical structures has been vastly established. Even if data are not stored natively in such structures, export mechanisms make data publicly available in hierarchical structures to enable its automatic processing by programs, scripts and agents. Processing data encoded in hierarchical structures has been a popular research issue, resulting in the design of effective query languages. However, the inherent structural aspect of such encodings has not received strong attention till lately, when the requirement for mining tasks, like clustering/classification methods, similarity ranking, etc., on hierarchical structures has been raised. The key point to perform such tasks is the design of a structural distance metric to quantify the structural similarity between hierarchical structures. The chapter will study distance metrics that capture the structural similarity between hierarchical structures and approaches that exploit structural distance metrics to perform mining tasks on them.

Query Languages for Data Exchange: Beyond Unions of Conjunctive Queries

2010 ◽  
Vol 49 (2) ◽  
pp. 489-564 ◽  
Author(s):  
Marcelo Arenas ◽  
Pablo Barceló ◽  
Juan Reutter
Keyword(s):  
Data Exchange ◽  
Query Languages ◽  
Conjunctive Queries

Machine-Scored Syntax: Comparison of the CLAN Automatic Scoring Program to Manual Scoring

2020 ◽  
Vol 51 (2) ◽  
pp. 479-493
Author(s):  
Jenny A. Roberts ◽  
Evelyn P. Altenberg ◽  
Madison Hunter
Keyword(s):  
Data Exchange ◽  
Child Language ◽  
Exchange System ◽  
Absolute Point ◽  
Language Analysis ◽  
Search Patterns ◽  
Language Data ◽  
Report Accuracy ◽  
Point To Point ◽  
Automatic Scoring

Purpose The results of automatic machine scoring of the Index of Productive Syntax from the Computerized Language ANalysis (CLAN) tools of the Child Language Data Exchange System of TalkBank (MacWhinney, 2000) were compared to manual scoring to determine the accuracy of the machine-scored method. Method Twenty transcripts of 10 children from archival data of the Weismer Corpus from the Child Language Data Exchange System at 30 and 42 months were examined. Measures of absolute point difference and point-to-point accuracy were compared, as well as points erroneously given and missed. Two new measures for evaluating automatic scoring of the Index of Productive Syntax were introduced: Machine Item Accuracy (MIA) and Cascade Failure Rate— these measures further analyze points erroneously given and missed. Differences in total scores, subscale scores, and individual structures were also reported. Results Mean absolute point difference between machine and hand scoring was 3.65, point-to-point agreement was 72.6%, and MIA was 74.9%. There were large differences in subscales, with Noun Phrase and Verb Phrase subscales generally providing greater accuracy and agreement than Question/Negation and Sentence Structures subscales. There were significantly more erroneous than missed items in machine scoring, attributed to problems of mistagging of elements, imprecise search patterns, and other errors. Cascade failure resulted in an average of 4.65 points lost per transcript. Conclusions The CLAN program showed relatively inaccurate outcomes in comparison to manual scoring on both traditional and new measures of accuracy. Recommendations for improvement of the program include accounting for second exemplar violations and applying cascaded credit, among other suggestions. It was proposed that research on machine-scored syntax routinely report accuracy measures detailing erroneous and missed scores, including MIA, so that researchers and clinicians are aware of the limitations of a machine-scoring program. Supplemental Material https://doi.org/10.23641/asha.11984364

Geoenvironmental and Geotechnical Data Exchange: Setting the Standard

2008 ◽  
Author(s):  
Scot D. Weaver ◽  
Thomas E. Lefchik ◽  
Marc I. Hoit ◽  
Kirk Beach
Keyword(s):  
Data Exchange ◽  
Geotechnical Data

Classification of means and methods of the Web semantic retrieval

2017 ◽  
pp. 030-050
Author(s):  
J.V. Rogushina ◽  
Keyword(s):  
Search Engines ◽  
Domain Knowledge ◽  
Information Needs ◽  
Web Search ◽  
User Interaction ◽  
Query Languages ◽  
Semantic Search ◽  
Semantic Retrieval ◽  
The Web

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.

Computing Cores for Existential Rules with the Standard Chase and ASP

2020 ◽  
Author(s):  
Markus Krötzsch
Keyword(s):  
Large Class ◽  
Data Exchange ◽  
Answer Set Programming ◽  
Query Answering ◽  
Universal Model ◽  
The Core ◽  
Universal Models ◽  
General Terms ◽  
Special Cases ◽  
The Many

To reason with existential rules (a.k.a. tuple-generating dependencies), one often computes universal models. Among the many such models of different structure and cardinality, the core is arguably the “best”. Especially for finitely satisfiable theories, where the core is the unique smallest universal model, it has advantages in query answering, non-monotonic reasoning, and data exchange. Unfortunately, computing cores is difficult and not supported by most reasoners. We therefore propose ways of computing cores using practically implemented methods from rule reasoning and answer set programming. Our focus is on cases where the standard chase algorithm produces a core. We characterise this desirable situation in general terms that apply to a large class of cores, derive concrete approaches for decidable special cases, and generalise these approaches to non-monotonic extensions of existential rules.

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