8,715 research outputs found
Web and Semantic Web Query Languages
A number of techniques have been developed to facilitate
powerful data retrieval on the Web and Semantic Web. Three categories
of Web query languages can be distinguished, according to the format
of the data they can retrieve: XML, RDF and Topic Maps. This article
introduces the spectrum of languages falling into these categories
and summarises their salient aspects. The languages are introduced using
common sample data and query types. Key aspects of the query
languages considered are stressed in a conclusion
Survey over Existing Query and Transformation Languages
A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability
of many current Semantic Web approaches to cope with data available in such diverging
representation formalisms as XML, RDF, or Topic Maps. A common query language is the first
step to allow transparent access to data in any of these formats. To further the understanding
of the requirements and approaches proposed for query languages in the conventional as well
as the Semantic Web, this report surveys a large number of query languages for accessing
XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from
all these areas. From the detailed survey of these query languages, a common classification
scheme is derived that is useful for understanding and differentiating languages within and
among all three areas
Unified Framework for Data Mining using Frequent Model Tree
Abstract: Data mining is the science of discovering hidden patterns from data. Over the past years, a plethora of data mining algorithms has been developed to carry out various data mining tasks such as classification, clustering, association mining and regression. All the methods are ad-hoc in nature, and there exists no unifying framework which unites all the data mining tasks. This study proposes such a framework which describes a data modelling technique to model data in a manner that can be used to accomplish all kinds of data mining tasks. This study proposed a novel algorithm known as Frequent Model (FM)-Growth, based on Frequent pattern (FP)-Growth algorithm. The algorithm is used to find frequent patterns or models from data. These models will then be used to carry out various data mining tasks such as classification, clustering. The advantage of these frequent models is that they can be used as it is with any data mining task irrespective of the nature of the task. The algorithm is carried out in two stages. In the first stage, we grow the FM-tree from the data and in the second stage, we extract the frequent models from the FM-tree. The accuracy of the proposed algorithm is high. However, the algorithm is computationally expensive when searching for frequent models in high volume and high dimensional data. The reason of expensiveness is that it needs to travel all the nodes of a tree. The study suggests measures to be taken to improve the efficiency of the overall process using dictionary data structure.Keywords: Data Mining, Frequent Pattern Recognition Unified Framework, Classification, Clustering, FPGrowth tree
Integrating and querying similar tables from PDF documents using deep learning
Large amount of public data produced by enterprises are in semi-structured
PDF form. Tabular data extraction from reports and other published data in PDF
format is of interest for various data consolidation purposes such as analysing
and aggregating financial reports of a company. Queries into the structured
tabular data in PDF format are normally processed in an unstructured manner
through means like text-match. This is mainly due to that the binary format of
PDF documents is optimized for layout and rendering and do not have great
support for automated parsing of data. Moreover, even the same table type in
PDF files varies in schema, row or column headers, which makes it difficult for
a query plan to cover all relevant tables. This paper proposes a deep learning
based method to enable SQL-like query and analysis of financial tables from
annual reports in PDF format. This is achieved through table type
classification and nearest row search. We demonstrate that using word embedding
trained on Google news for header match clearly outperforms the text-match
based approach in traditional database. We also introduce a practical system
that uses this technology to query and analyse finance tables in PDF documents
from various sources
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Retrieving information from heterogeneous freight data sources to answer natural language queries
textThe ability to retrieve accurate information from databases without an extensive knowledge of the contents and organization of each database is extremely beneficial to the dissemination and utilization of freight data. The challenges, however, are: 1) correctly identifying only the relevant information and keywords from questions when dealing with multiple sentence structures, and 2) automatically retrieving, preprocessing, and understanding multiple data sources to determine the best answer to user’s query. Current named entity recognition systems have the ability to identify entities but require an annotated corpus for training which in the field of transportation planning does not currently exist. A hybrid approach which combines multiple models to classify specific named entities was therefore proposed as an alternative. The retrieval and classification of freight related keywords facilitated the process of finding which databases are capable of answering a question. Values in data dictionaries can be queried by mapping keywords to data element fields in various freight databases using ontologies. A number of challenges still arise as a result of different entities sharing the same names, the same entity having multiple names, and differences in classification systems. Dealing with ambiguities is required to accurately determine which database provides the best answer from the list of applicable sources. This dissertation 1) develops an approach to identify and classifying keywords from freight related natural language queries, 2) develops a standardized knowledge representation of freight data sources using an ontology that both computer systems and domain experts can utilize to identify relevant freight data sources, and 3) provides recommendations for addressing ambiguities in freight related named entities. Finally, the use of knowledge base expert systems to intelligently sift through data sources to determine which ones provide the best answer to a user’s question is proposed.Civil, Architectural, and Environmental Engineerin
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