102,769 research outputs found
XML Schema Clustering with Semantic and Hierarchical Similarity Measures
With the growing popularity of XML as the data representation language, collections of the XML data are exploded in numbers. The methods are required to manage and discover the useful information from them for the improved document handling. We present a schema clustering process by organising the heterogeneous XML schemas into various groups. The methodology considers not only the linguistic and the context of the elements but also the hierarchical structural similarity. We support our findings with experiments and analysis
A Fast Minimal Infrequent Itemset Mining Algorithm
A novel fast algorithm for finding quasi identifiers in large datasets is
presented. Performance measurements on a broad range of datasets demonstrate
substantial reductions in run-time relative to the state of the art and the
scalability of the algorithm to realistically-sized datasets up to several
million records
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
This paper presents a Lisp architecture for a portable NLP system, termed
LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard,
customized and in-house developed NLP tools. Our system facilitates portability
across different institutions and data systems by incorporating an enriched
Common Data Model (CDM) to standardize necessary data elements. It utilizes
UMLS to perform domain adaptation when integrating generic domain NLP tools. It
also features stand-off annotations that are specified by positional reference
to the original document. We built an interval tree based search engine to
efficiently query and retrieve the stand-off annotations by specifying
positional requirements. We also developed a utility to convert an inline
annotation format to stand-off annotations to enable the reuse of clinical text
datasets with inline annotations. We experimented with our system on several
NLP facilitated tasks including computational phenotyping for lymphoma patients
and semantic relation extraction for clinical notes. These experiments
showcased the broader applicability and utility of LAPNLP.Comment: 6 pages, accepted by IEEE BIBM 2018 as regular pape
BlogForever D2.6: Data Extraction Methodology
This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform
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