29,769 research outputs found
Semantic Modeling of Analytic-based Relationships with Direct Qualification
Successfully modeling state and analytics-based semantic relationships of
documents enhances representation, importance, relevancy, provenience, and
priority of the document. These attributes are the core elements that form the
machine-based knowledge representation for documents. However, modeling
document relationships that can change over time can be inelegant, limited,
complex or overly burdensome for semantic technologies. In this paper, we
present Direct Qualification (DQ), an approach for modeling any semantically
referenced document, concept, or named graph with results from associated
applied analytics. The proposed approach supplements the traditional
subject-object relationships by providing a third leg to the relationship; the
qualification of how and why the relationship exists. To illustrate, we show a
prototype of an event-based system with a realistic use case for applying DQ to
relevancy analytics of PageRank and Hyperlink-Induced Topic Search (HITS).Comment: Proceedings of the 2015 IEEE 9th International Conference on Semantic
Computing (IEEE ICSC 2015
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
Towards the ontology-based approach for factual information matching
Factual information is information based on facts or relating to facts. The reliability of automatically extracted facts is the main problem of processing factual information. The fact retrieval system remains one of the most effective tools for identifying the information for decision-making. In this work, we explore how can natural language processing methods and problem domain ontology help to check contradictions and mismatches in facts automatically
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