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Semantic Translation for Rule-Based Knowledge in Data Mining
[chapter]
2011
Lecture Notes in Computer Science
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We show the effectiveness of our knowledge translation method in decision tree rules and association rules mined from sports and gene data respectively. ...
Considering data size and privacy concerns in a distributed setting, it is neither desirable nor feasible to translate data from one resource to another in data mining. ...
We thank Xiang Shao, Sridhar Ramachandran, and Tom Conlin in the ZFIN group for providing domain knowledge on genetic data, database mappings, and valuable comments. ...
doi:10.1007/978-3-642-23091-2_7
fatcat:g6bfxv3unfc5rk6bwr26rpisk4
A Semantic Approach for Extracting Medical Association Rules
2020
International Journal of Intelligent Engineering and Systems
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Semantic data mining can play an effective rule in analyzing such amounts of data. In this paper, we propose a framework for association rule extraction based on ontology semantics. ...
The Apriori algorithm is used to generate the association rules. ...
Hence, data mining methods can be combined with ontology in order to improve the overall data mining process where ignoring the semantic representation of data leads to generating unreasonable mining models ...
doi:10.22266/ijies2020.0630.26
fatcat:5hrmx4lil5g35lw3iv6lmcnlsi
Discovering interesting information with advances in web technology
2013
SIGKDD Explorations
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, which has established itself as a lingua franca for Web data exchange; and domain-specific markup languages, which are designed based on XML syntax with the goal of preserving semantics in targeted domains ...
In this article, we shed light on some interesting phenomena of the Web: the deep Web, which surfaces database records as Web pages; the Semantic Web, which defines meaningful data exchange formats; XML ...
Association Rule Mining with DSMLs The mining of association rules for XML documents extends the concept of frequent pattern mining in XML. ...
doi:10.1145/2481244.2481255
fatcat:lvr2d5k3cre6lpnwnd2udp22pe
Supporting Frameworks for the Geospatial Semantic Web
[chapter]
2009
Lecture Notes in Computer Science
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Discovering and linking this information poses eminent research challenges to the geospatial semantic web, with regards to the representation and manipulation of geographic data. ...
A lot of information on the web is geographically referenced. ...
In addition, the web now offers accessible mapping applications to allow for precise association of resources with a location on a map (e.g. linking photos on Flickr with Google maps). ...
doi:10.1007/978-3-642-02982-0_23
fatcat:zzsecrn7bbcopiizko2tmnar5e
Graph-based relational learning
2003
SIGKDD Explorations
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While a form of graph-based data mining, GBRL focuses on identifying novel, not necessarily most frequent, patterns in a graph-theoretic representation of data. ...
Learning from graphs, rather than logic, presents representational issues both in input data preparation and output pattern language. ...
A newer issue regarding scalability is what we call dynamic graphs. With the advent of real-time streaming data, many data mining systems must mine incrementally, rather than off-line from scratch. ...
doi:10.1145/959242.959254
fatcat:l2j3t2nb4jfgvej5zvzrsbzb3m
Mining h-Dimensional Enhanced Semantic Association Rule Based on Immune-Based Gene Expression Programming
[chapter]
2006
Lecture Notes in Computer Science
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Rule mining is very important for data mining. However, traditional association rule is relatively weak in semantic representation. ...
To address it, the main contributions of this paper included: (1) proposing formal concepts on h-Dimensional Enhanced Semantic Association Rule (h-DESAR) with selfcontained logic operator; (2) proposing ...
However, complex data mining application requires refined and rich-semantic knowledge representation. Traditional association rule is relatively weak in semantic representation. ...
doi:10.1007/11906070_5
fatcat:7edoouh5uffdldp4hxljtp67ri
Mining Generalized Associations of Semantic Relations from Textual Web Content
2007
IEEE Transactions on Knowledge and Data Engineering
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Traditional text mining techniques transform free text into flat bags of words representation, which does not preserve sufficient semantics for the purpose of knowledge discovery. ...
In this paper, we present a two-step procedure to mine generalized associations of semantic relations conveyed by the textual content of Web documents. ...
Association Rule Mining and Frequent Pattern Mining Association Rule Mining (ARM) [17] since its introduction has become one of the key data mining techniques in the field of Knowledge Discovery in Database ...
doi:10.1109/tkde.2007.36
fatcat:jkxp3oiotbe2vmmegagu7ekpuu
CrossMine: Efficient Classification Across Multiple Database Relations
[chapter]
2006
Lecture Notes in Computer Science
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However, most classification approaches only work on single "flat" data relations. ...
Most of today's structured data is stored in relational databases. ...
Rule Representation The algorithm aims at finding rules that can distinguish positive target tuples from negative ones. Each rule is a list of predicates, associated with a class label. ...
doi:10.1007/11615576_9
fatcat:5l56zymakbfgrmor6eae475lgm
A Study on Classification Approaches across Multiple Database Relations
2011
International Journal of Computer Applications
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Classification is an important task in data mining and machine learning, which has been studied extensively and has a wide range of applications. ...
Lots of algorithms have been proposed to build accurate and scalable classifiers. ...
With the development of data mining techniques, multi relational data mining has become a new research area. ...
doi:10.5120/1740-2366
fatcat:ianpqmt25vb6bbpmajvc76qxxy
ONTOGRATE: TOWARDS AUTOMATIC INTEGRATION FOR RELATIONAL DATABASES AND THE SEMANTIC WEB THROUGH AN ONTOLOGY-BASED FRAMEWORK
2010
International Journal of Semantic Computing (IJSC)
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The testing results of our implemented OntoGrate system in different domains show that the large amount of data in relational databases can be directly utilized for answering Semantic Web queries rather ...
database schemas to Semantic Web ontologies; (ii) we developed a highly automatic ontology mapping system which leverages object reconciliation and multi-relational data mining techniques; (iii) we developed ...
Pan at the University of Aberdeen for helpful discussion on OWL-QL and query answering. ...
doi:10.1142/s1793351x10000961
fatcat:zlslblvsqvcojbz7heonzryohi
Managing Semantic Big Data for Intelligence
2013
Semantic Technologies for Intelligence, Defense, and Security
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The development of this scalable data integration platform rests on the layered dataspace approach, makes use of recent Big Data technologies and leverages ontological models, and semantic-based analysis ...
Leveraging recent advances in data integration, Semantic Web and Big Data technologies, we are adapting key concepts of unified dataspaces and semantic enrichment for the design and implementation of a ...
, (semantic annotation of text based on domain ontologies, and automated extraction of facts from documents based on pattern matching rules), as well as multiple reasoners (rule-based reasoner, case-based ...
dblp:conf/stids/Boury-Brisset13
fatcat:j3f4ajty4bbfzozmuvphgwdiia
Towards SHACL Learning from Knowledge Graphs
2020
International Semantic Web Conference
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Knowledge Graphs (KGs) are typically large data-first knowledge bases with weak inference rules and weakly-constraining data schemes. ...
The SHACL Shapes Constraint Language is a W3C recommendation for the expression of shapes as constraints on graph data. ...
They work with a small amount of data and their representation formalism they use for their output is difficult to compare with the well-defined IOP rules which we use in this paper. [2] carries out the ...
dblp:conf/semweb/OmranTMH20a
fatcat:hrutfyf25faljgqvbqn7ed7brm
A Survey on State-of-the-art Techniques for Knowledge Graphs Construction and Challenges ahead
[article]
2021
arXiv
pre-print
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Structuring this data into a knowledge graph enables multitudes of intelligent applications such as deep question answering, recommendation systems, semantic search, etc. ...
The knowledge graph is an emerging technology that allows logical reasoning and uncovers new insights using content along with the context. ...
Some work has been done on the scalability issue of rule
mining/ pattern mining approaches. ...
arXiv:2110.08012v2
fatcat:q6utzgjahfehpftol3dttgolui
Provenance Information in Biomedical Knowledge Repositories - A Use Case
2009
International Semantic Web Conference
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While the storage and processing of statements has been greatly facilitated by the emergence of powerful triple stores and the standardization of query languages (e.g., SPARQL), recording and exploiting ...
ACKNOWLEDGMENT This research was supported in part by the Intramural Research Program of the National Institutes of Health (NIH), National Library of Medicine (NLM). ...
Text mining techniques are used to extract "predications" (i.e., statements) from text, for example in the Semantic Medline project [3] . ...
dblp:conf/semweb/Bodenreider08
fatcat:3mgnyyiho5hpnk5xlnacoordde
Scaling-up reasoning and advanced analytics on BigData
2018
Theory and Practice of Logic Programming
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systems, and thus obtain the performance and scalability that relational systems had achieved, as far back as the 80s, using data-parallelization on shared-nothing architectures. ...
and reasoning with the performance and scalability by which relational databases managed BigData. ...
This work was supported in part by NSF grants IIS-1218471, IIS-1302698 and CNS-1351047, and U54EB020404 awarded by NIH Big Data to Knowledge (BD2K). ...
doi:10.1017/s1471068418000418
fatcat:xvfcjy4fi5ctvpesstdhqrhsvq
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