#Discovery synonym manual#

The resulting DBpedia knowledge base currently describes over 2.6 million entities. The DBpedia project is a community effort to extract structured information from Wikipedia and to make this information accessible on the Web. We describe a set of experiments on several real-world domains, and show that LSD proposes semantic mappings with a high degree of accuracy. Importantly, its architecture is extensible to additional learners that may exploit new kinds of information. Our approach thus is distinguished in that it incorporates multiple types of knowledge. To further improve matching accuracy, we extend machine learning techniques so that LSD can incorporate domain constraints as an additional source of knowledge, and develop a novel learner that utilizes the structural information in XML documents. Once the learners have been trained, LSD finds semantic mappings for a new data source by applying the learners, then combining their predictions using a meta-learner. Each learner exploits a different type of information either in the source schemas or in their data. LSD first asks the user to provide the semantic mappings for a small set of data sources, then uses these mappings together with the sources to train a set of learners. We describe LSD, a system that employs and extends current machine-learning techniques to semi-automatically find such mappings.

#Discovery synonym manual#

A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the source schemas and the mediated schema. In addition to finding confident positive rules that have a strong correlation, the algorithm discovers negative association rules with strong negative correlation between the antecedents and consequents.ДЄ data-integration system provides access to a multitude of data sources through a single mediated schema. In this paper we propose an algorithm that extends the support-confidence framework with a sliding correlation coefficient threshold. Despite their usefulness, and while they were referred to in many publications, very few algorithms to mine them have been proposed to date. Indeed, mining for such rules necessitates the examination of an exponentially large search space. Many other applications would benefit from negative association rules if it was not for the expensive process to discover them. They are also very convenient for associative classifiers, classifiers that build their classification model based on association rules. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. Negative association rules also consider the same items, but in addition consider negated items (i.e. Such rules are referred to as positive association rules. Typical association rules consider only items enumerated in transactions. We show that synonym discovery in RDF data can be done efficiently using the proposed techniques based on association rule mining. Synonym discovery is useful for discovering globally valid synonyms for a thesauros as well supporting the completeness of SPARQL query results by including results that are connected to synonym predicates. A novel application that is based on mining configurations is synonym discovery. We described elaborated use cases such as ontology engineering, data imputation that are based on configurations in the context of RDF subjects. We introduce the concept of mining configurations that allows us to mine RDF data on statement level.

However there has been only limited application of association rules on semantic web data. One of the promising underlying techniques is association rule mining. The amount of available open data requires profiling and integration for desired applications. Its underlying data model RDF is heterogeneous and contains machine readable semantic relations. Linked Open Data brings new challenges and opportunities for the data mining community.