Adding Data Mining Support to SPARQL Via Statistical Relational Learning Methods

Kiefer, Christoph; Bernstein, Abraham; Locher, André

doi:10.1007/978-3-540-68234-9_36

Cited by 34 publications

(33 citation statements)

References 10 publications

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“… Names cannot contain spaces. [6] is a query language for the semantic web, which is format in RDF / XML or OWL. SPARQL language to access data through a Triple (Basic Graph Pattern) consists subject predicate and object.…”

Section: ) Elementsmentioning

confidence: 99%

Data Mining in Semantic Web Data

Chomboon¹,

Kaoungku²,

Kerdprasop³

et al. 2014

IJCTE

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Abstract-This research aims at studying the data mining role in semantic web data. Semantic web is popular in a variety of different applications, but research in data mining in semantic web data, appears less. As open source software for data mining in semantic web open source is minimal, and data model of the semantic web requires RDF or OWL format. These specific formats cannot be used directly in most data mining tools. We thus propose a methodology to mine data that appear in an RDF format. The mining process has been demonstrated through the use of R packages.Index Terms-Data mining, semantic web, R language. I. INTRODUCTIONCurrent data is not stored on a single computer, because the current is the era of information technology and social media, data can be stored in many computers on the internet, is difficult for them to access data quickly and easily. The researchers presented the technology to help manage these data called semantic web. The data in the format or the same specification as RDF/XML, N3, Turtle, N-Triples and OWL.Semantic web [1], [2] has been used in various fields such as Information Systems, Search Engine etc. Large data technology to handle with this is data mining, because the large data analyzed find patterns or relationships of data is an advantage of data mining. Research in the field of data mining in semantic web data is not yet widely, since there is a management tool for data mining of semantic web is less, and data from the semantic web is stored in a format that cannot be used directly in data mining. The research in data mining has appeared very little.Research in the field of data mining in semantic web data applied to various algorithms of data mining, such as data classification, association rule mining etc. Most research using the licensed software such as Microsoft Data Mining Extension (DMX) which is Microsoft SQL Server.From the above it can be seen that the present data are not stored on a single computer always, is difficult to put that information in the internet is analyzed find patterns or relationships with the data mining. This research has proposed methods for data mining in semantic web data. II. BACKGROUNDA. Semantic Web Semantic web, have been developed since the storage is Manuscript received December 13, 2013; revised March 14, 2014. This work was supported in part by grant from Suranaree University of Technology through the funding of Data Engineering Research Unit.The authors are with the School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand (e-mail: chomboon.k@gmail.com).only human to understand the meaning, but the machine cannot understand it, because data without structure. Semantic web has been developed to provide useful data on the Internet that can be analyzed and applied to various tasks. The language used for defining the data structure is RDF The standardization for semantic web in the context of web 3.0 shows in Fig. 2. The components of semantic web are as follows:  XML stands for Extensible ...

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Section: ) Elementsmentioning

confidence: 99%

Data Mining in Semantic Web Data

Chomboon¹,

Kaoungku²,

Kerdprasop³

et al. 2014

IJCTE

View full text Add to dashboard Cite

show abstract

“…This makes it difficult to discover new hypotheses, since the designer of the ontology can be tempted to model only those facts in the ontology that are considered relevant for the mining problem at hand. SPARQL-ML [10] is an approach that foresees the extension of the SPARQL query language [18] with a specialized statement to learn a model for a specific concept or numeric attribute in an RDF dataset. Such models can be seen as explanations in the way we use them in Explain-a-LOD.…”

Section: Related Workmentioning

confidence: 99%

Generating Possible Interpretations for Statistics from Linked Open Data

Paulheim

2012

Lecture Notes in Computer Science

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Abstract. Statistics are very present in our daily lives. Every day, new statistics are published, showing the perceived quality of living in different cities, the corruption index of different countries, and so on. Interpreting those statistics, on the other hand, is a difficult task. Often, statistics collect only very few attributes, and it is difficult to come up with hypotheses that explain, e.g., why the perceived quality of living in one city is higher than in another. In this paper, we introduce Explain-a-LOD, an approach which uses data from Linked Open Data for generating hypotheses that explain statistics. We show an implemented prototype and compare different approaches for generating hypotheses by analyzing the perceived quality of those hypotheses in a user study.

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“…The SPARQL extension presented by Kiefer et al, for example, adds machine learning algorithms (SPARQL-ML [38]) and similarity joins (iSPARQL [36]) to the Semantic Web. Both extensions could lead to a complete new family of Sofas services or at least simplify the implementation of existing ones.…”

Section: Ontologies In Mining Software Repositoriesmentioning

confidence: 99%

SEON: a pyramid of ontologies for software evolution and its applications

et al. 2012

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The Semantic Web provides a standardized, well-established framework to define and work with ontologies. It is especially apt for machine processing. However, researchers in the field of software evolution have not really taken advantage of that so far.In this paper, we address the potential of representing software evolution knowledge with ontologies and Semantic Web technology, such as Linked Data and automated reasoning.We present SEON, a pyramid of ontologies for software evolution, which describes stakeholders, their activities, artifacts they create, and the relations among all of them. We show the use of evolution-specific ontologies for establishing a shared taxonomy of software analysis services, for defining extensible meta-models, for explicitly describing relationships among artifacts, and for linking data such as code structures, issues (change requests), bugs, and basically any changes made to a system over time.For validation, we discuss three different approaches, which are backed by SEON and enable semantically enriched software evolution analysis. These techniques have been fully implemented as tools and cover software analysis with web services, a natural language query interface for developers, and large-scale software visualization. Abstract The Semantic Web provides a standardized, well-established framework to define and work with ontologies. It is especially apt for machine processing. However, researchers in the field of software evolution have not really taken advantage of that so far. In this paper, we address the potential of representing software evolution knowledge with ontologies and Semantic Web technology, such as Linked Data and automated reasoning. We present Seon, a pyramid of ontologies for software evolution, which describes stakeholders, their activities, artifacts they create, and the relations among all of them. We show the use of evolution-specific ontologies for establishing a shared taxonomy of software analysis services, for defining extensible meta-models, for explicitly describing relationships among artifacts, and for linking data such as code structures, issues (change requests), bugs, and basically any changes made to a system over time. For validation, we discuss three different approaches, which are backed by Seon and enable semantically enriched software evolution analysis. These techniques have been fully implemented as tools and cover software analysis with web services, a natural language query interface for developers, and large-scale software visualization.

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Adding Data Mining Support to SPARQL Via Statistical Relational Learning Methods

Cited by 34 publications

References 10 publications

Data Mining in Semantic Web Data

Data Mining in Semantic Web Data

Generating Possible Interpretations for Statistics from Linked Open Data

SEON: a pyramid of ontologies for software evolution and its applications

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