Conference Proceeding

Towards Machine Learning on the Semantic Web.

01/2008; DOI:10.1007/978-3-540-89765-1_17 In proceeding of: Uncertainty Reasoning for the Semantic Web I, ISWC International Workshops, URSW 2005-2007, Revised Selected and Invited Papers
Source: DBLP

ABSTRACT In this paper we explore some of the opportunities and chal- lenges for machine learning on the Semantic Web. The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. Semantic Web standards are used to describe meta data but also have great potential as a general data for- mat for data communication and data integration. Within a broad range of possible applications machine learning will play an increasingly im- portant role: Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of un- structured data, and to integrate semantic information into web mining. Machine learning will increasingly be employed to analyze distributed data sources described in Semantic Web formats and to support approx- imate Semantic Web reasoning and querying. In this paper we discuss existing and future applications of machine learning on the Semantic Web with a strong focus on learning algorithms that are suitable for the relational character of the Semantic Web's data structure. We discuss some of the particular aspects of learning that we expect will be of rele- vance for the Semantic Web such as scalability, missing and contradicting data, and the potential to integrate ontological background knowledge. In addition we review some of the work on the learning of ontologies and on the population of ontologies, mostly in the context of textual data.

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    ABSTRACT: The benefit of using ontologies, defined by the respective data standards, is shown. It is presented how ontologies can be used for the semantic enrichment of data and how this can contribute to the vision of the semantic web to become true. The problems existing today on the way to a true semantic web are pinpointed, different semantic web standards, tools and development frameworks are overlooked and an outlook towards artificial intelligence and agents for searching and mining the data in the semantic web are given, paving the way from data management to information and in the end true knowledge management systems.
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    ABSTRACT: Collective classification algorithms have been used to improve classification performance when network training data with content, link and label information and test data with content and link information are available. Collective classification algorithms use a base classifier which is trained on training content and link data. The base classifier inputs usually consist of the content vector concatenated with an aggregation vector of neighborhood class information. In this paper, instead of using a single base classifier, we propose using different types of base classifiers for content and link. We then combine the content and link classifier outputs using different classifier combination methods. Our experiments show that using heterogeneous classifiers for link and content classification and combining their outputs gives accuracies as good as collective classification. Our method can also be extended to collective classification scenarios with multiple types of content and link.
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