Statistical Relational Learning with Formal Ontologies

Abstract. Three common approaches for deriving or predicting instantiated relations are information extraction, deductive reasoning and machine learning. Information extraction uses subsymbolic unstructured sensory information, e.g. in form of texts or images, and extracts statements using various methods ranging from simple classifiers to the most sophisticated NLP approaches. Deductive reasoning is based on a symbolic representation and derives new statements from logical axioms. Finally, machine learning can both support information extraction by deriving symbolic representations from sensory data, e.g., via classification, and can support deductive reasoning by exploiting regularities in structured data. In this paper we combine all three methods to exploit the available information in a modular way, by which we mean that each approach, i.e., information extraction, deductive reasoning, machine learning, can be optimized independently to be combined in an overall system. We validate our model using data from the YAGO2 ontology, and from Linked Life Data and Bio2RDF, all of which are part of the Linked Open Data (LOD) cloud.

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“…Link prediction is covered and surveyed in [27] [13]. Inclusion of ontological prior knowledge to relational learning has been discussed in [28].…”

Section: Related Workmentioning

confidence: 99%

Combining Information Extraction, Deductive Reasoning and Machine Learning for Relation Prediction

Jiang

Huang

Nickel

et al. 2012

Lecture Notes in Computer Science

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“…3-6) Most of the results have been published before (cmp. Huang et al 2010;Rettinger et al 2009), but we added some new results or summarized existing results to draw concise conclusions.…”

Section: Selected Experimental Results On Semantic Web Mining Tasksmentioning

confidence: 93%

“…This is traditionally utilized for standard deductive inference tasks like instance membership checking or subsumption. The Infinite Hidden Semantic Model (IHSM) proposed by Rettinger et al (2009) combines the latent-class relational learning of the IHRM (cmp. Sect.…”

Section: First Order Probabilistic Inferencementioning

confidence: 99%

Mining the Semantic Web

Rettinger

Lösch

Tresp

et al. 2012

Data Min Knowl Disc

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In the Semantic Web vision of the World Wide Web, content will not only be accessible to humans but will also be available in machine interpretable form as ontological knowledge bases. Ontological knowledge bases enable formal querying and reasoning and, consequently, a main research focus has been the investigation of how deductive reasoning can be utilized in ontological representations to enable more advanced applications. However, purely logic methods have not yet proven to be very effective for several reasons: First, there still is the unsolved problem of scalability of reasoning to Web scale. Second, logical reasoning has problems with uncertain information, which is abundant on Semantic Web data due to its distributed and heterogeneous nature. Third, the construction of ontological knowledge bases suitable for advanced reasoning techniques is complex, which ultimately results in Responsible editor: 123 614 A. Rettinger et al.a lack of such expressive real-world data sets with large amounts of instance data. From another perspective, the more expressive structured representations open up new opportunities for data mining, knowledge extraction and machine learning techniques. If moving towards the idea that part of the knowledge already lies in the data, inductive methods appear promising, in particular since inductive methods can inherently handle noisy, inconsistent, uncertain and missing data. While there has been broad coverage of inducing concept structures from less structured sources (text, Web pages), like in ontology learning, given the problems mentioned above, we focus on new methods for dealing with Semantic Web knowledge bases, relying on statistical inference on their standard representations. We argue that machine learning research has to offer a wide variety of methods applicable to different expressivity levels of Semantic Web knowledge bases: ranging from weakly expressive but widely available knowledge bases in RDF to highly expressive first-order knowledge bases, this paper surveys statistical approaches to mining the Semantic Web. We specifically cover similarity and distance-based methods, kernel machines, multivariate prediction models, relational graphical models and first-order probabilistic learning approaches and discuss their applicability to Semantic Web representations. Finally, we present selected experiments which were conducted on Semantic Web mining tasks for some of the algorithms presented before. This is intended to show the breadth and general potential of this exiting new research and application area for data mining.

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“…Thor et al [10] have proposed a link prediction method which is based on finding frequent patterns in the data graph. Rettinger et al [7] have proposed a method for integrating ontology-based constraints with statistical relational learning. These approaches are tailored towards specific applications and thus do not expose the general applicability of kernel methods.…”

Section: Related Work: Specialized Methods For Mining Semantic Datamentioning

confidence: 99%

“…the data representation and the learning algorithm have been devised specifically for the problem at hand [6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Graph Kernels for RDF Data

Lösch

Bloehdorn²,

Rettinger

2012

Lecture Notes in Computer Science

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Abstract. The increasing availability of structured data in Resource Description Framework (RDF) format poses new challenges and opportunities for data mining. Existing approaches to mining RDF have only focused on one specific data representation, one specific machine learning algorithm or one specific task. Kernels, however, promise a more flexible approach by providing a powerful framework for decoupling the data representation from the learning task. This paper focuses on how the well established family of kernel-based machine learning algorithms can be readily applied to instances represented as RDF graphs. We first review the problems that arise when conventional graph kernels are used for RDF graphs. We then introduce two versatile families of graph kernels specifically suited for RDF, based on intersection graphs and intersection trees. The flexibility of the approach is demonstrated on two common relational learning tasks: entity classification and link prediction. The results show that our novel RDF graph kernels used with Support Vector Machines (SVMs) achieve competitive predictive performance when compared to specialized techniques for both tasks.

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Statistical Relational Learning with Formal Ontologies

Cited by 31 publications

References 12 publications

Combining Information Extraction, Deductive Reasoning and Machine Learning for Relation Prediction

Combining Information Extraction, Deductive Reasoning and Machine Learning for Relation Prediction

Mining the Semantic Web

Graph Kernels for RDF Data

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