Classification and retrieval through semantic kernels

Lecture Notes in Computer Science, 2005

Abstract. Improving accuracy in Information Retrieval tasks via se-mantic information is a complex problem characterized by three main aspects: the document representation model, the similarity estimation metric and the inductive algorithm. In this paper an original kernel func-tion ...

2008

We tackle the problem of statistical learning in the standard knowledge base representations for the Semantic Web which are ultimately expressed in description Logics. Specifically, in our method a kernel functions for the ALCN\ mathcal {ALCN} logic integrates with a support vector machine which enables the usage of statistical learning with reference representations. Experiments where performed in which kernel classification is applied to the tasks of resource retrieval and query answering on OWL ontologies.

2007

This work deals with the application of kernel methods to structured relational settings such as semantic knowledge bases expressed in Description Logics. Our method integrates a novel kernel function for the ALC\ mathcal {ALC} logic in a support vector machine that could be set up to work with these representations. In particular, we present experiments where our method is applied to the tasks of concept retrieval and query answering on existing ontologies.

2011

The paper focuses on the task of approximate classification of semantically annotated individual resources in ontological knowledge bases. The method is based on classification models built through kernel methods, a well-known class of effective statistical learning algorithms. Kernel functions encode a notion of similarity among elements of some input space.

International Journal of Semantic Computing, 2008

This work concerns non-parametric approaches for statistical learning applied to the standard knowledge representations languages adopted in the Semantic Web context. We present methods based on epistemic inference that are able to elicit and exploit the semantic similarity of individuals in OWL knowledge bases. Specifically, a totally semantic and language independent semi-distance function is introduced, whence also an epistemic kernel function for Semantic Web representations is derived. Both the measure and the kernel function are embedded into non-parametric statistical learning algorithms customized for coping with Semantic Web representations. Particularly, the measure is embedded into a k-Nearest Neighbor algorithm and the kernel function is embedded in a Support Vector Machine. The realized algorithms are used to perform inductive concept retrieval and query answering. An experimentation on real ontologies proves that the methods can be effectively employed for performing the target tasks and moreover that it is possible to induce new assertions that are not logically derivable.

Engineering Applications of Artificial Intelligence, 2015

Text categorization plays a crucial role in both academic and commercial platforms due to the growing demand for automatic organization of documents. Kernel-based classification algorithms such as Support Vector Machines (SVM) have become highly popular in the task of text mining. This is mainly due to their relatively high classification accuracy on several application domains as well as their ability to handle high dimensional and sparse data which is the prohibitive characteristics of textual data representation. Recently, there is an increased interest in the exploitation of background knowledge such as ontologies and corpus-based statistical knowledge in text categorization. It has been shown that, by replacing the standard kernel functions such as linear kernel with customized kernel functions which take advantage of this background knowledge, it is possible to increase the performance of SVM in the text classification domain. Based on this, we propose a novel semantic smoothing kernel for SVM. The suggested approach is based on a meaning measure, which calculates the meaningfulness of the terms in the context of classes. The documents vectors are smoothed based on these meaning values of the terms in the context of classes. Since we efficiently make use of the class information in the smoothing process, it can be considered a supervised smoothing kernel. The meaning measure is based on the Helmholtz principle from Gestalt theory and has previously been applied to several text mining applications such as document summarization and feature extraction. However, to the best of our knowledge, ours is the first study to use meaning measure in a supervised setting to build a semantic kernel for SVM. We evaluated the proposed approach by conducting a large number of experiments on well-known textual datasets and present results with respect to different experimental conditions. We compare our results with traditional kernels used in SVM such as linear kernel as well as with several corpus-based semantic kernels. Our results show that classification performance of the proposed approach outperforms other kernels.

2008

A novel family of parametric language-independent kernel functions defined for individuals within ontologies is presented. They are easily integrated with efficient statistical learning methods for inducing linear classifiers that offer an alternative way to perform classification wrt deductive reasoning. A method for adapting the parameters of the kernel to the knowledge base through stochastic optimization is also proposed.

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000

We propose to solve a text categorization task using a new metric between documents, based on a priori semantic knowledge about words. This metric can be incorporated into the definition of radial basis kernels of Support Vector Machines or directly used in a K-nearest neighbors algorithm. Both SVM and KNN are tested and compared on the 20newsgroups database. Support Vector Machines provide the best accuracy on test data.

2011

Typically, in textual document classification the documents are represented in the vector space using the “Bag of Words”(BOW) approach. Despite its ease of use, BOW representation cannot handle word synonymy and polysemy problems and does not consider semantic relatedness between words. In this paper, we overcome the shortages of the BOW approach by embedding a known WordNet-based semantic relatedness measure for pairs of words, namely Omiotis, into a semantic kernel.

Proceedings of the Ninth Conference on Computational Natural Language Learning - CONLL '05, 2005

Research on document similarity has shown that complex representations are not more accurate than the simple bag-ofwords. Term clustering, e.g. using latent semantic indexing, word co-occurrences or synonym relations using a word ontology have been shown not very effective. In particular, when to extend the similarity function external prior knowledge is used, e.g. WordNet, the retrieval system decreases its performance. The critical issues here are methods and conditions to integrate such knowledge.