Haishan Liu

Semantic data mining: A survey of ontology-based approaches

Semantic Data Mining refers to the data mining tasks that systematically incorporate domain knowledge, especially formal semantics, into the process. In the past, many research efforts have attested the benefits of incorporating domain knowledge in data mining. At the same time, the proliferation of knowledge engineering has enriched the family of domain knowledge, especially formal semantics and Semantic Web ontologies. Ontology is an explicit specification of conceptualization and a formal way to define the semantics of knowledge and data. The formal structure of ontology makes it a nature way to encode domain knowledge for the data mining use. In this survey paper, we introduce general concepts of semantic data mining. We investigate why ontology has the potential to help semantic data mining and how formal semantics in ontologies can be incorporated into the data mining process. We provide detail discussions for the advances and state of art of ontology-based approaches and an introduction of approaches that are based on other form of knowledge representations.

A Hypergraph-based Method for Discovering Semantically Associated Itemsets

In this paper, we address an interesting data mining problem of finding semantically associated item sets, i.e., items connected via indirect links. We propose a novel method for discovering semantically associated item sets based on a hyper graph representation of the database. We describe two similarity measures to compute the strength of associations between items. Specifically, we introduce the average commute time similarity,

OMIT: A Domain-Specific Knowledge Base for MicroRNA Target Prediction

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Constructing a User Preference Ontology for Anti-spam Mail Systems

, based on the random walk model on hyper graph, and the inner-product similarity,

Mining Biomedical Ontologies and Data Using RDF Hypergraphs

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Breaking the Deadlock: Simultaneously Discovering Attribute Matching and Cluster Matching with Multi-Objective Metaheuristics.

, based on the Moore-Penrose pseudoinverse of the hyper graph Laplacian matrix. Given semantically associated 2-itemsets generated by these measures, we design a hyper graph expansion method with two search strategies, namely, the clique and connected component search, to generate

Ontology-Based mining of brainwaves: a sequence similarity technique for mapping alternative features in event-related potentials (ERP) data

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Mapping ERP patterns from heterogeneous datasets: A simulation study

-item sets (

Development of Neural Electromagnetic Ontologies (NEMO): Ontology-based Tools for Representation and Integration of Event-related Brain Potentials

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Sharing and integration of cognitive neuroscience data: Metric and pattern matching across heterogeneous ERP datasets

). We show the proposed method is indeed capable of capturing semantically associated item sets through experiments performed on three datasets ranging from low to high dimensionality. The semantically associated item sets discovered in our experiment is promising to provide valuable insights on interrelationship between medical concepts and other domain specific concepts.