Using Semantic Web Technologies to Build a Community-Driven Knowledge Curation Platform for the Skeletal Dysplasia Domain

Groza, Tudor; Zankl, Andreas; Li, Yuan Fang; Hunter, Jane

doi:10.1007/978-3-642-25093-4_6

Cited by 9 publications

(9 citation statements)

References 17 publications

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“…We have opted for the Latent Dirichlet Allocation (LDA) topic model, which allows documents to have a mixture of topics [27]. The intuition behind LDA is that documents exhibit multiple topics; e.g., a discussion regarding Achondroplasia, a disorder of bone growth, in SKELETOME [28], will most likely include information about the possible causes of the disease such as inheritance and genetic mutation, genes and chromosomes, treatment options, medication, etc. LDA is a statistical model of document collections that tries to capture this intuition.…”

Section: Topic Modelingmentioning

confidence: 99%

Semantic and Time-Dependent Expertise Profiling Models in Community-Driven Knowledge Curation Platforms

2013

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Abstract:Online collaboration and web-based knowledge sharing have gained momentum as major components of the Web 2.0 movement. Consequently, knowledge embedded in such platforms is no longer static and continuously evolves through experts' micro-contributions. Traditional Information Retrieval and Social Network Analysis techniques take a document-centric approach to expertise modeling by creating a macro-perspective of knowledge embedded in large corpus of static documents. However, as knowledge in collaboration platforms changes dynamically, the traditional macro-perspective is insufficient for tracking the evolution of knowledge and expertise. Hence, Expertise Profiling is presented with major challenges in the context of dynamic and evolving knowledge. In our previous study, we proposed a comprehensive, domain-independent model for expertise profiling in the context of evolving knowledge. In this paper, we incorporate Language Modeling into our methodology to enhance the accuracy of resulting profiles. Evaluation results indicate a significant improvement in the accuracy of profiles generated by this approach. In addition, we present our profile visualization tool, Profile Explorer, which serves as a paradigm for exploring and analyzing time-dependent expertise profiles in knowledge-bases where content evolves overtime. Profile Explorer facilitates comparative analysis of evolving expertise, independent of the domain and the methodology used in creating profiles.

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Section: Topic Modelingmentioning

confidence: 99%

Semantic and Time-Dependent Expertise Profiling Models in Community-Driven Knowledge Curation Platforms

2013

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“…The context of our research is provided by the SKELETOME project, 16 which aims to create a community-driven knowledge curation platform for the skeletal dysplasia domain. a To date, we have developed an ontology, the Bone Dysplasia Ontology, 17 capable of capturing associations between skeletal dysplasias, gene mutations, and phenotypic descriptions, the latter grounded in HPO concepts.…”

Section: Introductionmentioning

confidence: 99%

Decomposing Phenotype Descriptions for the Human Skeletal Phenome

Groza

Hunter

Zankl

2013

Biomed Inform Insights

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Over the course of the last few years there has been a significant amount of research performed on ontology-based formalization of phenotype descriptions. The intrinsic value and knowledge captured within such descriptions can only be expressed by taking advantage of their inner structure that implicitly combines qualities and anatomical entities. We present a meta-model (the Phenotype Fragment Ontology) and a processing pipeline that enable together the automatic decomposition and conceptualization of phenotype descriptions for the human skeletal phenome. We use this approach to showcase the usefulness of the generic concept of phenotype decomposition by performing an experimental study on all skeletal phenotype concepts defined in the Human Phenotype Ontology.

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“…The context of our work is provided by the SKELETOME project that develops a community-driven knowledge curation platform for the bone dysplasia domain [1]. The underlying foundation of the platform is a two-phase knowledge engineering cycle which enables: (1) semantic annotation of patient cases -connecting domain knowledge to real-world cases; and (2) collaborative diagnosis, collaborative knowledge curation and evolution -evolving the domain knowledge, based on real-world cases.…”

Section: Introductionmentioning

confidence: 99%

Semantic Similarity-Driven Decision Support in the Skeletal Dysplasia Domain

Paul

Groza

Zankl

et al. 2012

The Semantic Web – ISWC 2012

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Abstract. Biomedical ontologies have become a mainstream topic in medical research. They represent important sources of evolved knowledge that may be automatically integrated in decision support methods. Grounding clinical and radiographic findings in concepts defined by a biomedical ontology, e.g., the Human Phenotype Ontology, enables us to compute semantic similarity between them. In this paper, we focus on using such similarity measures to predict disorders on undiagnosed patient cases in the bone dysplasia domain. Different methods for computing the semantic similarity have been implemented. All methods have been evaluated based on their support in achieving a higher prediction accuracy. The outcome of this research enables us to understand the feasibility of developing decision support methods based on ontology-driven semantic similarity in the skeletal dysplasia domain.

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Using Semantic Web Technologies to Build a Community-Driven Knowledge Curation Platform for the Skeletal Dysplasia Domain

Cited by 9 publications

References 17 publications

Semantic and Time-Dependent Expertise Profiling Models in Community-Driven Knowledge Curation Platforms

Semantic and Time-Dependent Expertise Profiling Models in Community-Driven Knowledge Curation Platforms

Decomposing Phenotype Descriptions for the Human Skeletal Phenome

Semantic Similarity-Driven Decision Support in the Skeletal Dysplasia Domain

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