Semantic Graph Mining for Biomedical Network Analysis: A Case Study in Traditional Chinese Medicine - PowerPoint PPT Presentation


PPT – Semantic Graph Mining for Biomedical Network Analysis: A Case Study in Traditional Chinese Medicine PowerPoint presentation | free to download - id: 690910-ZTI1O


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation

Semantic Graph Mining for Biomedical Network Analysis: A Case Study in Traditional Chinese Medicine


Semantic Graph Mining for Biomedical Network Analysis: A Case Study in Traditional Chinese Medicine Tong Yu HCLS 2008 – PowerPoint PPT presentation

Number of Views:332
Avg rating:3.0/5.0
Slides: 22
Provided by: w3Orgwikii9
Learn more at:


Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Semantic Graph Mining for Biomedical Network Analysis: A Case Study in Traditional Chinese Medicine

Semantic Graph Mining for Biomedical Network
Analysis A Case Study in Traditional Chinese

Tong Yu HCLS 2008
  • The TCM Semantic Web
  • Semantic Query and Search Portal
  • Semantic Graph Mining Methods
  • TCM Use Cases
  • Key Benefits of Using Semantic Web Technologies
    for TCM domain

Tcm Informatics A cross-cultural and
interdisciplinary endeavor
  • TCM Informatics aims at the computerization of
    TCM information and knowledge to provide
    intelligent resources for clinical
    decision-making, drug discovery, and education.
  • TCM Informatics is essentially an
    interdisciplinary endeavor involving Chinese
    Culture, Healthcare and Life Sciences, and
    Information Technology
  • The cross-cultural and interdisciplinary nature
    of TCM Informatics requires data from
    interrelated domains to be connected and shared.

Approach Overview
  • We intend to connect the knowledge systems of TCM
    and biomedicine to facilitate cross-cultural
    information retrieval and data analysis.
  • Engineer an ontology for TCM domain
  • Making associations between TCM Ontology and
    Western Medicine Ontology
  • The Semantic Web integrates structured data from
    the territories of TCM (Left) and Western
    Medicine (Right)
  • Semantic Mediator maps relational schemas into
    domain ontology by defining Semantic Views.
  • Query-Rewriting Engine translates a Sparql query
    into a series of SQL queries based on mapping
  • supports a variety of Web-based applications.

The Architecture of The TCM Semantic Web
  • The TCM Semantic Web portal

A Semantic Graph Model for TCM Domain
  • Semantic Graph Model can connect data from
    different TCM data sources while preserving the
    provenance of data. We use TCM Ontology to
    integrate data about EMR, Formulae Drugs,
    Diseases, and to connect the TCM data with
    orthodoxy medicine data e.g. UMLS, Gene Ontology.

Interactive Mining of TCM Knowledge
The Spora System perform interactive knowledge
discovery experiments on the Semantic
Web. Semantic Graph Mining Implement the semantic
graph mining algorithms (importance calculation,
frequent pattern discovery, clustering, etc. )
as generic operators that work on top of the
Semantic Web layer and query semantic graph
models in Sparql. KDD Experiments Users can
create an Experiment by specifying a knowledge
discovery process as a tree of operators with
customizable properties, and then execute the
process and review the results rendered as
interactive tables, histograms, etc.
Semantic Graph Resource Importance
  • the in-degree centrality CI of a resource is
    measured by the weighted sum of statements with
    the resource as object, and the out-degree
    centrality is measured by the weighted sum of
    statements with the resource as subject.

Semantic Graph Resource Importance
  • The Closeness Centrality of a resource r is
    defined as the inverse of the sum of the distance
    from r to all other resources.

Semantic Graph Resource Importance
  • The Betweenness Centrality of a resource r is
    defined as the ratio of shortest paths across the
    resource in the graph.

Semantic Associations
  • pathAssociated
  • ltthe prescription1 prescribes TCM Formula FGDgt
    AND ltFormula FGD cotains the Herb Glycyrrhizaegt,
  • So that
  • ltprescription1 Glycyrrhizae are
  • joinAssociated
  • ltthe prescription1 prescribes a Formula FGDgt to
    lttreat the TCM Syndrome KYDgt,
  • So that
  • ltFGD KYD are joinAssociatedgt with the join
    point as the prescription1.
  • classPathAssociated
  • ltthe Glycyrrhizae is of type HerbgtAND ltthe
    Atractylodis is of type DruggtAND ltHerb is a
    subclass of Druggt,
  • So that
  • ltGlycyrrhizae and Atractylodis are cpAssociatedgt.

Frequent Semantic Subgraph
Frequent Semantic Subgraph
Pattern Interpretation
  • Discovered patterns can be annotated with domain
    knowledge based on semantic associations of
    concepts, and visualized as a rich graph to
    facilitate human interpretation. Here semantic
    search is used to discover latent semantic
    associations of concepts.

Pattern Interpretation
  • This example pattern including four herbs and two
    drug efficacies, is interpreted by the fact that
    the formula FGD composed of these herbs has these
    two drug efficacies.

The Semantic Network of herb-drug interactions
  • The TCM domain involves a complex network of
  • We use Traditional Chinese Medicine (TCM)
    information resources to map an extensive view of
    Herb-Drug Interactions.
  • This network is mapped through semantic
    integration of legacy relational databases in
    Traditional Chinese Medicine (TCM) domain.
  • This network is used for domain experts to rank
    topologically-important herbs/drugs, to retrieve
    semantic associations between drugs, and to
    discern interesting patterns such as frequent
    sub-graphs and community structures.

The Semantic Network of herb-drug interactions
The process
  • Data Modeling Represent domain knowledge and
    facts in named semantic graphs.
  • Data Transformation Integration translate
    structured or semi-structured data into semantic
    web languages.
  • Entity Disambiguation 1344 CVD-associated herbs
    are identified.
  • Interaction Identification The nature and
    frequency of interactions between all pairs of
    drugs are discovered through semantic
  • A semantic network is generated by inserting a
    statement for every interaction to generate a
    global semantic graph of diverse drug
    interactions .
  • Clustering Drug communities are discovered
    through semantic graph clustering.

Global network of frequent herb-drug
interactions,with drugs represented by nodes
with size/font proportional to degree,
interactions represented by edges, and drug
communities represented by colors.
Key Benefits of Using Semantic Web Technology
  • Exposing of legacy data through a semantic layer
    so that it can be more easily reused and
  • Linking data across database boundaries so as to
    enabling more intuitive query, search, and
    navigation without the awareness of the
  • The ontology serves as the control vocabulary to
    make semantic suggestions such as synonyms,
    related concepts to facilitate query and search.
  • Reasoning capability such as sub-classing,
    transitive property can then be implemented at
    the semantic layer to increase the query
    expressiveness so as to retrieve more complete
  • Allows for more advanced data analysis and
    integrative knowledge discovery based on the huge
    web of data.  

  • We took the first systematic approach to leverage
    the progress of Biomedical Informatics to address
    the modernization of TCM.
  • Domain experts evaluate the platforms major
    technical features as original and productive in
    Drug Safety and Efficacy analysis.
  • This case study demonstrates the Semantic Webs
    advantages in representation, integration, and
    discovery of knowledge with complex domain
  • Contributes to the Preservation and Modernization
    of TCM as intangible cultural heritage.