1. Exploring neighboring entities via semantic links
Liang Zheng

2. The data sets published on the Web as Linked Data
Scenario
Browsing Linked Data
The Web of Data (a global data space) forms a giant global graph  (an RDF Graph, data model)
The data sets published on the Web as Linked Data

3. Scenario Surfing the Web of Data & the RDF Graph
The basic means to access and navigate the graph is to dereference HTTP URIs into RDF descriptions and to traverse RDF links discovered within the retrieved data[1].
[1]Heath, T., & Bizer, C. (2011). Linked data: Evolving the web into a global data space. Synthesis lectures on the semantic web: theory and technology, 1(1),

4. Example
RDF Graph
Entity Graph

5. Challenge The original idea
How to provide an assistance to help users access and navigate the entity graph.
The original idea
Collins, A. M. and Loftus, E. F present a spreading-activation theory of human semantic processing in 1975.

6. Spreading-activation
One node becomes active, more and more of its neighbors become active.
Applications
Associative retrieval in IR.
Information diffusion in social networks
The other kinds of activation

7. An Entity Graph: a user focused entity and its neighboring entities

8. An RDF Graph: a user focused entity and its neighboring entities

9. Task Given a focused entity e and its neighboring entities NE Our Task
is to explore neighboring entities NE.
Why choose this method?
Support of theory
当前Sview系统的数据管理能力的制约

10. The analysis of the dataset in LOD
object link graph (OLG). Ge.
图的平均度为3.44

11. An RDF Graph: a user focused entity and its neighboring entities
SemanticLink2
SemanticLink1
SL1=(knows, seeAlso)
SL2=(knows)

12. we propose a tool for exploring neighboring entities via semantic links by user-guided manipulation.
In addition, there are a lot of links between current entity and neighboring entities , we rank all semantic links based on multiple measures.

13. Semantic link operations
Suppose SL and SL’ be the semantic links starting from e and a mapping function  : SL → La, assigning to each semantic link as its label.
SL  SL’: we return an entity collection M= EndNode(SL)  EndNode(SL’).
SL  SL’ : if StartNode(SL)=StartNode(SL’)  EndNode(SL) = EndNode(SL’), we return a new atomic link SL’’= SL SL’.

14. CHI (1)-Basic birthPlace (3) deathPlace (3) deathPlace residence (1)
字典序

15. CHI (2)- Advanced birthPlace (3) birthPlace (3) deathPlace (3)
 NaviList
 NaviGraph
birthPlace (3)
deathPlace (3)
deathPlace residence (1)
residence (2)
More …
Expand your
birthPlace (3)
Caption (2)
Country(1)
Timezone (2)
More …
Expand

16. CHI (2)
Loading more data
Setting up width
 NaviList
 NaviGraph

17. birthPlace deathPlace Caption (3) Country
Expand your
Caption
Country
(3)
birthPlace
deathPlace
We show the top-10 links on the NaviGraph,
you can view the full result in the NaviList .

18. Thanks! Q&A