Exploring neighboring entities via semantic links Liang Zheng

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

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), 1-136.

Example http://dbpedia.org/resource/Will_Smith RDF Graph Entity Graph

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.

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

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

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

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

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

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

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.

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’.

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

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

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

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 .

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