1. Semantic Navigation over Linked Data Using the Link Pattern Space
Liang Zheng
11.9

2. Background
Challenges in browsing linked data are rich: large numbers of data instances (entities), high entropy and diversity in links between data instances (entities), often makes it hard for users to understand and find the particular bits of the data that might be of interest.
We introduce a novel navigating paradigm based on the link pattern space, that allows users to navigate the linked data by starting from a set of instances(entities) through meaningful link patterns.

3. Background
We formally define link patterns and analyze their desirable properties.
We design methods to efficiently detect link patterns based on multiple measures of “interestingness.”
We implement an approach to find a group of link patterns for refocusing and refining the detect link patterns.

4. Preliminaries Link Pattern (LP) Path (on a labeled entity graph)
Properties of LP
Atomicity/Minimality (Non-explicitly rdfs:subPropertyOf )
E[LP](e, G): the set of entities selected by the navigational link pattern starting from e on the entity graph G.
LP1。LP2: Join-operator ; path1。path2
(LP) k (LP)2=LP。LP
(LP) the inverse path
LP3=LP1&LP2: E[LP3](e, G)= E[LP1](e, G)  E[LP2](e, G)

5. Preliminaries Properties of LP Hierarchical link pattern space
All Link Patterns
LP1
LP2
author
first Author
second Author
Virtual Node(Link pattern)
LP3
Explicit inclusion relation
Underlying semantic relationship
LP3=LP1&LP2

6. 1. Automatically capture link patterns
Frequency
High-entropy (rareness)
Pointwise Mutual Information (PMI)
avgPMI(LP) = ∑ j=1 →n PMI (e, ej ) / n where
PMI (e, ej)= log( p(e&ej)/p(e)*p(ej)) p(e)= |hits(e)| / |Z| e ej
Path
LP:

7. 2. Finding a group of link patterns from the hierarchical link pattern space
Among all the link patterns, we aim to find up to K ones that are as meaningful as possible while covering the most of linked entities.
EBMC
GAP (General Assignment Problem)

8. Thanks!