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

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.

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.

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) -1 the inverse path LP3=LP1&LP2: E[LP3](e, G)= E[LP1](e, G)  E[LP2](e, G)

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

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:

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)

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