--Presented by Tianyi Zhang Building Community Wikipedias: A Machine-Human Partnership Approach

Motivation Wikipedia portal involves deeper and broader in our lives. Existing Approaches Semi-Automatic Methods(such as Libra, Citeseer) to extract and integrate data from a multitude of data sources. Deploy an initial portal in wiki format, then invite community members to revise and add material.(such as Wikipedia)

The Ultimate goal of this paper

Problems during the path to end How to build an initial wiki portal? How to model and implement the structure database G? What should be the view language? How to export the structured data of the views into wiki pages? How to manage user contributions to the portal? What is it that U is conceptually allowed to edit? How to efficiently infer such edits based on what U has done to a wiki page W? User can edit schema, not just the data. User’s edit limited to this wiki interface is usually ambiguous.

Contribution of this paper Introduce a novel hybrid approach which integrates user’s insight and machines input. Provide solution to modeling the underlying structured database, and exporting these views in wiki pages. Employ an efficient solution to process user edits from wiki page to underlying databases. Empirical results over a real-world implementation to demonstrate the promise of the approach

Architecture

Problems during the path to end How to build an initial wiki portal? How to model and implement the structure database G?

Has to be intuitive… The author define schema Gs of G to consist of a set of entity types E1,…En and a set of relation types R1,…Rn The author defines the data Gd of G to be a temporal ER data graph. A set of nodes that specify entity instances A set of edges that specify relation instances

Example

Storing G using RDBMS Create several tables to store definitions of entity types, relationship types and their attributes (such as person has attributes name, title, and organization). Create table Entity_ID(id, etype) and Relationship_ID(id, rtype, eid1, eid2) A_M and A_U are two tables which are to keep track of all values from users and machines. Human-first policy

Example

Problems during the path to end How to build an initial wiki portal? How to model and implement the structure database G? What should be the view language?

View Language Requirements Every wiki page is to describe one entity or relationship Materialize quickly Mortify view quickly

A path-based view language Data path and schema path Data path-start from one entity to another entity, retains only certain attributes for each node/edge along the path Schema path

Example

A path-based ER view

Example

Creating Views over ER Graph Wikify all entity and relationship Specify a default view schema Ve that specifies a neighborhood of instances of E

Problems during the path to end How to build an initial wiki portal? How to model and implement the structure database G? What should be the view language? How to export the structured data of the views into wiki pages?

Converting Views to Wiki Pages The straightforward solution is to convert Vd into a set of natural-language sentences. But, it is difficult to extract and update structured data.

Problems during the path to end How to build an initial wiki portal? How to model and implement the structure database G? What should be the view language? How to export the structured data of the views into wiki pages? How to manage user contributions to the portal? What is it that U is conceptually allowed to edit?

Problems during the path to end How to build an initial wiki portal? How to model and implement the structure database G? What should be the view language? How to export the structured data of the views into wiki pages? How to manage user contributions to the portal? What is it that U is conceptually allowed to edit? How to efficiently infer such edits based on what U has done to a wiki page W? User can edit schema, not just the data. User’s edit limited to this wiki interface is usually ambiguous.

Infer & Execute Structured Edits

Example

Mapping User Edits into Sequence of Actions

Empirical Evaluation

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