Magnet & /facet Zheng Liang 2012.11

Contents Magnet: Supporting Navigation in Semistructured Data Environments (SIGMOD2005) /facet: A Browser for Heterogeneous Semantic Web Repositories (IWSC2006)

Magnet: Supporting Navigation in Semistructured Data Environments Structure of paper in total 10 pages Introduction & Related Work (2 pages) Interface Walkthrough ( 1 page) Navigation Engine ( 1 page ) Vector Space Model ( 1.5 page) Evaluation ( 2 pages) Conclusion & References (1.5 page) Figures (1 page)

Magnet is a tool that offers users helpful navigation and refinement options for seeking information in these semistructured repositories. provide a set of natural, general-purpose refinement tactics.

Related Work Scatter / Gather: Create topical clusters and allow user to create smaller collections within those clusters NaviQue: Single visualization based on querying and document similarity (clusters applicable) Flamenco Project: Query refinement by selecting metadata (expertise necessary) For semi-structured (Focus on querying instead of Search): Lore: Dataguides to retrieve structural schema summaries to support query formulation by filling constraints. Trigoni: Allows user to combine a set of templates to describe the semi-structured graph

Related Work (Cont’d) XML Documents: reform the XML tree mentally to help search engine. Egnor tries iterative refinement process starting from a keyword. RDF: represents information by a directed graph in which information objects are nodes and attributes and relations are represented by property links. Another way of representing information is using tree structures as in the case of XML documents.

By specifying keyword searching to arriving at large collection Interface By specifying keyword searching to arriving at large collection

Navigation pane Add/Remove constraints Advisors Similar Items: that have the same overall content or share a common property with the collection Refine Collections: refine search by one of the metadata attributes Modify: go to related collections and negate constraint Refinement History: undo previous refinements

Navigation Engine Principle: Each view must present information to help the user make their next navigational decision. Magnet aims to support the search process and overall strategies by implementing recommenders of single step refinement tactics via advisors fed by one or more analysts.

Navigation Advisors Each advisor presents a particular type of navigation step. They work both with documents and collections. Related Items: viewed item to a collection of similar items Sharing a property (attribute in common or shared) Similar by Content: similar in structure and similar in textual Similar by Visit: That were visited last time the user left the currently viewed item Contrary Constraints: inverted constraint, overview of other related

Navigation Advisors (Cont’d) Refine Collections: Suggest navigation based on identified properties and values in common to some but not all items in the collection (filtering the collection) History Previous: that have seen most recently Refinement: that are in refinement trail. Principle: Advisors use the analyst provided information retrieval weights associated with each suggestion to select navigation suggestions.

Vector Space Model Maps text documents to vectors with a coordinate for each word in the corpus, whose value is set according to the number of occurrences of that word in the document Improvement possible by removing common words suffixes and normalization

Evaluation Flexibility to work with different data sources Browsing flexibility Interface through a user study. Much of the study is qualitative in nature.

/facet: A Browser for Heterogeneous Semantic Web Repositories Structure of paper in total 14 pages Introduction (1 pages) Example Scenario( 1 page) Requirements for Multi-type Facet Browsing (2.5page ) Functional Design for Multi-type Facet Browsing(4.5 page) Discussion and Related Work ( 2 pages) Conclusion & References (2 page) Figures (1 page)

/facet is a generic browser for heterogeneous semantic web repositories. Select and navigate facets of resources of any type.  Make selections based on properties of other, semantically related, types. Semantic autocompletion in three flavors.  1) search on all instances, helping to select the right type, 2) search within a single facet, helping to move in complex facet hierarchies, 3) search across all active facets, showing the user the different uses of a keyword in different facets.

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