AutoFocus, an Open Source Facet-Driven Enterprise Search Solution

Transcription

1 AutoFocus, an Open Source Facet-Driven Enterprise Search Solution ISKO UK Event, November 5, 2007 RANGANATHAN REVISITED: FACETS FOR THE FUTURE presentation by Jeroen Wester, CTO

2 Aduna key facts Open source semantic technology users worldwide 45 man-years in software development Semantic Web standard RDF Components for metadata-based data integration and enterprise search solutions

3 Clients and users

4 Enterprise search Aduna AutoFocus Server scans enterprise information sources, like files systems and databases for data and metadata. It helps users to get their question right and to get precise answers in their browser. Aduna AutoFocus scans enterprise sources and connects to Aduna AutoFocus Server. This powerful desktop application visualises search results in so-called Cluster Maps.

5 Cross-silo search and navigation presentation search navigate present metadata storage and querying metadata silos with information

6 Metadata and semantics Metadata closely related to objects Describes object attributes Examples: file name, author name, title Document194 hasauthor Bob Bater Ontological metadata Organises terms by describing relations Examples: taxonomy, thesaurus, vocabulary AAT, Taxonomische thesaurus Naturalis

7 Beware of the acronym slide! SOAP message exchange between software RDF structure for metadata RDF Schema vocabulary for metadata SKOS structure for thesauri XML structure for documents OWL richer vocabulary for metadata

8 Search Problems with full-text search: Vocabulary problem Large or empty result sets Vague information needs: people are bad in describing relevant information, but good in recognizing it! Desktop & enterprise search: No hypertext = no PageRank Less redundancy Distinguish relevant from ultra-relevant

9 AutoFocus' approach Metadata-centric UI for metadata exploration & query formulation Information visualisation for search result exploration Semantic backbone integrating structured information from a variety of sources, like file systems, web pages, mail boxes, enterprise sources, and more

10 AutoFocus demonstration

11 Use Case 1: Simple Search

12 This is the initial screen, all components are still empty. We have pre-defined four information sources to search with AutoFocus: two file folders, a web site and a mail folder.

13 We enter a full-text query for desktop search. We select two sources for our search.

14 AutoFocus finds 610 documents containing the phrase desktop search. The entire result set is displayed as a sphere.

15 Clicking on the sphere lists all hits in the table below.

16 We enter a new keyword query...

17 We now see three clusters. The middle cluster holds the intersection of both result sets. The outer clusters hold results that only match one query.

18 The little yellow spheres represent individual documents (only shown for small clusters).

19 Besides keyword search, AutoFocus features a number of other search capabilities...

20 Let's start with a regular search for autofocus...

21 The list at the left shows all search facilities, each corresponding with a particular metadata facet.

22 For example, the Type facet lists all occurring document types. We will select all Presentations...

23 The Cluster Map tells us that there are seven presentations containing the word autofocus.

24 Clicking on this Cluster tells us what those seven presentations are.

25 Use the Date & Time facet to find documents in a particular time interval, e.g. all files older than one year.

26 The Cluster Map now shows the overlap between three result sets. Coloured edges in the graph connect documents matching the same query combination.

27 In the centre we see the five presentations older than one year that contain the word autofocus.

28 Use Case II: Getting Help

29 Let's start with a search for the word metadata...

30 The result set is fairly large (566 documents), so we can use some help in narrowing down.

31 The Keyword facet suggests keywords extracted from the selected documents.

32 The People facet shows (among other things) all authors occurring in the document metadata. Highlighted authors mean that those authors appear in the current set of 566 documents.

33 Clearly, this facet could benefit from a more semantical modeling of authors: it contains various duplicates.