On Path-Centric Navigation and Search Techniques for Personal - PowerPoint PPT Presentation

On Path-Centric Navigation and Search Techniques for Personal Knowledge Stored in Topic Maps

Outline � Introduction � Preconditions and the Model � Navigation inside Topic Maps � Search based on Topic Selection 2 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Introduction Which problem do you address? 3 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Daily challenges with multiple devices, Viele Informationen, Programme und Geräte various tools and different location of data ?? DB DMS 4 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Collect and interconnect data, to ease daily work with information DB DMS 5 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

MIDMAY autonomously creates topic maps from data sources and preserves the user given structures 6 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Preconditions and the used Model What is the foundation of your approach? 7 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Topic map is designed to find the desired reference by leveraging redundancies in data sources � Global typing schema across all extractors (PSIs, PSIDs) � Association type reflects and unifies semantic of a property, type or hierarchical relation � No directionality inherent in an association � Each entry unique in the knowledge space of a user -> consistency 8 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Graph Definition � Topic map graph G is described by the pair ( V , E ). � V is finite set of vertices mapped to topics � E is a binary relation on V , representing the undirected associations between the topics. � Additionally, E explicitly contains the binary relations between topics and their types => G contains a vertex in V for every type topic. � Each edge ( v i , v j ) œ E is given a constant configurable weight w ij depending on the type of association and the search mode 9 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Navigation inside Topic Maps What do you mean with path centric? 10 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Cycling through the Graph navigate redo Term Association Selection Selection redo redo navigate Topic Selection mark 11 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Points of Entrance & Navigation Aid Type Lists 12 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Points of Entrance & Navigation Aid Hierarchy Root 13 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Topic Paths nce.ppt Example Ltd. d. Semantic Distanc per Discussed Pape Meeting E Doe oe John D Graz PPT [Filetype] [Perso [Event] [Location] [Email] [Att ttachment] son] 14 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Path Navigation � Follow paths in mind � I want to find a document, but can’t remember some fitting keywords � However, I recall that the document was sent by someone I meet in a meeting in Berlin � Let’s start the search with an item I can name: Berlin … 15 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Path Navigation Screenshots (1) 16 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Path Navigation Screenshots (2) 17 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Path Navigation Screenshots (3) 18 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Path Navigation Screenshots (4) 19 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Search based on Topic Selection How does selecting topics help us searching? 20 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Selecting topics to specify Search Query � Select two topics t a , t b and choose mode (0-3) � weighted Breadth-first Search from t a and t b , until the path with the lowest value is found � next path by removing the edge that connects both waves -> sufficient if all topics in possible paths are presented at least once 21 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Example: Show path between the topics Graz and PPT 22 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Path Maths Definitions introducing Bit Vectors � The vector B p ab indicates the presence or absence of topics < t 1 , t 2 , t 3 , ..., t k > in path p between topic t a and t b . ( B ab : topic presence for all shortest p) � � 23 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Marking multiple topics to calculate a set of relevant result topics � � Search Modes � Mode 0, the default mode that only uses the structure structure of the topic map � Mode 1, to focus the search on topics equally connected by hierarchy hierarchy Mode 2, to focus the search on equal properties properties of � marked topics Mode 3, to focus the search on equal types types of � marked topics 24 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Example: Search all employees involved in project MIDMAY which authored a PPT presentation � Think of topics related to search problem � I’m looking for a person person � .. He’s involved in project MIDMAY MIDMAY � The file type is PPT PPT � Navigate to the topics and mark them � Start query 25 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Search – Add MIDMAY Topic 26 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Search – Add PPT Topic 27 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Conclusion What’s the benefit and what are the remaining challenges? 28 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps

Already existing data can be used to offer an intuitive way to search for information � Path and set calculation provide search functionality in topic maps beyond keyword search techniques for non-technical users � Challenges Capacity of Topic Maps Engine � Enhanced UI for query, bringing the full flexibility of � path calculation to the user � Topic Maps can help to tackle the daily work with stored information 29 Jens Heider TMRA ´07 – On Path-Centric Navigation and Search Techniques Julian Schütte for Personal Knowledge Stored in Topic Maps