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An Email and Meeting Assistant Using Graph Walks

Use graph walks to derive extended similarity measures between email and meeting entries, allowing for retrieval of relevant messages, attendees, and email aliases from a joint graph representation. Preliminary results are promising.

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An Email and Meeting Assistant Using Graph Walks

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  1. An Email and Meeting AssistantUsing Graph Walks Einat Minkov William W. Cohen CEAS-2006

  2. Documents and Links • PageRank (Brin and Page, 98), HITS (Kleinberg, 98) • Co-training (Blum and Mitchell) • Documents are not isolated objects: they are connected to other documents via hyperlinks • Document similarity/relatednessvia random graph walk

  3. Structured Documents • In structured data, documents are inter-connected via other common objects. • Email and meeting entries are examples of structured data:text + meta-data • Represent email and meetings as a joint graph • Derive extended similarity measures between graph objects using lazy graph walks. • Show me recent relevant messages to this message • What is the full name of ‘Danny’ that is mentioned in this message? Framework: Questions we can ask:

  4. Email as a Graph Chris.germany@enron.com alias Chris sent_from sent_from_email Mgermany@ch2m.com sent_to_email 1.22.00 file1 On_date sent_to has_subj_term Melissa Germany has_term work where yo I’m you

  5. Email as a Graph • A directed graph • A node carries an entity type • An edge carries a relation type • Edges are bi-directional (cyclic) • Nodes inter-connect via linked entities.

  6. Meetings • Like Email messages, Meeting entries are structured. • Share entities with Email: • Email and meetings can be naturally represented as a joint graph. TIME TEXT PERSONS

  7. The Joint Graph nodex Shared content Social network Timeline

  8. Edge Weights • Graph G : - nodes x,y,z - node types T(x), T(y), T(z) - edge labels - parameters • Edge weight x y: • Prob. Distribution: a. Pick an outgoing edge label b. Pick node y uniformly

  9. Graph Similarity Defined by lazy graph walks over k steps. Given: Stay probability: (larger values favor shorter paths) A transition matrix: Initial node distribution: Output node distribution: We use this platform to perform SEARCH of related items in the graph:a query is initial distribution Vq over nodes and a desired output type Tout

  10. Evaluation Many tasks/ applications can be phrased as search queries in this framework. Given: a meeting: text & date Retrieve:a ranked listofrelevant email-addresses (potential attendees) TASK I: Find Meeting Attendees TASK II:Find Email Aliases Given: a person’s name Retrieve: a ranked list of his/hers email-addresses

  11. Methods Corpus • Baseline: String matchingUse distance metric (JARO-Winkler) – Finds similar email-addresses to personal / project names mentioned. • 346 email files (‘Meetings’ folder) • 334 meeting entries (‘Palm’) • Both over the same time span (about 6 months) • The joint graph includes 3,680 nodes • Graph walk • 3 Steps • Uniform weights

  12. Results: Find Meeting Attendees A. All email addresses • 11-point precision-recall curve, averaged over 13 examples meeting term date B. One address per person file e-address

  13. Results: Find Email Aliases A. By first name • 14 examples (2 to 5 email aliases each) term term term term file person B. By full name term e-address

  14. Summary • A Joint representation of email and meetings: • Denser links • Augments social network information • Supports Meeting management applications • Preliminary results are promising. • Application of learning and more results for email-related tasks, available at:“Contextual Search and Name Disambiguation in Email Using Graphs”, Einat Minkov, William W. Cohen, Andrew Y. Ng in SIGIR 2006

  15. Thanks! Questions?

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