 Copyright 2009 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute Extracting and Utilizing Social Networks from Log Files of Shared Workspaces Peyman Nasirifard, Vassilios Peristeras, Conor Hayes and Stefan Decker 10th IFIP Working Conference on VIRTUAL ENTERPRISES Thessaloniki, Greece, 7-9 October 2009

Digital Enterprise Research Institute Outline Introduction and Problem Definition Object-centric social network for extracting expertise User-centric social network for calculating the coperation index Prototypes  Expert Finder  Holmes Evaluation Conclusion Q and A

Digital Enterprise Research Institute Introduction and Problem Definition Online Shared workspaces provide various services for online collaboration  BSCW, SharePoint Difficult to find people with appropriate expertise in intra- and inter-organizations settings  People do not update their profiles regularly Difficult to spot „who works with whom“ or „who the senior within a community is“  People do not maintain their social networks frequently

Digital Enterprise Research Institute Problem Definition To find people with specific expertise To understand who works with whom and to what extend

Digital Enterprise Research Institute Our approach We use: Log files from CWEs Social Network Analysis Semantic technologies (RDF) to represent the extracted Social Network

Digital Enterprise Research Institute Social Network Analyis Social Network Analysis has a lot of potential Overt and Latent social networks exist among professionals Online social networks can be divided into two main types  Object-centric (e.g., based on videos, music)  User-centric We use both types in our work  We use object-centric SN for extracing expertise  We use user-centric SN for calculating cooperation index – Cooperation index: an index that determines how close two people work together

Digital Enterprise Research Institute Log files Log files of shared workspaces contain rich information and can be further analyzed A log record contains at minimum Subject (e.g., user), Object (e.g., document) and Action/Verb (e.g., read, revise)  Person with ID 123 revised the document with ID 456 We use these three elements to generate RDF triples for processing

Digital Enterprise Research Institute Object-centric Social Networks for extracing expertise

Digital Enterprise Research Institute Finding Experts First step: Key-phrase Extraction  Documents are analysed based on NLP techniques to identify phrases that occur frequently Second step: Log File Analysis  To identify the documents a user interacts with and how Third step: Assigning Expertise  A user is expert in topic X, if s/he created or revised a document that contains topic X.  A user is familiar with topic Y, if s/he just read a document that contains topic Y.

Digital Enterprise Research Institute Overall Approach

Digital Enterprise Research Institute User-centric SN for calculating cooperation index

Digital Enterprise Research Institute From Object-centric to User-centric Action Relationship

Digital Enterprise Research Institute Assigning weights to social networks First step: Build user-centric social network  Previous slide  Depth is also considered (e.g., Depth one means just one document connects two persons) Second step: Assign weights to relationships  User-defined weights with default values (e.g. Read-Read is low-weighted relationship, create-create high-weighted) Third step: Calculate cooperation index  Sum up the weights

Digital Enterprise Research Institute Overall Approach

Digital Enterprise Research Institute Prototypes Expert Finder  Holmes (Cooperation Index calculator)   The prototypes are SOA-based  The prototypes use the BSCW shared workspace  The prototypes use log files of BSCW and in particular the Ecospace project in the period of three years – Around 183 users extracted from log file and some thousands of events  Expert Finder uses around 50 deliverables of Ecospace project

Digital Enterprise Research Institute Snapshot: Expert Finder

Digital Enterprise Research Institute Snapshot: Holmes

Digital Enterprise Research Institute Evaluation with 12 participants We asked people to take a look at their cooperation indices  All participants confirmed that the presented results were relevant to them  Currently, we considered four main document events (i.e., Create, Revise, Delete, and Read) and only relationships at a depth of one. These events can be simply extended to cover more document events as well as deeper depths. – Combining events and assigning weights to them can bring overhead for users.  In a more complex model for calculating Cooperation Indices, different weights can be posed to documents based on their importance for the collaboration process.

Digital Enterprise Research Institute Evaluation with 12 participants IssueSolution Meaningless expertiseThe confidence values (provided by NLP package) were used as a threshold to identify the phrases that have a higher probability of being a meaningful key- phrase. The key phrases were filtered accordingly. Organization expertise profileAn expertise profile may be built for an organization by unifying the expertise of all members of that organization. Similar phrasesSome phrases were conceptually the same, but reported several times. One partial solution to this problem could be using WordNet to infer the semantics of the terms and merge relevant terms. Irrelevant expertiseVersion history of the shared workspace may be utilized to infer the exact contribution of a user (e.g. by using diff)

Digital Enterprise Research Institute Tools and technology overview Social Network Analysis Log files from CWEs NLP techniques for Phrase Extraction RDF for representing object-centric and user-centric Social Networks Web Services for exposing functionalities

Digital Enterprise Research Institute Conclusion and Future Work We presented our approach for extracting expertise from online shared workspaces We also presented our approach for calculating an index that determines how close two people worked together in the past Addressing the points (and shortages) mentioned in the evaluation is one of our future directions Using temporal aspects of log file is another future directions  Calculating cooperation index in a period of time

Digital Enterprise Research Institute Thank You! Q and A