2006 05 25 coi semdis

Semantic Analytics on Social Networks: Experiences in Addressing the Problem of Conflict of Interest Detection: 

Semantic Analytics on Social Networks: Experiences in Addressing the Problem of Conflict of Interest Detection World Wide Web 2006 Conference May 23-27, Edinburgh, Scotland, UK This work is funded by NSF-ITR-IDM Award#0325464 titled '‘SemDIS: Discovering Complex Relationships in the Semantic Web’ and partially by ARDA Boanerges Aleman-Meza1, Meenakshi Nagarajan1, Cartic Ramakrishnan1, Li Ding2, Pranam Kolari2, Amit P. Sheth1, I. Budak Arpinar1, Anupam Joshi2, Tim Finin2 1LSDIS lab Computer Science University of Georgia, USA 2Department of Computer Science and Electrical Engineering2 University of Maryland, Baltimore County, USA

Outline: 

Outline Application scenario: Conflict of Interest Dataset: FOAF Social Networks + DBLP Collaborative Network Describe experiences on building this type of Semantic Web Application

Conflict of Interest (COI): 

Conflict of Interest (COI) Situation(s) that may bias a decision Why it is important to detect COI? for transparency in circumstances such as contract allocation, IPOs, corporate law, and peer-review of scientific research papers or proposals How to detect Conflict of Interest? connecting the dots

Scenario for COI Detection: 

Scenario for COI Detection Peer-Review: assignment of papers with the least potential COI Our scenario is restricted to detecting COI only (not paper assignment) Current conference management systems: Program Committee declares possible COI Automatic detection by (syntactic) matching of email or names, but it fails in some cases i.e., Halaschek  Halaschek-Wiener

Conflict of Interest: 

Conflict of Interest Verma Sheth Miller Aleman-M. Thomas Arpinar Should Arpinar review Verma’s paper?

Social Networks: 

Social Networks Facilitate use case for detection of COI But, data is typically not openly available Example: LinkedIn.com for IT professionals Our Pick: public, real-world data FOAF, Friend of a Friend DBLP bibliography underlying collaboration network Covering traditional and semantic web data

Our Experiences: Multi-step Process: 

Our Experiences: Multi-step Process Building Semantic Web Applications involves a multi-step process consisting of: Obtaining high-quality data Data preparation Metadata and ontology representation Querying / inference techniques Visualization Evaluation

Our Experiences: Multi-step Process: 

Our Experiences: Multi-step Process Building Semantic Web Applications requires: Obtaining high-quality data DBLP, FOAF data

FOAF – Friend of a Friend: 

FOAF – Friend of a Friend Representative of Semantic Web data Our FOAF dataset was collected using Swoogle (swoogle.umbc.edu) Started from 207K Person entities (49K files) After some data cleaning: 66K person entities After additional filtering, total number of Person entities used: 21K i.e., keep all ‘edu/ac’

DBLP ( ): 

DBLP ( ) Bibliography database of CS publications Representative of (semi-)structured data We focused on 38K (out of over 400K authors) authors in Semantic Web area arguably more likely to have a FOAF profile DBLP has an underlying collaboration network co-authorship relationships

Combined Dataset of FOAF+DBLP: 

Combined Dataset of FOAF+DBLP 37K people from DBLP 21K people from FOAF 300K relationships between entities

Our Experiences: Multi-step Process: 

Our Experiences: Multi-step Process Building Semantic Web Applications requires: Data preparation Our goal: Merging person entities that appear both in DBLP and FOAF

Person Entities from two Sources: 

Goal: harness the value of relationships across both datasets Requires merging/fusing of entities Person Entities from two Sources

Merging Person Entities: 

Merging Person Entities We adapted a recent method for entity reconciliation - Dong et al. SIGMOD 2005 Relationships between entities are used for disambiguation Presupposition: some coauthors also appear listed as (foaf) friends With specific relationship weights Propagation of disambiguation results

Syntactic matches: 

DBLP Researcher Amit P. Sheth UGA Marek Rusinkiewicz Steefen Staab John Miller http://www.informatik.uni-trier.de/~ley /db/indices/a-tree/s/Sheth:Amit_P=.html Dblp homepage http://lsdis.cs.uga.edu/~amit/ coauthors homepage label FOAF Person Carole Goble Ramesh Jain John A. Miller Amit Sheth Professor 9c1dfd993ad7d1852e80ef8c87fac30e10776c0c http://www.semagix.com http://lsdis.cs.uga.edu http://lsdis.cs.uga.edu/~amit affiliation friends Workplace homepage label title homepage Syntactic matches mbox_shasum

… with Attribute Weights: 

DBLP Researcher Amit P. Sheth UGA Marek Rusinkiewicz Steefen Staab John Miller http://www.informatik.uni-trier.de/~ley /db/indices/a-tree/s/Sheth:Amit_P=.html Dblp homepage http://lsdis.cs.uga.edu/~amit/ coauthors homepage label FOAF Person Carole Goble Ramesh Jain John A. Miller Amit Sheth Professor 9c1dfd993ad7d1852e80ef8c87fac30e10776c0c http://www.semagix.com http://lsdis.cs.uga.edu http://lsdis.cs.uga.edu/~amit affiliation friends Workplace homepage label title homepage … with Attribute Weights mbox_shasum The uniqueness property of the Mail box and homepage values give those attributes more weight

Relationships with other Entities: 

DBLP Researcher Amit P. Sheth UGA Marek Rusinkiewicz Steefen Staab John Miller http://www.informatik.uni-trier.de/~ley /db/indices/a-tree/s/Sheth:Amit_P=.html Dblp homepage http://lsdis.cs.uga.edu/~amit/ coauthors homepage label FOAF Person Carole Goble Ramesh Jain John A. Miller Amit Sheth Professor 9c1dfd993ad7d1852e80ef8c87fac30e10776c0c http://www.semagix.com http://lsdis.cs.uga.edu http://lsdis.cs.uga.edu/~amit affiliation friends Workplace homepage label title homepage Relationships with other Entities mbox_shasum A coauthor who is also listed as a friend

Propagating Disambiguation Decisions: 

DBLP Researcher Marek Rusinkiewicz Steefen Staab John Miller coauthors FOAF Person Carole Goble Ramesh Jain John A. Miller friends Propagating Disambiguation Decisions If John Miller and John A. Miller are found to be the same entity, there is more support for reconciliation of the entities Amit P. Sheth and Amit Sheth based on the presupposition that some coauthors an also be listed as (foaf) friends

Results of Disambiguation Process: 

Results of Disambiguation Process Number of entity pairs compared: 42,433 Number of reconciled entity pairs: 633 (a sameAs relationship was established) 49 205 379 DBLP 38,015 Person entities 21,307 Person entities FOAF

Our Experiences: Multi-step Process: 

Our Experiences: Multi-step Process Building Semantic Web Applications requires: Metadata and ontology representation (How to represent the data)

Assigning weights to relationships: 

Assigning weights to relationships Weights represent collaboration strength Two types of relationships (in our dataset) ‘knows’ in FOAF (directed) ‘co-author’ in DBLP (bidirectional) Anna  co-author  Bob Bob  co-author  Anna

Assigning weights to relationships: 

Assigning weights to relationships Weight assignment for FOAF knows Verma Sheth Miller Aleman-M. Thomas Arpinar FOAF ‘knows’ relationship weighted with 0.5 (not symmetric)

Assigning weights to relationships: 

Assigning weights to relationships Weight assignment for co-author (DBLP) #co-authored-publications / #publications The weights of relationships were represented using Reification Sheth Oldham co-author co-author 1 / 124 1 / 1

Our Experiences: Multi-step Process: 

Our Experiences: Multi-step Process Building Semantic Web Applications requires: Querying and inference techniques

Semantic Analytics for COI Detection: 

Semantic Analytics for COI Detection Semantic Analytics: Go beyond text analytics Exploiting semantics of data (“A. Joshi” is a Person) Allow higher-level abstraction/processing Beyond lexical and structural analysis Explicit semantics allow analytical processing such as semantic-association discovery/querying

COI - Connecting the dots: 

COI - Connecting the dots Query all paths between Persons A, B using ρ operator: semantic associations query Anyanwu & Sheth, WWW’2003 Only paths of up to length 3 are considered Analytics on paths discovered between A,B Goal: Measure Level of Conflict of Interest Trivial Case: ‘Definite’ Conflict of Interest Otherwise: High, Medium, Low ‘potential’ COI Depending on direct or indirect relationships

Case 1: A and B are Directly Related: 

Case 1: A and B are Directly Related Path length 1 COI Level depends on weight of relationships Sheth Oldham co-author co-author 1 / 124 1 / 1

Case 2: A and B are Indirectly Related: 

Case 2: A and B are Indirectly Related Path length 2 Verma Sheth Miller Aleman-M. Thomas Arpinar Number of co-authors in common > 10 ? If so, then COI is: Medium Otherwise, depends on weight

Case 3: A and B are Indirectly Related: 

Case 3: A and B are Indirectly Related Path length 3 Verma Sheth Miller Aleman-M. Thomas Arpinar COI Level is set to: Low (in most cases, it can be ignored) Doshi

Our Experiences: Multi-step Process: 

Our Experiences: Multi-step Process Building Semantic Web Applications requires: Visualization

Visualization: 

Visualization Ontology-based approach enables providing ‘explanation’ of COI assessment Understanding of results is facilitated by named-relationships

Our Experiences: Multi-step Process: 

Our Experiences: Multi-step Process Building Semantic Web Applications requires: Evaluation

Evaluating COI Detection Results: 

Evaluating COI Detection Results Used a subset of papers and reviewers from a previous WWW conference Human verified COI cases Validated well for cases where syntactic match would otherwise fail We missed on very few cases where a COI level was not detected Due to lack of information or outdated data

Examples of COI Detection: 

Examples of COI Detection Wolfgan Nejdl, Less Carr Low level of potential COI 1 collaborator in common (Paul De Bra co-authored once with Nejdl and once with Carr) Stefan Decker, Nicholas Gibbins Medium level of potential COI 2 collaborators in common (Decker and Motta co-authored in two occasions, Decker and Brickley co-authored once, Motta and Gibbins co-authored once, Brickley and Motta never co-authored, but Gibbins (foaf)-knows Brickley) Demo at http://lsdis.cs.uga.edu/projects/semdis/coi/ or, search for: coi semdis

Our Experiences: Multi-step Process: 

Our Experiences: Multi-step Process Building Semantic Web Applications involves a multi-step process consisting of: Obtaining high-quality data Data preparation Metadata and ontology representation Querying / inference techniques Visualization Evaluation

Evaluation: 

Evaluation Demo at http://lsdis.cs.uga.edu/projects/semdis/coi/ or, search for: coi semdis Underlined: Confious would have failed to detect COI

Our Experiences: Discussion: 

Our Experiences: Discussion What does the Semantic Web offer today? (in terms of standards, techniques and tools) Maturity of standards - RDF, OWL Query languages: SPARQL Other discovery techniques (for analytics) such as path discovery and subgraph discovery Commercial products gaining wider use

… Our Experiences: Discussion: 

… Our Experiences: Discussion What does it take to build Semantic Web applications today? Significant work is required on certain tasks such as entity disambiguation We’re still on an early phase as far as realizing its value in a cost effective manner But, there is increasing availability of: data (i.e., life sciences), tools (i.e., Oracle’s RDF support), applications, etc

… Our Experiences: Discussion: 

… Our Experiences: Discussion How are things likely to improve in future? Standardization of vocabularies is invaluable such as in MeSH and FOAF; but also: microformats We expect future availability/increase of Analytical techniques used in applications Larger variety of tools Benchmarks Improvements on data extraction, availability, etc

What do we demonstrate wrt SW: 

What do we demonstrate wrt SW We demonstrated what it takes to build a broad class of SW applications: “connecting the dots” involving heterogeneous data from multiple sources- examples of such apps: Drug Discovery Biological Pathways Regulatory Compliance Know your customer, anti-money laundering, Sarbanes-Oxley Homeland/National Security …..

Our Contributions: 

Our Contributions Bring together semantic + structured social networks Semantic Analytics for Conflict of Interest Detection Describe our experiences in the context of a class of Semantic Web Applications Our app. for COI Detection is representative of such class

Data, demos, more publications at SemDis project web site, http://lsdis.cs.uga.edu/projects/semdis/Thanks!Questions: 

Data, demos, more publications at SemDis project web site, http://lsdis.cs.uga.edu/projects/semdis/ Thanks! Questions

References: 

References Related SemDis Publications (LSDIS Lab - UGA) B. Aleman-Meza, C. Halaschek-Wiener, I.B. Arpinar, C. Ramakrishnan, and A.P. Sheth: Ranking Complex Relationships on the Semantic Web, IEEE Internet Computing, 9(3):37-44 K. Anyanwu, A.P. Sheth, ρ-Queries: Enabling Querying for Semantic Associations on the Semantic Web, WWW’2003 C. Ramakrishnan, W.H. Milnor, M. Perry, A.P. Sheth, Discovering Informative Connection Subgraphs in Multi-relational Graphs, SIGKDD Explorations, 7(2):56-63 Related SemDis Publications (eBiquity Lab – UMBC) L. Ding, T. Finin, A. Joshi, R. Pan, R.S. Cost, Y. Peng, P., Reddivari, V., Doshi, J. and Sachs, Swoogle: A Search and Metadata Engine for the Semantic Web, CIKM’2004 T. Finin, L. Ding, L., Zou, A. Joshi, Social Networking on the Semantic Web, The Learning Organization, 5(12):418-435 Other Related Publications X. Dong, A. Halevy, J. Madahvan, Reference Reconciliation in Complex Information Spaces, SIGMOD’2005 B. Hammond, A.P. Sheth, K. Kochut, Semantic Enhancement Engine: A Modular Document Enhancement Platform for Semantic Applications over Heterogeneous Content, In Kashyap, V. and Shklar, L. eds. Real, World Semantic Web Applications, Ios Press Inc, 2002, 29-49 A.P. Sheth, I.B. Arpinar, and V. Kashyap, Relationships at the Heart of Semantic Web: Modeling, Discovering and Exploiting Complex Semantic Relationships, Enhancing the Power of the Internet Studies in Fuzziness and Soft Computing, (Nikravesh, Azvin, Yager, Zadeh, eds.) A.P. Sheth, Enterprise Applications of Semantic Web: The Sweet Spot of Risk and Compliance, In IFIP International Conference on Industrial Applications of Semantic Web, Jyväskylä, Finland, 2005 A.P. Sheth, From Semantic Search & Integration to Analytics, In Dagstuhl Seminar: Semantic Interoperability and Integration, IBFI, Schloss Dagstuhl, Germany, 2005 A.P. Sheth, C. Ramakrishnan, C. Thomas, Semantics for the Semantic Web: The Implicit, the Formal and the Powerful, International Journal on Semantic Web Information Systems 1(1):1-18, 2005