1. Research Overview 23 August 2006
Research Overview
23 August 2006

2. UMBC and Ebiquity
UMBC is a research extensive University with a a major focus on Information Technology
Ebiquity is a large and active research group with the goal of
“Building intelligent systems in open, heterogeneous, dynamic, distributed environments”
Current research includes mobile and pervasive computing, security/trust/privacy, semantic web, multiagent systems, advanced databases, and high performance computing
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3.
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4. People and funding Faculty: Finin, Yesha, Joshi, Peng, Halem
Colleagues: Oates, desJardins, Pinkston, Segall, …
Students: ~10 PhD, ~10 MS, ~5 undergrad
Funding
Current: DARPA (Trauma Pod, STTRs), NSF (two ITRs, Cybertrust, NSG, …), Intelligence community, NASA, NIST, Industry (IBM, Fujitsu, …)
Recent: DARPA (CoABS, GENOA II, DAML), NSF (CAREER)
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5. Ebiquity Research SpaceKR
information
extraction
user modeling
semantic web
machine
learning IR AI
data
mining
Intelligent Information Systems
web services/SOC DB
knowledge management
wearable computing
mobility
policies
HPCC
assurance
Networking & Systems
wireless
Security
trust
context awareness
DRM
pervasive computing
intrusion detection
privacy
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6. Ebiquity Research Space
language technology
robotics
HCI
planning KR
Building intelligent systems in
open, heterogeneous, dynamic, distributed environments
user modeling
semantic web
data
mining
machine
learning AI DB
Intelligent Information Systems
knowledge management
web services IR
service oriented computing
wearable computing
policies
Networking & Systems
wireless
Security
mobility
assurance
context awareness
pervasive computing
intrusion detection
privacy
trust
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7. Some Current and Recent Projects
Pervasive and mobile computing
(1) Trauma Pod
(2) Context aware pervasive computing
(3) Mogatu: Tivo for mobile computing
Semantic Web
(4) Swoogle: searching and indexing Semantic Web data
(5) Semnews: text understanding and extraction
(6) Agents and the Semantic Web
(7) Spire: Semantic Web for data discovery and integration
Security and trust
(8) Semantic policy languages
(9) Semdis: Discovering Semantic Links
(10) Securing ad hoc networks
(11) Privacy for passive RFID tags
Information extraction and retrieval
(12) Recognizing spam weblogs
(13) Extracting opinions from weblogs
(14) Modeling the Spread of Influence on the Blogosphere
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8. Semantic Web "The Semantic Web is an
extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation."
-- Berners-Lee, Hendler and Lassila, The Semantic Web, Scientific American, 2001
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9. OntoRank for RDF documents
Doc. & Term Ranking
Swoogle Search
OntoRank for RDF documents
TermRank for RDF terms
TermRank: Ranking semantic web terms
Analysis
Index
Discovery
IR Indexer
Search Services
Semantic Web
metadata
Web
Service
Server
Candidate
URLs
Bounded Web Crawler
Google Crawler
SwoogleBot
SWD Indexer
Ranking
document cache
SWD classifier
human
machine
html
rdf/xml …
Semantic Web Archive
Ontology Dictionary
Sem. Web Archive
Swoogle Statistics
SW docs.
1.6M
classes
1.3M
embedded
400K
properties
175K
triples
300M
individuals
43.1M
ontologies
10K
registered users
417
Swoogle Triple Shop
July 2006
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Filip Perich
Contributors: Tim Finin, Li Ding, Rong Pan, Pavan Reddivari, Pranam Kolari, Akshay Java, Anupam Joshi, Yun Peng, R. Scott Cost, Jim Mayfield, Joel Sachs, and Drew Ogle. Partial research support was provided by DARPA contract F and by NSF by awards NSF-ITR-IIS and NSF-ITR-IDM April 2005.
Semantic Web Navigation

10. Semantic Prototypes in Research Ecoinformatics
Ebiquity • CAIN• RMBL • Mindswap
Semantic Prototypes in Research Ecoinformatics
ELVIS Ecosystem Location Visualization and Information System
What are likely predators and prey of an invader in a new environment?
Web Ontologies
For intelligent agents
Species List Constructor
Click a county, get a species list.
Food Web Constructor
Predict food web links using database and taxonomic reasoning.
Evidence Provider
Examine evidence for predicted links.
SpireEcoConcepts
Food web database
concepts and results of Food Web Constructor
ETHAN
Evolutionary Trees and Natural History
For online scientific data-bases about species and higher taxonomic levels
For humans
For machines
Predictions
In an new estuary, Nile tilapia could compete with ostracods (green) to eat algae. Predators (red) and prey (blue) of ostracods may be affected.
Oreochromis niloticus
Nile tilapia
TripleShop A SPARQL Workshop for Triples
Use the semantic web to find and share body masses of fish that eat fish.
Query
Enter a SPARQL query.
Create a dataset
Find semantic web docs that can answer query.
Get results
Apply query to dataset with semantic reasoning.
PREFIX rdf: <
PREFIX rdfs: <
PREFIX spec: <
PREFIX ethan: <
PREFIX kw: <
SELECT DISTINCT ?predator ?prey ?preymaxmass ?predatormaxmass
WHERE {
?link rdf:type spec:ConfirmedFoodWebLink .
?link spec:predator ?predator .
?link spec:prey ?prey .
?predator rdfs:subClassOf ethan:Actinopterygii .
?prey rdfs:subClassOf ethan:Actinopterygii .
OPTIONAL { ?prey kw:mass_kg_high ?preymaxmass } .
OPTIONAL { ?predator kw:mass_kg_high ?predatormaxmass } }
Esox_lucius.owl
webs_publisher.php?
published_study=11
Actinopterygii.owl
Export results for further analysis
(e.g. in Food Web Constructor).
UMich Animal Diversity Web
Name, tag, and share with group members or public.

11. SEMDIS Motivation Architecture
On Homeland Security and the Semantic Web: a Provenance and Trust Aware Inference Framework
Semantic Association Discovery and Evaluation
Motivation
Architecture 2 1
Semantic association between X and Bin Laden
Provenance
Multiple sources contribute unique fragments of association
Multiple sources confirm a fragment in different belief states
Rank: only some discovered associations are interesting
Trust: some information sources not sufficiently trustworthy
Collaborative implementation
University of Georgia
Extracting knowledge from the Web
Discovering complex semantic association
Ranking semantic association by content
UMBC
Tracking provenance of semantic association
Trusting semantic association by context
Enabling best-first search using trust heuristics

12. SEMDIS Provenance Trust
On Homeland Security and the Semantic Web: a Provenance and Trust Aware Inference Framework
Semantic Association Discovery and Evaluation
Provenance 3
Trust 4
Provenance of an RDF graph or sub-graph
Three sources of a RDF graph, G
where-provenance: the web documents that serialize G
whom-provenance: the person who created/published G
why-provenance: the RDF graphs which logically imply G
Trustworthiness of an RDF graph
The hypothesis “Mr X is associated with Bin Laden” is proved by a four-triple semantic association (SA), how to evaluate SA’s trustworthiness.
S1: eg:MrX eg:isPresidentOf eg:companyA
S2: eg:organizationB eg:invests eg:companyA
S3: eg:organizationB eg:isOwnedBy eg:MrY
S4: eg:MrY eg:relatesTo eg:BinLaden
Trust relation between agents helps propagate belief states
case1: (belief concatenation) exact one source per triple
case2: [belief aggregation] multiple sources for a triple
case3: [social dependency] sources are dependent through social network
We assume all triples are semantically independent
RDF graph provenance service
Observations
provenance information is part of context information
provenance is not required for most inference tasks
provenance is useful for context based trust analysis
provenance can be used to group knowledge
Approach
provide a stand alone service that queries provenance of a given RDF graph or sub-graph

13. SemNews: A Semantic News Framework
SemNews: A Semantic News Framework
Provides RDF version of the news.
WWW
Web of documents
Web of data
Text
Images
Audio
video
Ontologies
Instances
triples
Natural Language
RDF/OWL
Facts from NL
Semantic Web
NLP Tools
Intelligent agents need knowledge and information.
Majority of content on the Web remains in natural language.
SW can benefit NLP tools in their language understanding task.

14. SemNews: A Semantic News Framework
SemNews: A Semantic News Framework
Semantacizing RSS
Semantic RSS
Data Aggregators
News Feeds
OntoSem
TMRs FR
Language Processing
OntoSem2OWL
Dekade Editor
Knowledge Editor Environment
Semantic Web Tools
OntoSem Ontology
(OWL)
TMR
Inferred
Triples
Fact Repository Interface
Ontology &
Instance browser
Text Search
RDQL Query
Swoogle Index 1 2 5 6 7 8 9 3 4 10 11 12 13 14 15
RSS
Aggregator
Browsing Facts
View structured representation of RSS news stories.
Browse facts not just news.
Ontologically linked News
Semantic Queries
Semantic Alerts
Tracking Named Entities
RDQL
Find news stories by browsing through the OntoSem ontology.
Structured queries over text converted to RDF representation.
Ontological Semantics
Find stories about a specific named entity.
Alerts can be specified as ontological concepts/ keywords/ RDQL queries. Subscribe to the results as an RSS feed.
OntoSem to OWL
NL Text
OntoSem
OWL
Ontology
Lexicon
OntoSem2OWL
Fact
Repository
TMR
TMRs
In OWL
OntoSem is a NLP system that processes text and converts them into facts.
Supported by a constructed world model encoded in an rich ontology. Its Ontology has > concepts with an average of 16 properties/concept.
OntoSem2OWL is a rule based conversion engine that maps frame-based OntoSem ontology, fact repository to OWL. Over triples generated.

15. Analyzing Weblogs Blogs are an important new communication technology
Social networking + user generated content
On the Blogosphere or in an Intranet (e.g., Sun, Intel)
Modeling and understand blog-based systems
Extracting and computing metadata
Topic modeling and splog detection
Community recognition; influence and information flow
Opinion extraction (TREC 06)
More to come: fact extraction, recognizing bias, trend mapping, event detection, monitoring for “surprises”
If the Web is our common “brain”, the Blogosphere is its consciousness
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16. Detecting Spam Blogs: A Machine Learning Approach
What are Spam Blogs (Splogs)?
Blogs hosting machine generated posts, each adding to web spam
Posts have content hijacked from other blogs and/or stuffed keywords
Posts with links interspersed between random text
Blogs with context based ads to fool users into clicking ads (See 3) 1
A Case of Content Plagiarism
(1) Original Post by Ebiquity
(2) Infiltration in Search Results
(3) A splog result 2
Why are Splogs a Problem?
Splogs undermine ranking algorithms (See 6)
Splogs water down search results (See 6)
Splogs threaten the Web advertising model (See 3)
Splogs indulge in “plagiarism” (See 1,2,3)
Splogs skew results of social research tools (See 4)
Splogs stress the Blogosphere infrastructure (See 4,5) 3
$197
“Holy Grail Of Advertising... “
“Easy Dominate Any Market, Any Search Engine, Any Keyword”
This Work
Formalizes the Splog Detection Problem
Supervised Machine Learning Technique
Training set of 1400 hand labeled examples
Effectiveness of Specialized features
Local Models for Fast Splog Detection
Global link-based models good for (delayed) detection
Precision/Recall of 87% for bag-of-words
See
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Filip Perich
Blog Features
We, what, was, my, org, flickr, paper, words, me, thank, go, archives
Splog Features
Find, info, news, website, best, articles, perfect, Products, uncategorized, hot, Resources, inc, copyright

17. Feeds That Matter General Statistics Applications 5/14/2019
83,204 publicly listed subscribers.
2,786,687 feeds of which 496,879 are unique.
26,2436 users (35%) use folders to organize subscriptions
Data collected in May 2006.
Feed Recommendations
Finding Influential Feeds for a Topic.
Two feeds are similar if they are categorized under similar folder names. The above chart shows the feed recommendations and corresponding text based cosine similarity.
The number of subscribers per feed follows a power law distribution.
The distribution of domains in the Bloglines dataset.
Leading Blogs on topic “Politics”. Seed set are top blogs in “politics” from bloglines and blog graph used is from Blogpulse dataset..
The number of folders per user. Most users tend to use modest number of folders.
Scatter plot showing the relation between the number of folders and the number of feeds subscribed. As more feeds are subscribed users tend to organize feeds into folders.
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18. Feeds That Matter Tag Cloud Before Merge Tag Cloud After Merge
Tag cloud generated by using the folder names as labels (Top 200 folder names).
Tag cloud generated by merging related folders (Top 200 folder names).
Folder Names are used as a substitute for topics. Lower ranked folder merged into a higher ranked folder if there is an overlap and high cosine similarity.
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19. Opinion extraction from Blogs
Data: 3M posts from 83K blogs over two weeks
Task: given a topic (e.g., March of the Penguins), find blogs posts that express an opinion about it.
Some features of our system:
Clean data by removing splogs (~12% posts) and non-content (e.g., ads, headers, footers, blogrolls)
Use Google to find opinion words relevant to topic and induced topic category (eg, heavy=bad for digital cameras)
Multiple-hand crafted heuristic scoring functions to measure opinionatedness given the topic phrase
Train an SVM to learn appropriate scorer weights
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20. Security and Trust in Open Environments
Many new information systems are open, heterogeneous and dynamic
Examples: the web, web services, P2P systems, Grid computing, pervasive computing, MANETs, etc.
Providing security and privacy in such systems is challenging
We can not rely on traditional authentication-based schemes
Recognizing “bad actors” in such systems is hard
We are exploring new approaches using computational policies, trust and reputation.
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21. Trust & Security for the Semantic Web
Autonomous agents need policies as “norms of behavior”
In OS, networking, data management, applications, multiagent systems, pervasive environments, etc.
Especially to secure complex open, distributed, dynamic environments
Traditional “hard coded” rules like DB access control & file permissions depending on known entities won’t work!
Trust associations based on attributes are needed
Interesting issues abound, like how to
Resolve conflicts among agents governed by multiple policies
Enforce policies via sanctions, reputation, escalation, etc.
Modify policies dynamically according to context
Make policy engineering easier than software engineering
1 A robot may not injure a human being, or, through inaction, allow a human being to come to harm.
2 A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
3 A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
An early policy for agents
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22. Rei Policy Language
Developed several versions of Rei, a policy specification language, encoded in (1) Prolog, (2) RDFS, (3) OWL
Used to model different kinds of policies
Authorization for services
Privacy in pervasive computing and the web
Conversations between agents
Team formation, collaboration & maintenance
The OWL grounding enables policies that reason over SW descriptions of actions, agents, targets and context
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23. Rei Policy Language
Developed several versions of the Rei policy specification language in Prolog, RDFS, & OWL
Used to model different kinds of policies
Authorization for services
Privacy in pervasive computing and the web
Conversations between agents
Team formation, collaboration & maintenance
The OWL grounding enables policies that reason over SW descriptions of actions, agents, targets and context
XSB
FLORA
YAJXB
USER
JAVA API
FOWL
REI
REI INTERFACE
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24. Applications – past, present & future
Coordinating access in supply chain management system
Authorization policies in a pervasive computing environment
Policies for team formation, collaboration, information flow in multi-agent systems
Security in semantic web services
Privacy and trust on the Internet
Privacy in pervasive computing environments
1999
2002
2003 …
2004 …
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25. Enhancing Web Privacy via Policies and Trust
Motivation
compliant
Non-compliant
Trust website policies
W3C specified P3P Architecture
Distrust website policies
P3P Compliance
Consumer Confidence
Key Points
Web Sites optionally publish P3P policies
Clients specify privacy preferences using a policy language, for instance Rei
Privacy Expert is the privacy enhancement enabler by binding together entities of the system
Rei Engine evaluates policies of users against website attributes
Website Recommender Network propagates and builds a model of websites based on reputation
FOAF – Enables the creation of the website recommender network
publish (optionally)
Website
Recommender
Network
Web Server
DiscussionGroup
serviceType 9
URI --
popularity
hasP3P
hasTextPolicy
hasPrivacyCertifier
subDomainOf
isBasedOutOf
hasPolicyEnforcement
lawEnforcedBy
USA
Yes US
OSDN
policySimilarTo
owner
Website Evaluation Ontology
P3P Policy
Ontologies, Trust rules
Personal agents
XSLT Transformer
Rei Engine
Privacy Expert
Intelligent Privacy Proxy*
FOAF
Rei Privacy Policy
(RDF based, enhancements
over APPEL)
Trusted Agent
Network#
Clients
publish
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26. Securing Ad-Hoc Networks
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27. Monitoring and Response
Active Response Framework
Nodes Snoop Locally
Send Signed Accusations to Other Nodes
Each Node Makes Decision Locally based on Policy
Accusations can be Corroborated and lead to increase in reputation
False Accusations Can Be Flagged and lead to loss of reputation (or even sanctions)
Nodes Can Choose Not To Communicate Through Suspected Nodes
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28. SWANS: Secure and Adaptive WSNs
A holistic policy driven approach to designing secure and adaptive wireless sensor networks
Secure self-organization
Centralized and distributed protocols
State determination
Parameters to define “raw” state
Node-level logical construct to identify complete state
Network-level logical construct to help identify global state
A set of policies to adapt to changes in state
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29. SWANS: Secure and Adaptive WSN
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30. ORs will be data rich CAST
Drugs
RFID
RFID
CAST
Tools
RFID
Patient Monitors
Staff
ORs will be awash in low-level data, much of it noisy or incomplete
Challenges include coping with the noise and interpreting the low-level data to recognize high-level events and activities
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31. System Architecture Trend Analyzer Context Aware Agent Rule RFID Base
Patient Monitor
Stream
Processor
(TelegraphCQ)
Continuous
Queries
Trend
Analyzer
Physiological Data
RFID
System
Medicines
Tools
Staff
Assert facts
Context
Aware
Agent
Database
Patient
History
Medical
Supplies
Staff
Assert facts
Rule
Base
Video
Clipper
Events
Medical
Encounter
Record
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34.
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