1. Hermes: News Personalization Using Semantic Web Technologies
Flavius Frasincar
Erasmus University Rotterdam

2. Contents Motivation Hermes Framework: Hermes News Portal: Conclusions
News Classification
News Querying
Results Presentation
Hermes News Portal:
An example
Conclusions
Future Work

3. Motivation Large quantity of news on the Web:
Difficult to find the ones of interest
News messages have a strong impact on stock prices
Limited annotation of RSS feeds:
Broad categories (business, cars, entertainment, etc.)
Google finance shows direct news which pertain to a certain portfolio:
Indirect news (competitors of Google like Microsoft) are not presented
Not possible to ask time-related queries about news

4. Hermes Framework Input: Output: Three phases:
News items from RSS feeds
Domain ontology linked to a semantic lexicon (e.g., WordNet)
User query
Output:
News items as answers to the user query
Three phases:
1. News Classification:
Relate news items to ontology concepts
2. News Querying
Allow the user to express his concepts of interest and the temporal constraints
3. Results Presentation
Present the news items that match user’s query

5. Hermes Architecture

6. 1. News Classification
Concept defined in the ontology (class or individual)
Multiple lexical representations for the same concept:
Ontology synonyms (e.g., New York → New York, Big Apple)
Semantic lexicon synonyms (e.g., buy → acquire)
Concepts without subclasses or instances:
Semantic lexicon hyponyms (e.g., company → dot-com)
Lookup ontology concepts into news items
A longer match supersedes a shorter match (European Central Bank supersedes European)

7. 1. News Classification 1.1 Tokenization (words, punctuation signs)
1.2 Sentence splitting (sentences)
1.3 Part-of-speech tagging (e.g., noun, verb, adj., etc.)
1.4 Morphological analysis (e.g., lemma “read” for “reading” as a verb)
1.5 Word sense disambiguation (e.g., Structural Semantic Interconnection (SSI) based on word context)
1.6 Adding “hits” between news items and the domain ontology

8. 2. News Querying 2.1 Query Formulation
Present the domain knowledge as directed labeled multi-graph:
with the additional constraint that arcs between two nodes are not allowed to share the same label (called conceptual graph)
User selects the concepts of interest in the conceptual graph (e.g., Google)
User is able to add to its selection concepts related to the concepts of interests using specified relations (e.g., hasCompetitors: Microsoft, eBay, and Yahoo)
The selected concepts are presented in a separate graph (called search graph)

9. 2. News Querying News are time stamped
User is able to specify that only news in a certain time interval should be retrieved
Time constraints:
Last hour
Last day
Last year
[ T00:00: :01, T00:00: :01 ]
[Future: order constraints (e.g., order by time)]

10. 2. News Querying 2.2 Query Execution
Generate the query in a semantic query language:
Map concepts of interest to query restrictions (current: disjunctive queries)
Map temporal constraints to query restrictions
Execute the semantic query
The order of the relevant news items is not important here

11. 3. Results Presentation 3.1 News Sorting
Return news items that match a query
Sort the news items based on their relevance degree to the query
The relevance degree is determined empirically:
based on a weighted sum of the number of hits in title (higher weight) and body (lower weight) of the news item
News items that have the same relevance degree are sorted in descending timestamp order

12. 3. Results Presentation 3.2 News Presentation
Present the concepts involved in the query
Per each news items show a summary:
Title
Source
Date
Few beginning lines from the news item ([Future: snippet])
Emphasize the hits (found concepts from the ontology) in the retrieved news items
Show the icons of the most important query concept found in a news item:
based on a weighted sum of the number of hits in title (higher weight) and body (lower weight) of a concept in a news item

13. Hermes News Portal
Hermes News Portal (HNP) is an implementation of the Hermes framework
Implementation language: Java
Ontology represention language: OWL (e.g., cardinality restrictions, inverses, etc.)
Semantic lexicon: WordNet
Graph visualization: Prefuse (OWL2Prefuse)
Query language: SPARQL
SPARQL extended with custom time functions (e.g., currentDate(), currentTime(), etc.)
Natural language processing: GATE

14. An Example
Query:
Which are the news items about Google or one of its competitors from the past six months?

15. 1. News Classification – Import News

16. 1. News Classification – Conceptual Graph

17. 2. News Querying- Search Graph
Individuals
Classes
Selected
concepts
Concepts
related
to the selected
node
Concepts
from
keyword
search

18. 2. News Querying - Search Graph

19. 2. News Querying- SPARQL SPARQL query:
PREFIX hermes: <
SELECT ?title
WHERE {
?news hermes:title ?title .
?news hermes:time ?date .
?news hermes:relation ?relation .
?news hermes:relatedTo ?concept .
FILTER
( ?concept hermes:relatedTo hermes:Google ||
?concept hermes:relatedTo hermes:Micosoft ||
?concept hermes:relatedTo hermes:Ebay ||
?concept hermes:relatedTo hermes:Yahoo ) (
?date > " T00:00: :01" &&
?date < " T00:00: :01" }

20. 2. News Querying- tSPARQL
Custom time functions:
Function name
Output type
currentDate()
xsd:date
currentTime()
xsd:time
now()
xsd:dateTime
dateTime-add(xsd:dateTime A, xsd:duration B)
dateTime-substract(xsd:dateTime A, xsd:duration B)

21. 2. News Querying- tSPARQL
tSPARQL query:
PREFIX hermes: <
SELECT ?title
WHERE {
?news hermes:title ?title .
?news hermes:time ?date .
?news hermes:relation ?relation .
?news hermes:relatedTo ?concept .
FILTER
( ?concept hermes:relatedTo hermes:Google ||
?concept hermes:relatedTo hermes:Micosoft ||
?concept hermes:relatedTo hermes:Ebay ||
?concept hermes:relatedTo hermes:Yahoo ) (
?date > hermes:dateTime-substract(hermes:now(), P0Y6M) &&
?date < hermes:now() }

22. 3. Results Presentation

23. Conclusions
Hermes Framework: presents news items that match the user interests
Hermes Framework:
News Classification
News Querying
Results Presentation
Hermes News Portal (HNP): an implementation of the Hermes framework
HNP based on:
WordNet semantic lexicon, OWL ontology, (extended) SPARQL queries, Prefuse visualization, GATE natural language processing

24. Future Work Word Sense Disambiguation: Ontology updates:
GAMBL (supervised learning algorithm)
Ontology updates:
Learning from news items
Check if the extracted information obeys the ontology axioms:
Faulty extraction
Ontology axioms update
Simplify the query interface:
Allow users to ask English queries based on a limited vocabulary
Evaluate the tool outside the university lab