The document describes a semantic recommendation system for helping customers select fish for an aquarium. The system takes into account various criteria like temperature, predator/prey relationships between fish, food requirements, ecosystem needs, size, and color preferences. It integrates data from multiple sources and uses semantic technologies like ontologies and linked data to make personalized recommendations based on a user's needs and preferences. The system aims to connect people interested in fish keeping through a social network application.

1. Group: The Semantic 4

2. I need
an
Aquarium!
Let‘s Consult some stores
and select some fishes
Empty Aquarium 
Customer

3. I Want an
Aquarium !
Semantic
Aquarium
Compatibility check
Solution
The criteria are:
 Temperature
 Predator
 Food
 Ecosystem: Sea , lake, …
 Dimension/ Size
Customer

4. I want Nemo!
I want a fish that will not eat my Nemo
Can I put Dolly with Nemo?
I dont want to buy different kind of food for my fish
I have a small Tank what about buying small fish?
I love orange colour
I do not like Bruce, he will just eat them all and
maybe me also

5. User
Interface
Knowledge Base
Query
API
Inter-Linking
Data

6. IMarine DB Pedia
owl: sameAs

7. Shop DB IMarine
tloCore: LX3_has_type
Fish x
Thunnus
Albacares
Has_type
SameAquaCore:
hasFood
Fake Food
SameAquaCore:
haColour
„red“
SameAquaCore:
hasSize
„red“

8. Group: The semantic 4

9. Recommendation System based on user
preferences and profiles
Integrating with other datasets:
Plants, Food, ...
Music, clothes, …
FishBook: bringing fish enthousiasits together!