Um, in the bottom right corner of your

page, you should have a go on stage

button. So, if you click that,

>> I done it.

>> Nice. And then if you can test sharing

your screen.

>> Yeah, I

Where was the sh Oh, a screen.

Yes.

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>> You sure?

>> Okay. I'll give you a five minute

warning before the session ends. I'll

also I'll I'll click start session.

We'll get 30 seconds. Um and then um

I'll introduce and hand over to you and

I'll give you five minutes before the

session ends as well.

>> Thank you.

Um

>> I need to share my uh other my no 4G

browser as well. How can Oh, I should go

to that and share change the share. Can

I?

>> Uh yes. Yeah, I am going to start the

session now and

it will give us a countdown timer.

Okay.

Hello all, welcome to the session. Uh

this session is building contextual

knowledge graphs to adapt, evaluate and

leverage LLMs with Samira Kurani. Uh

over to you Samira.

>> Hi nice to have be here. Thank you so

much for this opportunity and thank you

so much for everyone who attend this uh

session for me. I really would like talk

about what I have done during last year

and also thanks to no for for building

my career. I'm here for the second time

and I learned a lot from no and I got a

good support from my startup which is

I think my screen is shar so let's start

oh no

okay

I would like talk about what is exactly

three put down. The triper wants to if

you're going to a trip and you're

walking on the street and suddenly you

want to do something nice and see for

example moon is shining and you would

like to watch how moon is beautiful and

find an event about going to adventure

adventure and uh find having

conversation and find the perfect night

for little adventure helps you If if one

of you like to go to a wine drink a wine

tasting event or another of you wants to

go for

watching moon have a idea and give you

the best offer based on your your trip.

Actually a trip is always was my things

and I had always that problem that that

if I want go to a trip and I'm a wine

person I couldn't easily find the uh

best wine event is like be being in trip

like I'm local. So I want to have this

recommener system and triple but easily

come to mind that oh that's okay LLM can

do that for us but no LLM never done

that for us because LLM never understand

it justification

it's just about

statistical number of words it's never

wants to bring the truth for the user

and fact for the user even for the

latest uh version of LLM. I last night I

asked that I want to go a trip to a

restaurant for Christmas. I'm living in

Gway and ask about the Gway. This can

you offer me Christmas event in a

vegetarian restaurant. I didn't get an

answer. I just I'm sorry I cannot find

an event. It's that restaurant is close

to cliff of Malahour and you can go that

it's both of one of them is CL another

is chat GBT5

I didn't get that answer because LLM

doesn't know the fact so I've decided to

instead of put the context inside the

LLM and prompt I decided to put the

context inside the graph what that means

instead of just playing with the

location and event and uh and uh to tour

populated by people, review by people. I

give more more uh context like weather

like uh event like like concert if I'm

going to Ireland tomorrow morning which

concert I can uh attend I can have in in

a one question not the list of data that

uh I I didn't want to burn on the lots

of data they provided to me so I decided

to change the uh the way usually

construct the graph. So instead of just

entity and semantic relationship I add

some more attribute which is dynamically

adopted and uh uh and actually help me

to have a reasoning layer when my LLM

try to understand to give answer to the

user. I also added some sort of

demography. What was how I brought that

demography from Instagram the viral post

who who have this post who talk about

which type of trip who talk about which

kind of adventure is uh perfect for for

me as a for example as a

woman as a second

English speaker. with how can I enjoy my

trip

but uh what h happened in that states I

the query understandings get very easy

and graphs

graphs what what I meant about graphs I

didn't want to I wanted my llm generate

the cipher when I gave more context the

graphs work better for me because it

didn't need to understand to have a um

fixed cipher it could understand after

this pass what is the priority if it's

in Gway so and I'm a woman so and I will

arrive in 10 p.m. I do not offer walking

on this walking in a for a distance

because and for that is this hop of

reasoning is based on the graph that

knows and reason before answering the

question

and also ground response. I definitely

uh had zero sense of hallucination.

graph is working without hallucinating

without adding anything to the answer

for the user.

How that work? What exactly? I wanted to

answer that where where can I stay near

the eel tower for Christmas that we

within walking distance of hotel Louisa

and has a holiday event. This is the

question everyone's going to trip

sometimes. Answer that. How my graph

answer find the node which is the hotel

name and age of walking distance. I

easily could calculate that one and has

event how because I get data from event

and then the walking distance is less

than 2 kilometer. I define that and

probably it can be better to the output

is look like hotel hotel A and hotel C

are in with two kilometer of hotel

Louisa and after Christmas event this

was the part that missed on a general

LLM at the moment.

So how I create this contextual

knowledge graph? There is a lots of data

and every day generated more data again

and again. For example, the number of

tourist uh

venue in Gway do not change in a year.

But every day with uh LLM with uh

influencer

many data generated for one place

for that reason I just gather place

Latin lang. It was one of the things

that I really get a lots of help for JS

because they had a a great function to

handle Latin L

for for gathering my data set data set.

I get data from Yel trip advisor event

bright open street map weather API

general name and construct my graph.

After constructing my graph, I provide a

restful API to a LLM which every times

LLM have a plug-in. Uh I use some anti-

kernel plugin connect to the graph bring

the answer and uh give it to the user.

The conversational can be ongoing and

every time answer can be improving. But

uh the most important part of this

project was constructing

the graph based on

location and based on

temporal data behavioral and situational

why I'm why focus on situational for

example Gway in Ireland the weather in

that in winter and autumn is awful But

we have a galway in United States which

the weather is not as rainy as in as in

Ireland. So um it it was another

challenge I had to deal with other than

Latin lang uh lang on uh conversation I

should understand when people were going

to uh for example Gway in Ireland they

are talking about the Gway in Ireland.

do they are not talking about Gway in

United State and there is a lots of more

uh example of that without context

without having the fact on knowledge

graph it was impossible to answer this

question correctly zero hallucination at

first question first answer

uh we construct this graph so we had

data source and we add this data to the

data base and give it to LLM to LLM

retrieve the answer. Definitely a

structure is agentic based and uh we had

many agent which a planner make decision

when can connect to the knowledge graph

but uh for the phase of evaluation I

definitely rely on uh factchecking for

for me I get API from different source

and it was another beauty of ner I could

uh will show that uh in no forj you can

connect to API get data farm API and and

generate node and relation that was

really helpful because I get weather API

uh every time construct the relation and

after 24 hours that relationship

would would change and delete that that

was beauty of the uh no forges graph So

uh let's go talk about the schema I've

design and this is a very small part of

my graph because it's is located is just

in island or even less than island. Uh

we had

uh accommodation, we had we had

hospitals, night life, even post office,

uh country, city and whether restaurant

we have we create these data set based

on the address uh even the the URL,

Latin language status and

and construct different type of uh

relation with them. Some of this data

was a statistic like place

names can be changed but Latin lang is

really hard to change. So uh my office

doesn't need to get update about the

place which is include uh hotels uh and

uh

hospitals for examp

zero chance you get change in a year but

the the constant change was comes from

event seasonal data

about the concert

about the uh weather that was the part

of my graph that should be changed

I create

many different semantic relationship

which most of them came from API. So uh

what then after that what we have done

we we add we improved the prompt to use

subgraph for answering the question. If

it's about island, you need to go just

search on subg graph name island that

label that helps you to bring back the

answer for the user and uh and that what

and other things was everything

hallucination zero and dynamic

adaptation which I talk about it because

of changing an API that data gave every

day

but u still we need evaluation for

example if

Seasoned Ky coffee in Gway hosting

Christmas event with 2K of hotel data is

that we you find the entity matching you

can construct concentrate which is in

that case was event and season and uh

check the distance verify the output.

The output should give you the most

direct answer based on what you need not

what I have.

An orange is perfect for my case. Why?

Because of Eskimo was flexible pattern

based quering

and seamless integration. Uh no has a

connection to lang chain. Unfortunately,

I didn't use lang. I used semantic

kernel and but it still was a very

helpful uh application for my use case.

But still cipher query is a is is a

bottleneck is always a bottleneck when

you have a big graph. You you need to

understand when you start and in a graph

with at least for a small country like

Ireland with 45 million no age is really

hard to start. Indexing was helpful and

P APO which is uh batch optimizing was

great. I I write something on my

medium about and indexing or sol solve

that problem and data quality filter. We

we try to have a uh high quality data

and stop generating

duplicate data. For example,

our data was based on Latin lang and

Latin lang was enough for me to

understand how my graph is working and

any uh extra data just add as a property

to the same Latin line for me and uh for

for going to a scale I used cloud

integration and power processing and

vector index. So another problem we have

which when user ask a question uh we

need to understand what is the intent to

based on that intent go and find the

pass and bring the answer for the user

for that using reasoning for for

understand the intent of the user wasn't

the best approach I went for uh for a

store intent of user in different

vectors

but what I learned is flexibility is

everything. Database design is not graph

design and graph enrichment is the

bridge data silos. We had a lots of data

in different source which talking about

same things but they never been together

and never been uh in some place to give

whatever user needs in in actually in

travel industry or especially for

inexperienced traveler and subgraph is

is the key and real time context uh

required hybrid infrastructure for me

and My takeaway was context is the

missing bridge graph design. I said that

and uh if that's okay I can show my

graph now. It's oh

let me share my screen

now.

So uh I said when you have a for example

this is my graph and when every day

every uh uh an hour I get update from

weather API and it's easily for I will

remove the my uh API every times you run

that code the the relation and the

weather will be created and it's not

just about that everything's in this

graph are related to each other based on

different attribute including in same

city in uh in in in same in in same

category

in in in even in same weather in same

season and even based on has event

and that's it

I think I'm finish everything

Okay. If there is

Yes, please. If there is any question, I

would be happy to answer.

Oh.

Yes, I have question here to check the

coherence. You took vector embedding

between the names that might be

heterogeneous. No. Uh I had a W vector

based on named and Latin lang.

uh it's it's take me uh two three months

to build the graph then we improve that

and now we are ready for launch actually

we are in the phase of getting fund

Yeah. Yes. Actually when you have a

question, you write a question where

what can I do now and you get you give

access to us to have your Latin line

based on who you are and what we know

about you. We offering the we we

offering you the events you can attend.

Thank you.

And yes

no no no we are having a graph learning

graph during the time with the with for

example if if this year get data from

Gway in 2023 2024 for 2025 can be

learned based on what happened and we we

actually um emphasize on graph learning

more than LLM learning.

Yes, because reasoning it's important.

You answer the question based on the

intent of the user. And if if I just

rely on reasoning, I can rely on 12 13

uh intent. But when I have a vector

database, I have a bunch of re a bunch

of intern that I know how to answer that

actually I can answer everything on my

knowledge graph because I do not rely on

reasoning on llam.

So if there is not a question

anymore.

Okay.

Sure.

Thanks. Thank you so much guys for

having me for attending my presentation.

It was really nice to have audience.

Thank you.