=Paper=
{{Paper
|id=Vol-2994/paper60
|storemode=property
|title=Smart Conversational User Interface for recommending Cultural Heritage Points of Interest (Discussion Paper)
|pdfUrl=https://ceur-ws.org/Vol-2994/paper60.pdf
|volume=Vol-2994
|authors=Flora Amato,Francesco Moscato,Vincenzo Moscato,Giancarlo Sperlì
|dblpUrl=https://dblp.org/rec/conf/sebd/Amato0MS21
}}
==Smart Conversational User Interface for recommending Cultural Heritage Points of Interest (Discussion Paper)==
https://ceur-ws.org/Vol-2994/paper60.pdf
Smart Conversational User Interface for
recommending Cultural Heritage Points of Interest
(Discussion Paper)
Flora Amato1 , Francesco Moscato2 , Vincenzo Moscato3 and Giancarlo Sperlì4
1 DIETI - University of Napoli Federico II, Naples, Italy
2 DIEM. University of Salerno, Fisciano (SA), Italy
4 DIETI - University of Napoli Federico II, via Claudio 21 - Naples, Italy
4 DIETI - University of Napoli Federico II, via Claudio 21 - Naples, Italy
Abstract
Researchers and companies are making great efforts to create new ways of interaction with a increasing
number and types of electronic devices, with particular attention to the conversational interface, whether
spoken or written. The cultural heritage domain can bring many benefits from the great effort in this field,
as a smart conversational system can provide both room/hotel/apartment information and information
related to the area, natural or cultural points of interest, tourist routes, social events, history etc. The
paper’s main goal is to present a quick and effective service to the tourists visiting a city for the first time.
Learning the cultural preferences of the users can enhance customer satisfaction, since a smart system can
propose them customised tours in order to reach point of interests according to the expressed preferences.
The interaction with the user is simplified by a conversational interface (chatbot).
1. Introduction
The Chatbot4Heritage (CB4H) project was born with the aim of integrating and centralising,
on a single platform, information related to the tourism sector in Campania, generally from
heterogeneous sources. In fact, the information a tourist may need during his or her stay ranges
from details on local cultural sites, to upcoming cultural and entertainment events, the location of
various types of services (e.g. in the catering sector), updates on the status of mobility, making it
necessary to carry out numerous searches on different websites and worsening the overall user
experience.
The project aims, through a service-oriented architecture, to extrapolate through a web crawler
the information coming from multiple event aggregator websites, transform it into a common
format and, through the mediation of an Enterprise Service Bus, store it in special databases. This
information can then be made available to client applications, including a Chatbot[1, 2, 3] with
which any users will be able to interact easily in natural language.
Researchers and companies are making great efforts to develop new ways of communicating
with a increasing number and types of electronic devices, with particular attention to the conver-
" flora.amato@unina.it (F. Amato); fmoscato@unisa.it (F. Moscato); vmoscato@unina.it (V. Moscato);
giancarlo.sperli@unina.it (. G. Sperlì)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
sational interface, whether spoken or in writing. The cultural heritage domain can bring many
benefits from the great effort in this field, as an intelligent conversational framework can include
both room/hotel/apartment information[4] and area specific information, natural or cultural points
of interest, tourist routes, social events, history etc. The main aim of the paper is to provide the
tourists visiting a city for the first time with a fast and efficient service.
The system can suggest to the users most appreciated cultural sites, by analysing opinion taken
from social networks[5], and specifically taking in consideration cultural heritage communities[6]
or the most authoritative persons[7] in the field of cultural heritage domain. Therefore, under-
standing the users’ cultural preferences can improve customer loyalty, as a smart system can give
them personalised tours to reach the point of interest according to the preferences expressed. A
conversational interface[8] (chatbot) simplifies communications with the user.
Moreover, since of Covid-19 disease spreading, many cities started to improve their mobility
services, like underground, by implementing a routing service for tourists[9, 10], that offers:
• Route planning by taking as input user’s first and last stop.
• number of stops from starting stop to last one.
• Number of line changes during the route.
• Detection of the nearest stop to a given tourist’s attraction.
TravelBot[8] provides the aforementioned basic functions and other services like:
• Informing the user about any faults on the lines.
• Showing the metro’s map.
• Informing and guiding the user to the purchase of tickets.
• Showing the order of the stops for the line selected.
• Informing the user about the working timetables of metro’s services.
2. System Architecture
In order to retrieve information about locations, events or other relevant information, we have to
chose a set of sources considered reliable. Then, the obtained data-set must undergo a process of
processing and cleaning, removing formatting errors and non machine readable elements, so that
the information contained in it can be inserted in a natural language sentence[11].
We adopted a Service Oriented Architecture because of its intrinsic modularity. In this type of
architecture, in fact, each module is developed independently and can be connected and removed
from the main system without compromising the others. If, conceptually, the chatbot is considered
a front end module, then it is possible to delegate data extraction and cleaning operations to other
back end modules, using the Service Bus as an intermediary. The chatbot, at this point, can use
the received data immediately, without having to process it further at runtime.
Some example components were chosen and described in Figure 1: a scraper, whose implemen-
tation in Python is described here through the Scrapy framework, for the Napolitoday.it website;
an external API, that of the Yelp website; a MySQL database, which in this field is identified as
Data Service; a web application in Django for direct insertion.
Figure 1: High level architecture of a bot. Data extraction occurs only in scraping bots.
For the implementation of the Service Bus, the choice fell on the open-source framework
WSO2. WSO2 Enterprise Integrator has a graphical drag-and-drop interface for the configuration
of the message flows between one component and another. One of the main features of a Service
Oriented Architecture is the possibility to configure the interactions between components, instead
of defining them entirely by code.
The application in Django, finally, was born as an interface for the management of CRUD (Crud-
Retrieve-Update-Delete) operations by an employee or operator of a cultural entity. However,
due to restrictions imposed by the framework itself, it was decided to use it for insertion only. In
the section dedicated to future developments, this issue will also be dealt with[12].
3. Knowledge Base population
Information extracted from web pages sources are processed and stored in the system’ Knowledge
Base. The Knowledge Base was implemented in Prolog language, taking advantage of its ease of
use. The code will be divided into:
• Facts: they report certain data known by the machine.
• Rules: they manipulate the facts to obtain data useful to the end user.
The “machine learning” process is used to obtain useful information for the user (e.g.) and this
will help the programmer who does not need to provide too many facts to the machine.
3.1. Facts
The machine knows two types of facts:
1. Connections (conn), composed by 3 arguments:
a) First stop;
b) Second stop;
c) Median time between that two stops, it also serves to identify which line is it.
It establishes that there is a connection between the two stops and how long the subway
takes to travel from one to another.
1 conn(battistini, cornella, 2).
2
2. Locations (locate), composed by 2 arguments:
a) List of one or more tourist attraction
b) Nearest stop to the attraction list.
It links a list of tourist attraction to their nearest stop.
1 locate([s_pietro,musei_vaticani],ottaviano).
2
3.2. Rules
The rules can enhance the inferential process that retrieve customised information accord-
ing to user preferences. Further enhancement can be taken with the exploitation of semantic
techniques[13]
Conn1 The machine can determine only a one way connection with his known fact “conn”.
To be able to establish a bidirectional connection between two stops, the machine uses this
mathematical expression: ∃conn(X,Y,C)i f ∃conn(Y, X,C)
The OR statement is needed to get rid off infinite recursive call that can be possible in the next
set of rules.
Append The “append” rule, like his name suggest, will take care of appending one element to
an existing list and it will give a new resulting list as an output.
• [Head1|Tail1] = List where you want to append an element.
• List2 = Element or list of elements that you wish to append.
• [Head1|TailResult] = Result.
First part of the code, by using a simple fact, establishes the result of appending an element
to an empty list. In the second part then we will use a rule that with a recursive call, taking
advantage of backtracking, delete the head of [Head1|Tail1] and continues until the list is empty,
after that it starts to rebuilt a new list starting from tail to head, using as a tail List2. This rule will
come in handy when we will need to find out how many line change we have to do during end
user’s journey.
1 append([],X,X).
2 append([Head1|Tail1],List2,[Head1|TailResult]):-
3 append(Tail1,List2,TailResult).
Change We use average time to go from one stop to the next, to check how many lines the
tourist must change.
• T = Average time between two stops.
• Lc = List of changes, initially empty and used as a temporary.
• [T |Ris] = A second list of changes, needed to make append rule to work and to get a result.
The rule will initially check if given T value is inside Lc by using: not(member(T,Lc)). After
that if the result is true, append rule will add the given T value to [T|Ris]. Otherwise we use
append with an empty list, so it will not add nothing to [T|Ris]. Result will be a list containing a
maximum of three elements, that it will be then used to determine how many lines the tourist
must change.
1 change(T,Lc,[T|Ris]):-
2 not(member(T,Lc)),
3 append([T],Lc,[T|Ris]).
4 change(T,Lc,[T|Ris]):-
5 member(T,Lc),
6 append([],Lc,[T|Ris]).
Len len works pretty much like append with a recursive call (backtracking) of itself and using
a given fact: len([],0). goal is different, we need to evaluate the length of a given list, in our case
that list will be Lc.
• [_HEAD|TAIL] = List.
• N = Number of elements.
Decr This simple rule will decrement P by 1 and it will store the result in Q. decr will work
with change and len to work on Lc, we will see that our main goal is to get the number of elements
in Lc and decrement that number by one to get our result.
Connection connection is a rule that takes two stops as input and calculates the list of stops,
line changes made and duration time of the journey. The algorithm takes care of solving the
"travel salesman" problem.
Rule’s arguments are:
• Fs = First stop.
• Ls = Last stop.
• Ms = Middle stop.
• Lc = List of changes.
• Pl = Prohibited list.
• T = Total time of journey.
• N1 = Number of changes.
• Result = Non decremented list of changes.
• L1c = Temporary list with last stops to do.
In the first part of code, connection will check if there is a direct connection between first stop
(Fs) and last stop (Ls) by using rule conn1 and checking after if these two stops are member of
Pl. Otherwise if this first part is false, the machine will search for a middle stop Ms and check if
there is a direct connection to first stop (Fs). When this stop is found, the machine will check if
both stops are not in the prohibited list using not(member) after that change rule will be called
checking if there was a line change from one stop to another. Change rule as we saw makes a
list made of average times taken only once (in this specific case), so the maximum length of this
list will be three, decreasing this value by 1 will give us N1. Please note: Both Pl and Lc must
be empty list while calling connection in a query, because when the tourist starts his journey he
have not travelled to anywhere still. Also neither first stop and last stop can be prohibited stops.
Connection is going to use a recursive call that will find a connection between Ms and Ls, using
updated arguments value:
Fs → s Pl → [Fs, Pl] Lc → Result (1)
The recursive call has to be called until it find a direct connection between middle stop (Ms)
(which is going to be updated everytime) and final stop (Fs). After the rule will find final route
it will determine length of Result by using change and len, lastly to get N1 it uses decr (N,N1).
Then connection will calculate T by adding each time between all the stops.
1 connection(Fs,Ls,Pl,Lc,[Fs,Ls],T,N1):-
2 conn1(Fs,Ls,T),
3 not(member(Fs,Pl)),
4 not(member(Ls,Pl)),
5 change(T,Lc,Ris),
6 len(Ris,N),
7 decr(N,N1).
8 connection(Fs,Ls,Pl,Lc,[Fs|L1c],T,M):-
9 conn1(Fs,Ms,T1),
10 not(member(Fs,Pl)),
11 not(member(Ms,Pl)),
12 change(T1,Lc,Result),
13 connection(Ms,Ls,[Fs,Pl],Result,L1c,T2,M),
14 T is T1+T2.
Visit Rule that calculate the path for the nearest stop, based on the selected attraction, it also
shows the travel time, the number of changes and the stops list. Or, choose the final stop you want
to reach and define the initial stop where the user is located, check if there are tourist attractions
nearby. Compared to ‘Connection’ rule, there is an additional argument: the attraction that the
user wants to visit (Att). The IM, as we already said, shows facts that associate a list of attractions
with their nearest stop. Arguments:
• Att = Attraction.
• Fs = First stop.
• Ls = Last stop.
• Ms =Middle stop.
• Lc = List of changes.
• Pl = Prohibited list.
• T = Total time of travel.
• N1 = Number of changes.
• Result = List containing the number of changes not decreased.
• L1c =List that represent the remaining path.
In the first case the IM checks if there is already an attraction or a list of them X, in the
Knowledge Base, that can be associated with the last stop (Ls). After the IM finds it, checks if
the desired attraction belongs to the list X, if it’s true the IM searches a connection between Fs
and Ls. If, at the first try, the IM doesn’t find a direct connection, then it recalls the OR in visit
and it will repeat the initial step, but this time searching, In the facts, a connection between Fs
and a middle stop Ms, with T1 as travel time, recalling the rule: conn1. For the remaining part of
the code, can be checked the rule: connection.
1 visit(Att,Fs,Ls,Pl,Lc,[Fs,Ls],T,N1):-
2 locate(X,Ls),
3 member(Att,X),
4 conn1(Fs,Ls,T),
5 not(member(Fs,Pl)),
6 not(member(Ls,Pl)),
7 change(T,Lc,Ris),
8 len(Ris,N),
9 decr(N,N1).
10 visit(Att,Fs,Ls,Pl,Lc,[Fs|L1c],T,M):-
11 locate(X,Ls),
12 member(Att,X),
13 conn1(Fs,Ms,T1),
14 not(member(Fs,Pl)),
15 not(member(Ms,Pl)),
16 change(T1,Lc,Result),
17 visit(Att,Ms,Ls,[Fs,Pl],Result,L1c,T2,M),
18 T is T1+T2.
4. TravelBot
In this section we will show the main functions of the chatbot component and guidelines for
correct execution. The main program uses two doc.json, these two document has been made with
two different approach to test the chat bot:
1. ‘doc-v1.json’ had pattern made of extended question containing when/how/what/which
pronouns;
2. ‘doc.json’ is the current document in use, and is made of keywords for each tag with no
pronouns at all.
If you want to test the two different documents please make sure to delete model[8] and data
files or uncomment line 46 and 123, and change the following constant to the corresponding file.
In Listing 1 we open the document ’doc.json’ and download the content. With the Boolean
cycle ’for’ we are taking the words taken from the document and inserting them in the lists
through the functions ’append’ and ’extend’.
1 with open(DOC_FILE) as file: #Opens "doc.json" file and load with json libray
.
2 data = json.load(file)
3
4 try:
5 #asfgsadf <----- uncomment this to force data parsing again
6 with open("data.pickle", "rb") as f:
7 words, labels, training, output = pickle.load(f)
8 except:
9 #Empty lists
10 words = [] #Words list contains all the words of our "patterns"
11 labels = [] #Labels list containing all the "tag" words
12 docs_x = [] #doc containing a list of all the patterns with its "tag" in
the corresponding position (docs_y)
13 docs_y = []
14
15 #Start cycling through out data dictionary (doc.json)
16 for intent in data["intents"]:
17 for pattern in intent["patterns"]:
18 #tokenize all the world in our document using nltk, after that we
extend word list,
19
20 wrds = nltk.word_tokenize(pattern)
21 words.extend(wrds)
22 docs_x.append(wrds)
23 docs_y.append(intent["tag"])
24
25 if intent["tag"] not in labels:
26 labels.append(intent["tag"])
Listing 1: File open
In Listing 2 we make all the words lowercase, and remove the character "?". After that we will
stem the words, in other words we find the root version of our words. This will help us to see the
dimension of the whole vocabulary not counting on duplicates.
1 words = [stemmer.stem(w.lower()) for w in words if w not in "?"]
2 words = sorted(list(set(words))) #Remove duplicates and sort my list of
words.
3
4 labels = sorted(labels) #Sorts labels list.
Listing 2: Text Normalization
5. Conclusion
The paper main goal is to present a fast and effective service to the tourist, who is visiting for
the first time a city, full of culture and connections. In this work we verified the feasibility
of a centralised service-oriented system aiming to the population of a Knowledge Base to be
integrated into a tourism chatbot. Further development involves the enhancement of natural
language processing algorithms to perform a data extraction based on page content, using the bot
only to automate navigation or to support data already extracted.
Acknowledgement. This paper has been produced with the financial support of the Project
financed by Campania Region of Italy "Synergy-net: ricerca e Digital Solutions nella lotta alle
patologie oncologiche” CUP B61C17000090007. "POR CAMPANIA FESR 2014/2020 – ASSE
1 – O.S. 1.2 Avviso “Manifestazione di Interesse per la Realizzazione di Technology Platform
nell’Ambito della Lotta alle Patalogie Oncologiche”Dec. N 460 DEL 28/11/2018. CINI"
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