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Artificial Intelligence for Virtual Assistant
IDOL for Virtual Assistant, is an AI (Artificial Intelligence) powered solution, that allows organizations
to offer their customers and employees access to information and processes using an automatedhuman-like operator which can engage in natural language dialogues.

To address an increasingly diverse
range of information requests,
IDOL uses our unique NaturalLanguage Question Answering(NLQA) technology to allow naturalconversations between end users
and a virtual assistant, to perform
queries against: curated (FAQ)
responses (Answer Bank), datatables and data services (FactBank), and unstructured documents(Passage Extraction); and tofollow configured processes tofacilitate knowledge discovery.

Question Answering
The information retrieval process is at firstglance well established—a user enters somesearch terms, a Boolean expression, a document, or a natural language query, and a setof relevant documents are returned—but inmany situations this workflow can be optimized
further. If a user enters a direct question, forexample, then it is generally more appropriate
to respond with a direct answer rather than adocument or documents that may contain theanswer. The technology must be able to understand, process, and answer direct questions tostreamline the retrieval process, which allows
information to be obtained in a more convenient and user-friendly fashion. Moreover, it
should offer an interface to allow configuration of that process to ensure existing humanknowledge is allowed to create and train thesystem to answer questions optimally. In general, there are three independent steps to accomplish this.

Answer Bank
Many administrators of support or user-help
systems have an existing set of frequently
asked questions that human support agentsare trained on, or the help pages are populated
with. For example, if a user encounters a particular problem on his mobile phone, then themanufacturer has established steps that theuser should follow to try to correct the problem. The system should be loaded with theseanswers to provide the best one when a given
question is asked or a particular problem isencountered. In addition, the system’s natural
language processing (NLP) should ensure thatdifferently worded variants of the same questions are all directed to the relevant answer.
This knowledge base is generally referred toas the answer bank.

Fact Bank
Users frequently have tables of information thatare commonly matched via structured (SQL)
queries. The system should be able to exploit

such databases of information to allow natural
language questions to receive direct factualanswers. This is achieved by processing andunderstanding the question and mapping it toan appropriate structured query that will in turnrespond with the desired answer. An exampleof this would be databases of financial prices
over time, allowing a query such as, “What wasthe EPS of HPQ in Q3 2016?” to receive theprecise answer. This database of structure information is generally known as the fact bank.
In addition, the system should have the ability to understand and extract information (forexample names, numbers) from unstructured
data such as free-form documents. It meansthat databases can be automatically populatedwith a rich set of structured data from a corpus
of unstructured documents. For example, ingesting a set of corporate annual reports could
create tables of financial data so that a question such as, “What was BP’s revenue in North
America in the second half of 2015?” will returnthe correct value pulled from such a table.

Passage Extraction
In many cases, the information requested issimply not present in either an FAQ data set
or a structured database, and an extended approach is required. It is at this point that the system must be able to process human information
effectively. Once again, the question should beprocessed to achieve a basic understanding

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Artificial Intelligence for Virtual Assistantof what is being requested and then, the system must form a query against its corpus tofind the most relevant documents. The system
must process the selected portions of thosedocuments to determine the short passage
that it believes most accurately answers theoriginal question. Statistical and probabilistic
thresholding is used to determine whether ananswer is relevant enough to answer the question, so that if a valid answer simply isn’t present in the corpus then none is returned. Onceagain, the system must have the language-handling capability to allow this process to work in
any language.
The above steps combine to form a powerfuldata analytics system for processing questions of any type. The typical workflow is to
first check whether the query is appropriate forquestion processing as many queries are notof this type. If it is, then it matches the question against the answer bank to see if a predefined answer has been set. If none is found,
it will then try the fact bank to see whether aprecise answer is present. And again, if no answer is found, then it will pass the query to thesystem’s unstructured index to see if an appropriate passage can be found that answers
the question.

How do I turn off roaming oniPhone® 7

Answer bank

FAQ
support documents

Fact bank

Structured datafrom structured andunstructured sources

Passageextraction

Document
repositories

Step by step instructions

What is HPQ’s Q3 FY16 EPS?
$0.48

What did critic X say about
movie Y?
Quote from a TV interview

Figure 1. Question answering in natural language

answer. However, large teams of human operators are costly and clients are often frustrated
by having to wait to talk to someone. So, how
can we accelerate the right information to theright user in the right context?
A Natural Language Question Answering system can be extended to create a more natural
system of customer services. Rather than asingle search box, an IM-style interface allows
a user to enter an initial question or problem
description. In many cases, the problem canbe answered directly and a single answer orsolution provided, but in many other cases, aconversation between the system and the user
is required to gather more information on how
to diagnose and solve the problem.
This can be achieved by training the systemwith classes of problems and the type of information that is needed in each case. The system
will then respond with follow-up questions until it believes it has enough knowledge to suggest a solution. Here is a very simple example:
user enters, “Cheapest ticket from London toBoston.” System asks, “Quantity, date and time,preferred airline?” User responds with answers.
System presents best options optimized for

Dialogue Systems
For many users contacting a company for help
with a problem or an inquiry, a search box is insufficient. Even if the system is capable of understanding and processing direct questions,the user is often unable to accurately describe
the problem or need or fails to provide enoughinformation. The natural response in such situations is to want to talk to a human operator
who will guide them to the correct solution or

2

Figure 2. Dialogue system•

I need the cheapest ticket from London to Boston•“Quantity?”, “Date and time?”, “Preferred airline?”•Ticket quantity, schedule, airline preference•Best options optimized for different parameters– Schedule, cost, mileage programConnect with Us
OpenText CEO Mark Barrenechea’s blog

different parameters: schedule, cost, mileage program.

Simple Curation of Question/Answer Pairs in Answer BankWhile it is natural to expect a manually intensive process for curating question/answer pairs(FAQs), OpenText™ IDOL comes with an intuitive
GUI tool to simplify such a process. This tool
allows administrator to easily refine the answers’ availability, accuracy and relevancy witha quick clicks.

Figure 3. Question/Answer pairs in answer bank261-000130-001 | O | 04/23 | © 2023 Open TextConnecting Relevant Information
to Humans
Technology is only as good as the value realized by its users. With the alluring promise of
Big Data, comes some of the biggest barriers
to success—user adoption and productivity.
True impact will come when humans can truly
connect to technology.
To learn more, please visit www.microfocus.
com/idolwww.microfocus.com/opentext