On a Chatbot Navigating a User through a Concept-Based Knowledge Model (2020)

Abstract: Information retrieval chatbots are widely used as assistants, to help users formulate their requirements about the products they want to purchase, and navigate to the set of items that satisfies their requirements in the best way. The work of the modern chatbots is based mostly on the deep learning theory behind the knowledge model that can improve the performance of the system. In our work, we are developing a concept-based knowledge model that encapsulates objects and their common descriptions. The leveraging of the concept-based knowledge model allows the system to refine the initial users’ requests and lead them to the set of objects with the maximal variability of parameters that matters less to them. Introducing the additional textual characteristics allows users to formulate their initial query as a phrase in natural language, rather than as some standard request in the form of, “Attribute - value”.

## introduction in recent years, a number of chatbots for products and services exploration has been proposed @xcite . chatbots are noticeably one of the most popular ai technologies across the world. from simplifying business workflows, enhancing employee and customer experience to reducing costs, chatbots provide numerous benefits for organizations of all sizes. besides customer service, chatbots support sales, marketing, it helpdesk, business intelligence, intranet, and hr. the use cases of chatbots are diverse and vary across departments and industries. most enterprise leaders are yet to understand and explore the full potential of chatbots. in spite of the efforts spent on scientific philosophy of chatbots, most are yet to produce systems capable of an efficient deployment in real-world environments. these efforts are mostly spent on a learning theory behind the conception of the chatbots. today's level of scientific knowledge is way ahead of the real-world deployment of chatbots. dialogue management is one of the bottlenecks of a successful chatbot, and regretfully each of the above domains requires a specialized dialogue structure. some dialogue structures are covered by discourse representations of the text being communicated. an exploration of products with features and attributes requires a distinct navigational structure such as lattice (a partially ordered set of pairs of product lists and features they share). in @xcite the authors have explored how a user can walk along a lattice picking a product he needs. once products and their features are available, we build a lattice and navigate it. however, features of products are not always available in explicit way of direct association, in a form of a database: frequently, they need to be extracted from text. in this paper, we extract associations between products and features from text. to do that, we first reveal product names as entities, together with their features and attributes, from a corpus of documents. then the noun phrases for products are aggregated, grouped, filtered, and cleaned. also, these noun phrases need to occur in opinionated expressions. moreover, we conduct an argumentation analysis giving a preference to product features being the subject of argumentative expressions of the users in their reviews. opinion mining or sentiment analysis detects customers' opinions, sentiments, emotions, appraisals, and attitudes towards products and services. chatbots employ nlp and sentiment analysis for multiple purposes including human-machine communication applied to business, education, and health. the paper is organized as follows. in section 2 we present the basic description of the proposed model. in section 3 we overview the algorithm for textual data preparation. in section 4 we give a brief overview of the proposed knowledge-based model. in the 5th section we describe the possible scenarios for navigation through the model. in section 6 we provide a few examples of the dialogue conducted by the proposed chatbot model. we conclude and give the directions for future work in section 7. ## model description information retrieval (ir) chatbots represent systems that provide web search in case of imprecise queries in specific domains. in many cases the user has only general idea about the product he wants to purchase, so, the goal of ir chatbots is to help the user to find the desired item, and simultaneously to refine his initial idea about the product. while the work of the standard ir chatbots is based on sending simple queries to the database and requiring the user to refine the set of predefined standard, or catalog, features (the price, e.g.), knowledgebased ir models have proved their effectiveness w.r.t. to the ability to interactively refine the query. for example, @xcite proposed lattice navigation chatbot model that incorporates a conceptbased knowledge model and an index-guided traversal through it to ensure the discovery of information relevant for users and coherent to their preferences. this chatbot not only supports a search session, but also helps users to discover properties of items and sequentially refine an imprecise query. however, it enables the chatbot to process only the standard features, and ignores text descriptions of an object, which could allow the users to grasp some additional characteristics of the product he wants to buy. in our work we propose to enlarge the proposed in @xcite knowledge model with the additional characteristics, extracted from textual descriptions of the products. the online stores usually contain a database of digital reviews, where a person may refer to find some additional information about the product. if we encapsulate this kind of information into the knowledge model, the system will be able to provide more additional characteristics to the user, while the user can formulate his requests not just in the "attribute -value" form, but also as the phrase in natural language. the pipeline shown in figure 1 illustrates the scheme of the proposed ir system. it combines several blocks of data preparation responsible for retrieving the informative features from the text and enlarging the catalog data, building the lattice-based knowledge model, and navigation through the lattice with the chatbot to retrieve the information relevant for the user. 3 textual data preparation ## combination of the textual and catalog features and building the knowledge model in the previous sections we described the preparation of text data that can be extracted from the users' reviews (or some other text descriptions of the product). thus, we enlarge standard characteristics of the product that could be revealed from the database with its text descriptions. based on this data we propose to build the conceptual knowledge model that will represent the data in the form of non-overlapping hierarchically organised groups of objects and their common descriptions. we have already implemented the similar approach in data aggregation block (section 3.3.1). to build this knowledge model we also leverage pattern structures, whereas, now the set of objects is the real set of the products available in the dataset, and the description is the row in this dataset containing all the features that the system possesses about the product: the numerical, categorical, binary, and textual ones. based on the constructed knowledge model, the chatbot walks through the lattice and navigate the user to the group of the objects that satisfies his initial query. simultaneously the chatbot assesses the variability of the items inside the closed concept and proposes some attributes for the refinement. in the framework proposed in @xcite the authors assumed that the user specifies only the numerical characteristics of the product, and the chatbot navigates the user and refines only this type of attributes, such as price, etc. now, we can take into account both the numerical and text description of the product, and, thus, to expand the user's scope to formulate his query. ## navigation procedure once the conceptual model is built, the chatbot utilizes this information and traverses the model based on the users' requests. at the beginning of the search, the user formulates the initial query about the product he wants to purchase. this query may contain object and attribute names, values of the attributes, or object description formulated as some phrase. as a result of running the keyword query, we get the set of objects and their descriptions the user has requested, (o q , d q ), where o q is a set of objects satisfying this query, and d q is the description specified by the user. from this pair, we identify the initial lattice node (o 0 , d 0 ) whose description satisfies d q . if the user mentioned some numerical features, the system matches the numerical values from d q with the corresponding values in d 0 . otherwise, if the user inserted a phrase, the system retrieves its noun phrases and matches it with the textual description of the products inside a concept (o 0 , d 0 ). here we present a model of the interactive search where the chatbot clarifies the user needs in the course of navigation. the chatbot looks for the concept, which satisfies the initial user's request and simultaneously keeps the variability of the attributes that the user did not refine. the specification (updating the set of constraints) for the queries continues until the user found no more appropriate specification or a product that corresponds exactly to what he searched for. once (o 0 , d 0 ) is identified, we fix the lattice node and the chatbot shows the user its current position in the lattice (o i , d i ). the chatbot calculates the diversity of the features inside the concept @xcite . if the feature is diverse then the chatbot asks the user, if he wants to refine it. then at each iteration i the user is expected to request one of the following: ## simulation of the dialogue in this section, we simulate the interaction of a chatbot with a user, where the knowledge model is built based on the information obtained from the small dataset presented in below we present two dialogues with the chatbot that walks through the constructed knowledge model and helps the user to purchase a tablet based on his requests. ## conclusion in the work, we have introduced a conceptual-based ir chatbot that is able to process both the standard numerical features of an object and the features retrieved from some textual descriptions. our work is motivated by the fact that utilizing a clear navigational structure such as lattice improves the performance of ir systems. despite the fact that the ir systems utilizing these structures exist, none of them combines the standard numerical features with the complex features of the product that can be extracted from texts. so, in our work, we are concentrated on retrieving text features from the dataset of users' reviews, which are a great source of additional information about an item. the pipeline we introduced combines three steps of text data preparation, where we filter, extract, and aggregate the text descriptions. thus, the final text characteristics of the object are obtained only from the argumentative texts that provide the reasons and motivation rather than plain sentiment. the features themselves are aggregated into the groups in accordance with the percentage of the reviewers that have used them to describe a product. the introduction of the textual features to the product description enables the chatbot to process not just the standardized users queries written in the "attribute -value" form, but also the requests formulated in natural language. we also presented the simulation of the dialogue that illustrates possible scenarios of the user-chatbot interaction. while we remark that this is a preliminary study, and in the future works we will present the comparison of the proposed framework with the existing models of ir chatbots. below are the concepts that the chatbot has passed during the communication with the user in the first and second dialogues respectively.