Ontology Population Reusing Resources for Dialogue Intent Detection: Generic and Multilingual Approach (2021)

Abstract: This work presents a generic semi-automatic strategy to populate the domain ontology of an ontology-driven task-oriented dialogue system, with the aim of performing successful intent detection in the dialogue process, reusing already existing multilingual resources. This semi-automatic approach allows ontology engineers to exploit available resources so as to associate the potential situations in the use case to FrameNet frames and obtain the relevant lexical units associated to them in the target language, following lexical and semantic criteria, without linguistic expert knowledge. This strategy has been validated and evaluated in two use cases, from industrial scenarios, for interaction in Spanish with a guide robot and with a Computerized Maintenance Management System (CMMS). In both cases, this method has allowed the ontology engineer to instantiate the domain ontology with the intent-relevant information with quality data in a simple and low-resource-consuming manner.

## introduction nowadays, the arrival of new technologies and virtual assistants (e.g., siri, alexa) prove that naturally interacting with different devices and applications is a reality that is constantly improving through time. in many contexts, such as industrial scenarios, this fact has contributed to an increasing demand of technologies that allow workers to naturally communicate (i.e, without relying on specific constructions or keywords) with industrial systems such as robots, machines or information systems so as to increase productivity and security @xcite , among other aspects. a common approach to support natural communication between humans and machines are dialogue systems. when the goal is for the system to perform a specific action requested by the user, task-based dialogue systems are used @xcite . as stated in @xcite , a wide range of modern commercial taskoriented dialogue systems are to some extent inspired in the genial understander system (gus) architecture in @xcite . this architecture consists of frame structures, that model the situations to be invoked by the user -the intentsand the required slots -arguments-to be provided for each frame. to determine the frame to be selected for a specific request, an intent detection step is needed. the main research goal is to develop a generic semantic-based task-oriented dialogue system inspired by gus, in which both the dialogue process and the domain modelling are conceptualized in a generic manner by making use of ontologies foot_0 . the focus in this paper is the population process of the domain ontology to efficiently perform intent detection, minimizing human intervention and linguistic knowledge when adapting the dialogue system to specific use cases. communication based on natural language can use a variety of words and expressions to convey the same meaning. therefore, a dialogue system that interacts with users must be able to recognize different words that evoke the same situation or refer to the same entity to perform successful intent detection 2 . in an ontology-based approach, to populate the domain ontology with this information, it is very important that the maximum number of these possibilities is considered. doing so manually would be a high time-and resource-consuming task, and the tendency is to apply semi-automatic or automatic techniques @xcite . according to this tendency, this work presents a generic strategy that aims to overcome the challenge of instantiating the domain ontology with intents and their trigger words by semiautomatically exploiting existing lexical resources, without reducing the quality of the results and decreasing time and costs in the adaptation to different scenarios and applications. the result, thus, is a semi-automatic process which leverages existent multilingual lexical resources to ( 1 ) be able to model the intents specific to a use case by using existing semantic frames @xcite ) and (2) obtain the maximum number of words that trigger them in different languages -i.e., their translation equivalents-, so as to instantiate the domain ontology of the dialogue system approach previously described. this process has been validated in two use cases from industrial scenarios. the rest of the paper is organised as follows: section 2 will provide relevant related work. section 3 will describe the ontology population strategy. section 4 will present the validation and evaluation of the strategy through 2 use cases, and section 5 will provide conclusions and further work. ## related work there is a wide range of works in the literature that deal with intent detection, as it is one of the core tasks in the interpretation component in taskoriented dialogue systems @xcite . in this sense, two main approaches to perform intent detection can be distinguished: on the one hand, approaches that rely on machine learning (ml) methods and, on the other hand, approaches that make use of ontologies. for both methodologies, a fair amount of data is needed and, thus, cannot cope with scenarios with limited or nonexistent labeled data, which implies the necessity of performing manual work, which is resource-consuming @xcite . for machine-learning-based methods, several approaches can be observed in the literature that make use of traditional ml algorithms such as support vector machines @xcite or logistic regression @xcite . however, modern approaches employ deep learning (dl) methods @xcite . as mentioned previously, ml-based approaches require of a fair amount of data to perform training tasks, especially dl models. for ontology-based methods, the population of the domain ontology to perform intent detection is generally based on patterns, which can be obtained automatically or semi-automatically. the formalization in @xcite , who have designed an intent detection system for emails in french, is inspired in semantic frames, and the population of the domain ontology is based on a set of mainly automatically-generated patterns that allow to determine the words that trigger the intents. however, to obtain these patterns, a large amount of data was collected and processed. on the other hand, @xcite present a dialogue system in which intent detection in health-related texts is performed. as in the previous case, the authors also rely on patterns to determine key and dependent concepts, which are considered to determine the intent in a sentence. also, the information related to patterns is obtained combining expert knowledge and automatic methods. considering the previous remarks, intent detection seems to require either a large volume of data or manual work. in ontology-based approaches, for the former, the task of instantiating the domain ontology in low-resourced scenarios is severely limited and, for the latter, the use of manual expert knowledge is time and resource consuming. in this sense, a well-known challenge to the semantic web community is how to reduce the amount of manual work in the ontology population task. as noted by @xcite , most approaches for ontology population make use of textual input and rely on natural language processing techniques to obtain the necessary knowledge to populate the ontology @xcite , whereas approaches that rely on data that is structured at some degree -which are of interest for this work-are less common @xcite . considering this, the authors in @xcite use structured knowledge in linked data for ontology population. the approach presented in this paper aims to overcome these challenges providing a methodology that could be used in scenarios with a lack of data with a relative minimal effort, without depending of expert linguistic work, and by making use of already available structured knowledge. ## generic strategy for ontology population for intent detection the aim of the generic strategy is to support ontologists in the process of task-oriented dialogue system domain ontology population, minimizing the manual work, reusing linguistic resources, but without expert knowledge of the linguistic field. the core linguistic resource used for collecting the necessary information for the ontology population process is pm, since it integrates and maps, among others, the information contained in framenet, sumo and the mcr-the resources that include most of the relevant data for this work. the strategy will guide the ontologist through the selection of the relevant information within pm, and will gather, semi-automatically, the relevant intents (through frames), the associated trigger words (through lexical units) and their corresponding synonyms for a specific use case in which the dialogue will take place. considering this, the main steps of the strategy are the following: 0. use case characterization. the identification of the different situations (associated 3 equivalence ("="), subsumption ("+") and instantiation ("@") mapping relations @xcite . to intents) that surround the use case is performed. for example, for a guide robot, the situations involved may be to be taken to a given place or just to present information. ## validation and evaluation the process has been implemented as an api rest and has been tested in two use cases: a guide robot scenario and a computerized maintenance management system (cmms), both for the spanish language. the domain ontology used is the domain module for task-oriented dialogue management ontology (todo) -tododomfoot_4 , as it is an ontology that provides a top model for domain modelling for task-based dialogue systems. the implementation of the methodology to instantiate the ontology described in this work applied to these use cases will be detailed in the following lines. ## conclusions this work focuses on the intent detection step in an ontology-based task-oriented dialogue system, which requires a modelling of the intents into the domain ontology. since the intents and the words that trigger them vary depending on the use case, the task of instantiating the domain ontology with such information every time a new use case is needed is time and cost consuming. the paper has presented a generic, semiautomatic strategy that reuses existing multilingual lexical resources to assist ontology engineers in modelling the specific intents and the words that elicit them for a given use case. this method consists of a preliminary characterization of the use case, a selection of relevant framenet frames, lexical units in english and sumo tags and an automatic data gathering for intents given the previously selected data. the result is a structured set of intents, their corresponding frames and the words that evoke them. finally, and depending on the modelling of the target domain ontology, a sparql query is generated, to be used to instantiate it. to prove the generality of the approach, this strategy has been validated in 2 different industrial use cases: interaction in spanish with a guide robot and with a cmms. moreover, an evaluation in terms of precision, recall and f1 has been performed. this evaluation validates the steps described in this work and their positive contribution to instantiate valid intent-related information with a lower resource consumption than doing so manually. further work includes research oriented to the improvement of the quality of the data extracted. as stated previously, pm only includes frames that have verbs as lexical units, leaving out other frames, such as information, that have nominal or adjectival lexical units. in this line, it may be interesting to explore other sources of information that allow to obtain information about these missing frames and in different languages, in a similar manner that pm does with frames with verbal lexical units.