Identifying F rame N et Lexical Semantic Structures for Knowledge Graph Extraction from Financial Customer Interactions (2023)

Abstract: We explore the use of the well established lexical resource and theory of the Berkeley FrameNet project to support the creation of a domain-specific knowledge graph in the financial domain, more precisely from financial customer interactions. We introduce a domain independent and unsupervised method that can be used across multiple applications, and test our experiments on the financial domain. We use an existing tool for term extraction and taxonomy generation in combination with information taken from FrameNet. By using principles from frame semantic theory, we show that we can connect domain-specific terms with their semantic concepts (semantic frames) and their properties (frame elements) to enrich knowledge about these terms, in order to improve the customer experience in customer-agent dialogue settings.

## introduction improving customers experience is a desirable task for companies. this can be tackled at different levels: improving the accuracy of the information given to solve a query or a problem, improving the speed of the information given, improving the response time to solve the problem, and, going further into the experience, recommending a relevant service or product to a customer. extensive understanding of the domain and the customer needs is required for improving customer satisfaction. with this motivation in mind, @xcite explored the use of a domain-specific taxonomy of terms built from customer-agent dialogues without manual input. the resource was built to be used as an intermediate and complementary resource, with the aim to contribute towards improving the efficiency of customer service agents, leveraging the issue of prior domain knowledge necessary in digital conversational agents (dcas), and bridging the gap between the experts knowledge and the customer needs. in this paper, we propose to go a step further by integrating semantic knowledge into the taxonomy and moving towards a knowledge graph (kg). more specifically, we use semantic knowledge from the well established resources from the berkeley framenet project foot_0 and the frame semantic theory it follows @xcite . framenet provides a database of concepts following lexical semantic structures, as well as a dataset of sentences annotated following these structures. semantic frames, referred to as frames in this paper, make up the core of framenet. they represent situations, objects or events, and are given a label to represent them. each frame depends on one or more core (ie. essential to the meaning of a frame) and non-core (ie. non-essential to the meaning of a frame) arguments, the frame elements (fes). while frames and fes are at the conceptual level, the lexical unit (lu) is the realisation of these concepts as words: the lu is said to evoke a frame or fe. for example, the request frame describes a common situation involving a speaker, an addressee and a message (the content of the request), and is evoked by words such as "demand". for example, this frame is evoked in the sentence "the customer demanded a refund.". the frame semantic theory is useful to understand the deeper meaning of terms and what they depend on, and this is why, for this work, we decided to integrate frames and fes into a kg, with the aim of further improving the customer-agent dialogue experience. our approach is data-driven and domain independent, therefore flexible and adaptable to new data, and relies on a rich resource widely recognised and used over the years. these characteristics of our method allow for a wide range of applications in a variety of domains. a typical example of use is in question-answering systems, eg. who did what and to whom? (identified through fes). furthermore, kgs in general are beneficial to many applications, from text classification @xcite to recommender systems @xcite or chatbot development @xcite . in this paper we test our approach in the context of a financial customer-agent interaction setting. after exploring the related work on the use of framenet in information extraction tasks and kg construction (section 2), we describe the data used in our experiments (section 3). we then detail our approach to extract the framenet concepts (frames and fes) from our dataset of financial customeragent interactions, integrate them in a kg, and describe the rdf model design to generate the final kg (section 4). in section 5, we present the results of the kg creation as well as the evaluation, and discuss the results and challenges faced. finally, we present some directions for future work in section 6. ## related work the use of framenet in natural language processing tasks has been widely explored, specifically in the context of information extraction (ie), semantic role labelling (srl), and frame semantic parsing (fsp) (ie. extracting frame-semantic structures from textual data). in terms of fsp systems, das et al. @xcite presented the first computational and statistical model for frame-semantic parsing. in the semeval-2007 task 19 @xcite , the goals of the frame semantic structure extraction task were to recognise words and phrases that evoke semantic frames, label them, identify and label their arguments, and integrate them into an overall semantic dependency graph. three groups submitted results, with only one submitting full results, while the others submitted only the frames identification step. several systems have then followed, including one of the latest, opensesame @xcite . the authors use a softmax-margin segmental rnn, i.e. a combination of bidirectional rnns with a semi-markov conditional random field (crf), to segment and label the sentence relative to each frame @xcite . framenet has also been used for relation extraction and kg construction tasks. @xcite describe their approach using linked open data and combining framenet and sar-graphs, knowledge resources that connect semantic rela-tions from factual knowledge graphs to a linguistic phrases. @xcite explore relation extraction through exploiting frame element and frame annotations, and takefive @xcite uses also verbnet for the semantic role labeling. fred @xcite generates an rdf graph representations out of the data extracted from text (for each sentence of the input text) using deep semantic parsing, verbal event detection, semantic role labeling with verbnet and framenet roles. spikes @xcite is another similar tool of the state-of-the-art from 2016, extracting rdf triples of sentences using framebase, a semantic web ontology derived from framenet. it is based on a two step process: first the linguistic feature extraction to build a linguistic-oriented structured representation of the text (graph), and second the knowledge distillation, which combines structured information to build a knowledge graph made of instances of events and entities. all these systems either extract relations only, or they construct a full kg out of each sentence, while our aim is to represent a full domain and lexical semantic structures associated to it, not each individual piece of text. we are therefore not interested in the semantic representation of individual sentences. additionally, framenet has been used in domain-specific tasks. with the spinet system, ferreira and pinheiro (2020) describe how they use the principles of framenet coupled with the specific domain knowledge of the mesh thesaurus foot_1 to extract information and classify sentences about spine and its disease to their semantic types. they identified 4 frames (condition symptom relation, medical intervention, cure and medical conditions) and fes such as disease, treatment, organism function relevant to this domain and their dataset. in the area of customer-agent interaction in the financial domain, @xcite made a first step towards a data-driven system for knowledge graph extraction, by using the tool saffron foot_2 for the creation of a domain-specific taxonomy of terms, without the need for a domain-specific lexical resource. in this paper, we extend this approach by using the lexical semantic structures from framenet and automatically extracting and adding domainrelevant relations and concepts to the taxonomy, in the aim to create a kg. in our experiments, we use three resources: a proprietary dataset of chatlogs, the open source framenet annotated dataset, and a taxonomy of terms extracted from the chatlog dataset using the saffron tool. ## methodology we describe in this section our approach to create a kg by enriching a domain-specific taxonomy with framenet-based information. new links and nodes are added to the taxonomy, that contain information about frames and fes corresponding to the terms in the taxonomy and the content of the input dataset. the whole pipeline can be visualized in figure 3 . ## conclusion and future work in this work, we combine the strength of the term extraction and domain taxonomy generation capabilities of the saffron tool, with lexical semantic structures from framenet and the opensesame tool to create a kg from financial customer interactions in an unsupervised manner and without the need of a domain specific lexical resource. we have observed challenges to overcome, such as the ambiguity and incorrect frames identification increased by the single word limitation, as well as the lack of some relevant semantic concepts. we have contributed towards constructing a data-driven fully unsupervised and domain-independent system for kg extraction in domain-specific settings. we have identified a number of semantic concepts from framenet with their arguments related to the financial domain, and enriched a taxonomy, in the aim of improving the customer-agent interaction. there is no other system, to our knowledge, that creates a domain kg from terms, includes taxonomic relations and lexical semantic structures, all based on a dataset of unstructured textual data. in future work, we want to optimise the application and accuracy of opensesame in our approach, as well as building a fully automated method where human intervention is not required anymore. the processing time has proven to be significantly long on our dataset, due to the output format chosen by the tool not optimal for processing large datasets. framenet being a collaborative project, we also intend to contribute with the proposal of new frames to cover the missing concepts, as well as to provide new annotations of texts from our domain of interest. also, our system does not currently deal with negation in the text, which would be an important feature to take into account. finally, we would like to work further on the issue of single word lu and the ambiguity it entails.