Taxonomy Extraction for Customer Service Knowledge Base Construction

Abstract

Customer service agents play an important role in bridging the gap between customers' vocabulary and business terms. In a scenario where organisations are moving into semi-automatic customer service, semantic technologies with capacity to bridge this gap become a necessity. In this paper we explore the use of automatic taxonomy extraction from text as a means to reconstruct a customer-agent taxonomic vocabulary. We evaluate our proposed solution in an industry use case scenario in the financial domain and show that our approaches for automated term extraction and using in-domain training for taxonomy construction can improve the quality of automatically constructed taxonomic knowledge bases.

Conversation Concepts: Understanding Topics and Building Taxonomies for Financial Services

“…The process of extracting a knowledge graph is essentially the same as that of ontology learning [12] and consists of three main stages: firstly, the relevant entities or terms must be identified, in a process called automatic term recognition, then the relations between these terms must be constructed, and finally, these must be organized into a single coherent knowledge graph. For the first task of extracting terms, a number of heuristic methods have been developed based on the frequency of terms and syntactic patterns [2,13,14]. Relation extraction approaches were initially based on the discovery of patterns that are indicative of relations, such as the patterns proposed by Hearst [15]; however, recently, new distributional methods have become popular for extracting relations [16], which rely on word embeddings to predict relations between entities, and authors have explored combining pattern-based and distributional methods [17] or extending the results to multiple relations [18].…”

“…Relation extraction approaches were initially based on the discovery of patterns that are indicative of relations, such as the patterns proposed by Hearst [15]; however, recently, new distributional methods have become popular for extracting relations [16], which rely on word embeddings to predict relations between entities, and authors have explored combining pattern-based and distributional methods [17] or extending the results to multiple relations [18]. Taxonomy construction extends the task of term and relation extraction to a holistic approach, and initial results have shown that this is a very hard task [19]; until recently, very few approaches have improved on the state-of-the-art [2,20].…”

“…Given the requirements for the CCAI tool, it was clear that current taxonomy extraction solutions were not suitable to realise its goals. As such, we further developed the Saffron system [2], to support the development of knowledge graphs within FMR LLC. In particular, key requirements that were identified were the extension of the automatic extraction system to cover more sophisticated relation types, as well as the development of tools to support the evolution of the knowledge graph.…”

“…The CCAI tool builds on NLP components provided by the Saffron system [2]. This tool consists of a number of steps: firstly, term extraction, where terms are identified using parts-of-speech patterns and then ranked as relevant terms according to some metrics (for more details, see https://github.com/insight-centre/saffron/wiki/4.1.-Term-Extraction (accessed on 9 April 2021)).…”

“…This collaboration builds firstly on the work of the Insight SFIResearch Centre for Data Analytics at NUI Galway's experience with the creation of knowledge graphs and, in particular, the system called Saffron [2]. This system is designed to create a taxonomy automatically from a large text corpus and works by first identifying all the terms using syntactic and corpus frequency information.…”