# GraphWOZ: Dialogue Management with Conversational Knowledge Graphs arXiv: https://arxiv.org/abs/2211.12852 ยท HTML: https://mlsanity.com/paper/2211.12852/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 34 of its 54 references that are in the map. - First posted on arXiv 23 Nov 2022. ## Builds on (most cited first, 30 of 34) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019) - [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018) - [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016) - [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017) - [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://mlsanity.com/paper/1909.05358/) (2019) - [Key-Value Retrieval Networks for Task-Oriented Dialogue](https://mlsanity.com/paper/1705.05414/) (2017) - [A Knowledge-Grounded Neural Conversation Model](https://mlsanity.com/paper/1702.01932/) (2017) - [Task-Oriented Dialogue as Dataflow Synthesis](https://mlsanity.com/paper/2009.11423/) (2020) - [Language and Visual Entity Relationship Graph for Agent Navigation](https://mlsanity.com/paper/2010.09304/) (2020) - [A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions](https://mlsanity.com/paper/2105.11644/) (2021) - [Tracking the World State with Recurrent Entity Networks](https://mlsanity.com/paper/1612.03969/) (2016) - [Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2009.13656/) (2020) - [Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base](https://mlsanity.com/paper/1910.05069/) (2019) - [Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning](https://mlsanity.com/paper/2004.14224/) (2020) - [Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks](https://mlsanity.com/paper/2104.01569/) (2021) - [Augmenting End-to-End Dialog Systems with Commonsense Knowledge](https://mlsanity.com/paper/1709.05453/) (2017) - [Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation](https://mlsanity.com/paper/2106.05894/) (2021) - [Towards Socially Intelligent Agents with Mental State Transition and Human Utility](https://mlsanity.com/paper/2103.07011/) (2021) - [Conversation Graph: Data Augmentation, Training and Evaluation for Non-Deterministic Dialogue Management](https://mlsanity.com/paper/2010.15411/) (2020) - [AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning](https://mlsanity.com/paper/1905.11259/) (2019) - [Conversational Entity Linking: Problem Definition and Datasets](https://mlsanity.com/paper/2105.04903/) (2021) - [Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs](https://mlsanity.com/paper/2206.07296/) (2022) - [Neural Response Ranking for Social Conversation: A Data-Efficient Approach](https://mlsanity.com/paper/1811.00967/) (2018) - [PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking](https://mlsanity.com/paper/1909.01296/) (2019) - [SlugNERDS: A Named Entity Recognition Tool for Open Domain Dialogue Systems](https://mlsanity.com/paper/1805.03784/) (2018) - [Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2012.11937/) (2020) ## Built on by (most cited first, 5 of 5) - [Towards a Neural Era in Dialogue Management for Collaboration: A Literature Survey](https://mlsanity.com/paper/2307.09021/) (2023) - [Prior Lessons of Incremental Dialogue and Robot Action Management for the Age of Language Models](https://mlsanity.com/paper/2501.00953/) (2025) - [EventWeave: A Dynamic Framework for Capturing Core and Supporting Events in Dialogue Systems](https://mlsanity.com/paper/2503.23078/) (2025) - [Frame of Reference: Addressing the Challenges of Common Ground Representation in Situational Dialogs](https://mlsanity.com/paper/2601.09365/) (2026) - [Retrieval-Augmented Neural Response Generation Using Logical Reasoning and Relevance Scoring](https://mlsanity.com/paper/2310.13566/) (2023) ## Abstract (arXiv) We present a new approach to dialogue management using conversational knowledge graphs as core representation of the dialogue state. To this end, we introduce a new dataset, GraphWOZ, which comprises Wizard-of-Oz dialogues in which human participants interact with a robot acting as a receptionist. In contrast to most existing work on dialogue management, GraphWOZ relies on a dialogue state explicitly represented as a dynamic knowledge graph instead of a fixed set of slots. This graph is composed of a varying number of entities (such as individuals, places, events, utterances and mentions) and relations between them (such as persons being part of a group or attending an event). The graph is then regularly updated on the basis of new observations and system actions. GraphWOZ is released along with detailed manual annotations related to the user intents, system responses, and reference relations occurring in both user and system turns. Based on GraphWOZ, we present experimental results for two dialogue management tasks, namely conversational entity linking and response ranking. For conversational entity linking, we show how to connect utterance mentions to their corresponding entity in the knowledge graph with a neural model relying on a combination of both string and graph-based features. Response ranking is then performed by summarizing the relevant content of the graph into a text, which is concatenated with the dialogue history and employed as input to score possible responses to a given dialogue state.