Integrating Conversational Entities and Dialogue Histories with Knowledge Graphs and Generative AI (2025)

Abstract: Existing methods for storing dialogue history and for tracking mentioned entities in spoken dialogues usually handle these tasks separately. Recent advances in knowledge graphs and generative AI make it possible to integrate them in a framework with a uniform representation for dialogue management. This may help to build more natural and grounded dialogue models that can reduce misunderstanding and lead to more reliable dialogue-based interactions with AI agents. The paper describes ongoing work on this approach.

## introduction recognising conversational entities and maintaining dialogue history are two crucial tasks in natural language understanding for dialogue management. they help to maintain dialogue context and support coherent continuation of the dialogue. the two tasks have traditionally been clearly separated. recent advances in knowledge graphs and generative ai suggest possibilities for integrating them into a model that not only facilitates the dialogue processing with knowledge that focuses on a particular domain and contributes to the relevance and reliability of the information, but simultaneously serves as a uniform representation for storing the dialogue history. this paper presents ongoing work on a prototype design of dialogue history graphs. while domain knowledge graphs store relatively static, long-term information about well-established domain entities and relationships between them, we use rapidlychanging, short-term graphs to represent dialogue history, including all mentioned conversational entities even if they do not necessarily correspond to known entities in the domain graph. we use llms to split dialogue turns into smaller semantic units and to extract conversational entities. we present examples from recent results, with the aim that this approach will improve conversational grounding, reduce misunderstandings, and result in more reliable spoken interaction with ai agents. the paper is structured as follows. section 2 surveys related work on conversational entities and dialogue histories. section 3 describes our work on representing the structure, content and history of dialogues in knowledge graphs. we use an example dialogue, shown in ## related work this section surveys how conversational entities and dialogue histories are handled by existing conversational ai frameworks. entity recognition helps to build semantic representations of user intents. dialogue history records which entities have been talked about, to maintain coherence of the dialogue as it progresses towards the goal. ## dialogue history graphs in earlier work we used knowledge graphs to store domain information for spoken dialogue systems, and showed that more cooperative responses can be generated by adding semantic metadata such as taxonomies extracted from wikidata to the domain graphs @xcite . more recently we proposed a new role for knowledge graphs, aiming to reduce dialogue errors by better support for conversational grounding. this approach (jokinen and wilcock, 2025) uses both domain knowledge graphs and dialogue history graphs. the aim is to explore how to improve conversational grounding by constructing shared knowledge via entity linking. dialogue history has not usually been stored in knowledge graphs. nevertheless, there are potential benefits from using graphs to store dialogue history, as already mentioned in section 2. these include flexibility of entity tracking, domain switching, and the use of graph formalism for search and update of knowledge. another benefit is to provide a flexible dialogue memory for cooperative dialogues and enable conversational grounding to help building of a shared context within which to enhance mutual understanding, clarify vague utterances, and resolve misunderstandings. recently there have been significant advances in graph-based retrieval from documents that go beyond basic rag retrieval. one example is graph rag for local and global summarization over large document collections @xcite and another advance is graphreader @xcite for retrieval from long documents such as books. graphreader represents documents using graphs with a generic schema. document nodes are linked to text chunk nodes. text chunk nodes are linked to "atomic fact" nodes. atomic fact nodes are linked to "key element" nodes. @xcite suggests that using this generic schema enables the approach to be applied to documents in any domain. we propose a similar approach for representing the structure, content and history of dialogues, with the intention that a generic dialogue schema will be applicable to dialogues in any domain. this aligns with early dialogue research like ravenclaw @xcite which emphasises the separation of task content and dialogue skills. figure 2 shows the four layers in the generic schema for a dialogue history graph. dialogue nodes (blue) in the top layer are linked to turn nodes (red) in the second layer. turn nodes are linked to semantic unit nodes (yellow) in the third layer. semantic unit nodes are linked to conversational entity nodes (green) in the fourth layer. dialogue history graphs grow incrementally with each new dialogue turn. figure 2 shows the state of the graph after the first human turn (what is hypertension?) and the ai response. at this point in the dialogue there are only two turn nodes. the complete dialogue history graph for the example dialogue in when each turn is added to the dialogue history graph, its content is split by an llm into semantic units and mentioned conversational entities. the prompts for these tasks are inspired by the prompts for extracting atomic facts and key elements in the graphreader approach to document processing @xcite . finally, conversational entities can be grounded to real-world entities in a domain knowledge graph, which represents the state of the world (conceptual structure of the world combined with immediate information from external sensors). of course, con-figure 3 : an overview of the dialogue history graph for the full dialogue in ## evaluation this paper describes ongoing work for which user evaluation has not yet been possible. results for a qualitative evaluation are shown in appendices. appendix a lists all semantic units extracted from response turns. the turns are correctly split into semantic units, and the pronouns are correctly disambiguated. the semantic units correspond to conceptual propositions expressed in the surface turn, which can be further used as a basis for search, translation, or summarization. appendix b shows examples of the mentioned conversational entities and linked semantic units. the entities are in general correctly extracted from the dialogue turns that mention them, but they are not always linked to the correct semantic unit within the turn. this requires improvements to the prompts that guide the llm in this task. ## conclusion and future work most existing approaches to dialogue histories and mentioned entities in spoken dialogue systems keep them separate. while acknowledging the differ-ent logics behind them, the paper describes an approach that integrates them based on recent advances in knowledge graphs and generative ai. such integration has several benefits for dialogue managent and we expect dialogue graphs also to facilitate research on conversational grounding, where even modest improvements could reduce misunderstandings and lead to more reliable spoken dialogue interactions with ai agents and robots. for instance, recent studies have investigated the use of knowledge graphs to bridge information gaps in dialogues with grounded exchanges @xcite , and the capacity of llms to build shared knowledge by classifying grounding dialogue acts and by extracting mutually grounded information @xcite . future work will include improvements to the prompts to help the llm to more carefully link conversational entities extracted from the turns to the specific semantic units that mention them. finally, it is crucial to enhance work in automatic building of knowledge graphs. as discussed in @xcite , conversational entities can be used to link to entities in a domain graph, or to build new domain graphs, with verbs representing relationships. given the potential of learning about the world through interaction, future research will also focus on how conversational grounding can facilitate acquiring knowledge by interaction and help to build effective knowledgebases.