The recently introduced task of Conversational Entity Retrieval from a Knowledge Graph (CER-KG) presents unique research challenges due to the complexity of the queries along with the necessity to consider KG structure and the context of an information-seeking dialog. This paper proposes a novel approach to CER-KG that first constructs a sub-graph around each candidate response entity, which includes its neighboring KG components, such as other entities, literals, categories and predicates, and then scores and ranks each candidate answer entity with Diverse Relevance signal Aggregation via Graph cONvolution (DRAGON), a novel learning-to-rank neural architecture for CER-KG. Unlike previous approaches to CER-KG, DRAGON directly takes a large number of fine-grained relevance signals as input and learns to effectively aggregate and transform those signals into the ranking scores of candidate response entities. In particular, a set of sparse and structured vectors of relevance features used as input to DRAGON measure lexical and semantic similarity between a query in the current turn or responses from the past turns of an information-seeking dialog and each node in the candidate response entity's sub-graph. DRAGON then propagates the relevance signals in feature vectors around the sub-graph using graph convolution layers and aggregates those signals into the candidate response entity ranking score with multi-head attention and fully-connected layers. This design enables DRAGON to attenuate noisy relevance signals from the local KG neighborhood during propagation and attend to the signals from the most important nodes in the candidate entity sub-graph. Our results demonstrate that DRAGON yields significant gains in retrieval accuracy over the previously proposed approach for CER-KG and performs comparably to a much larger fine-tuned cross-encoder architecture.

Conversational information-seeking systems increasingly rely on neural architectures and large language models (LLMs), yet they remain limited in their ability to retrieve structured, contextually relevant knowledge from large-scale knowledge graphs (KGs). My doctoral research addresses this gap through the novel task of Conversational Entity Retrieval from a Knowledge Graph (CER-KG)—the problem of identifying the correct KG entity in response to a context-dependent query within a multi-turn dialog. To support reproducible research, I introduced QBLink-KG, the first benchmark for CER-KG, adapted from conversational reading comprehension data. Building on this foundation, I proposed two neural ranking architectures: NACER, which aggregates lexical and semantic relevance signals from the local KG neighborhood, and DRAGON, which employs graph convolution and self-attention to model fine-grained, dialog-aware relationships across KG components. DRAGON achieves significant performance gains demonstrating the effectiveness of integrating graph-structured reasoning with conversational context modeling. Collectively, this research advances context-sensitive, structure-aware retrieval, bridging symbolic reasoning from KGs with neural representation learning for conversational information access.

This paper introduces a novel information retrieval (IR) task of Conversational Entity Retrieval from a Knowledge Graph (CER-KG), which extends non-conversational entity retrieval from a knowledge graph (KG) to the conversational scenario. The user queries in CER-KG dialog turns may rely on the results of the preceding turns, which are KG entities. Similar to the conversational document IR, CER-KG can be viewed as a sequence of interrelated ranking tasks. To enable future research on CER-KG, we created QBLink-KG, a publicly available benchmark that was adapted from QBLink, a benchmark for text-based conversational reading comprehension of Wikipedia. As an initial approach to CER-KG, we experimented with Transformer- and LSTM-based query encoders in combination with the Neural Architecture for Conversational Entity Retrieval (NACER), our proposed feature-based neural architecture for entity ranking in CER-KG. NACER computes the ranking score of a candidate KG entity by taking into account diverse lexical and semantic matching signals between various KG components in its neighborhood, such as entities, categories, and literals, as well as entities in the results of the preceding turns in dialog history. The reported experimental results reveal the key challenges of CER-KG along with the possible directions for new approaches to this task.