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A prevalent approach of entity-oriented systems involves retrieving relevant entities by harnessing knowledge graph embeddings. These embeddings encode entity information in the context of the knowledge graph and are static in nature. Our goal is to generate entity embeddings that capture what renders them relevant for the query. This differs from entity embeddings constructed with static resource, for example, E-BERT. Previously, ~\citet{dalton2014entity} demonstrated the benefits obtained with the Entity Context Model, a pseudo-relevance feedback approach based on entity links in relevant contexts. In this work, we reinvent the Entity Context Model (ECM) for neural graph networks and incorporate pre-trained embeddings. We introduce three entity ranking models based on fundamental principles of ECM: (1) \acl{GAN}, (2) Simple Graph Relevance Networks, and (3) Graph Relevance Networks. \acl{GAN} and Graph Relevance Networks are the graph neural variants of ECM, that employ attention mechanism and relevance information of the relevant context respectively to ascertain entity relevance. Our experiments demonstrate that our neural variants of the ECM model significantly outperform the state-of-the-art BERT-ER ~\cite{10.1145/3477495.3531944} by more than 14\% and exceeds the performance of systems that use knowledge graph embeddings by over 101\%. Notably, our findings reveal that leveraging the relevance of the relevant context is more effective at identifying relevant entities than the attention mechanism. To evaluate the efficacy of the models, we conduct experiments on two standard benchmark datasets, DBpediaV2 and TREC Complex Answer Retrieval. To aid reproducibility, our code and data are available. https://github.com/TREMA-UNH/neural-entity-context-models 

This tutorial will provide an overview of recent advances on neuro- symbolic approaches for information retrieval. A decade ago, knowl- edge graphs and semantic annotations technology led to active research on how to best leverage symbolic knowledge. At the same time, neural methods have demonstrated to be versatile and highly effective. From a neural network perspective, the same representation approach can service document ranking or knowledge graph rea- soning. End-to-end training allows to optimize complex methods for downstream tasks. We are at the point where both the symbolic and the neural research advances are coalescing into neuro-symbolic approaches. The underlying research questions are how to best combine sym- bolic and neural approaches, what kind of symbolic/neural ap- proaches are most suitable for which use case, and how to best integrate both ideas to advance the state of the art in information retrieval. Materials are available online: https://github.com/laura-dietz/ neurosymbolic-representations-for-IR 

This tutorial will provide an overview of recent advances on neuro-symbolic approaches for information retrieval. A decade ago, knowledge graphs and semantic annotations technology led to active re- search on how to best leverage symbolic knowledge. At the same time, neural methods have demonstrated to be versatile and highly effective. From a neural network perspective, the same representation approach can service document ranking or knowledge graph reasoning. End-to-end training allows to optimize complex methods for downstream tasks. We are at the point where both the symbolic and the neural research advances are coalescing into neuro-symbolic approaches. The underlying research questions are how to best combine symbolic and neural ap- proaches, what kind of symbolic/neural approaches are most suitable for which use case, and how to best integrate both ideas to advance the state of the art in information retrieval. 

Using entity aspect links, we improve upon the current state-of-the-art in entity retrieval. Entity retrieval is the task of retrieving relevant entities for search queries, such as "Antibiotic Use In Livestock". Entity aspect linking is a new technique to refine the semantic information of entity links. For example, while passages relevant to the query above may mention the entity "USA", there are many aspects of the USA of which only few, such as "USA/Agriculture", are relevant for this query. By using entity aspect links that indicate which aspect of an entity is being referred to in the context of the query, we obtain more specific relevance indicators for entities. We show that our approach improves upon all baseline methods, including the current state-of-the-art using a standard entity retrieval test collection. With this work, we release a large collection of entity-aspect-links for a large TREC corpus.