Knowledge-Driven Cross-Document Relation Extraction (2024)

Abstract: Relation extraction (RE) is a well-known NLP application often treated as a sentence or document-level task. However, a handful of recent efforts explore it across documents or in the cross-document setting (CrossDocRE). This is distinct from the single document case because different documents often focus on disparate themes, while text within a document tends to have a single goal.Current CrossDocRE efforts do not consider domain knowledge, which are often assumed to be known to the reader when documents are authored. Here, we propose a novel approach, KXDocRE, that embed domain knowledge of entities with input text for cross-document RE. Our proposed framework has three main benefits over baselines: 1) it incorporates domain knowledge of entities along with documents’ text; 2) it offers interpretability by producing explanatory text for predicted relations between entities 3) it improves performance over the prior methods. Code and models are available at https://github.com/kracr/cross-doc-relation-extraction .

## introduction identifying relations between entity pairs within unstructured text is known as relation extraction @xcite . earlier studies concentrated on uncovering relations between entities within a sentence @xcite @xcite @xcite . however, an entity pair need not necessarily occur in a sentence. it can also be part of a document. there is substantial literature on extracting relations between entities based on the document itself @xcite @xcite @xcite @xcite . beyond a single sentence or a document, entity pairs can also be present in different documents @xcite . figure 1 : three textual paths indicate the relationship path between the source entity (gcompris) and the target entity (gnu project). these connections are established through pairs of documents, where one document features the source entity, and the other contains the target entity. in each path, the connection between the source and target entities is led by a mentioned entity in both documents (e.g., linux). an analysis on wikipedia shows that more than 57.6 relational facts do not co-occur in a single document @xcite . this indicates that many facts occur across the documents. considering that, little work has been performed in cross-document relation extraction @xcite . codred (cross-document relation extraction dataset) is the first dataset published in this line of work @xcite , which serves as a starting point to solve crossdocre. documents containing the source entity are identified and retrieved from multiple documents; the same is done for the target entity. various text paths be-tween entity pairs (source and target entity) across documents are recognized using the mentioned entity (other than the source and target entity). text paths refer to the paths that connect the source and target entities via the mentioned entities. these paths are retrieved from both the source and target documents. figure 1 shows an example of crossdocre. between entity pairs gcompris and gnu project, multiple text paths via the mentioned entities, such as linux, qt and gnu, can be used to get the correct relation label as developer. codred uses these text paths in a bag and performs reasoning @xcite . although this approach seems reasonable, along with relevant text, these text paths also contain noisy data. to overcome these issues, ecrim (entity-centered crossdocument relation extraction) proposed filtering text paths using a mentioned entity @xcite . however, ecrim only works in one of the settings of crossdocre where text paths are provided for reasoning. to address this issues, mr.cod has been proposed, which is a multi-hop reasoning framework based on path mining and ranking @xcite . these models rely on the knowledge between entities in a text and do not consider the domain knowledge associated with entities. past work along these lines uses entity types and entity aliases to predict the relation @xcite . recon @xcite encoded attribute and relation triples in the knowledge graph. kb-both @xcite uses entity details from hyperlinked text documents of wikipedia and knowledge graph (kg) from wikidata to enhance performance. given input text as a sentence or a document, these models use basic details of entities to improve the performance. in this paper, we explore whether incorporating domain knowledge can enhance the performance of crossdocre tasks. the main contributions of this work are as follows. ## related work the relation between entities can extend across multiple documents, and researchers have investigated the extraction of entities, events, and relationships in a cross-document context with unlabeled data @xcite . codred @xcite is the first open source human-annotated cross-document dataset. in this work, the authors address the problem using two approaches. the first method involves a pipeline approach in which they construct a relational graph for each document and reason over these graphs to extract the desired relation. the second method, referred as the joint approach, combines various text path representations through a selective attention mechanism to predict relations. although this method is effective, it does not consider the mentioned entity-based sentences. ecrim uses an entity-based document context filter to retain useful information in the given documents by using the mentioned entities in the text paths. secondly, they solve crossdocre using cross-path entity relation attention, allowing entity relations across text paths to interact with each other @xcite . nevertheless, this work focuses on a closed setting where evidential context has been given instead of all documents. a multi-hop evidence retrieval method based on evidence path mining and ranking has been proposed in mr.cod @xcite . in evidence path mining, a multi-document passage graph is constructed, where passages are linked by edges corresponding to shared entities. a graph traversal algorithm mines the passage paths from source-to-target entities. in evidence path ranking, paths are ranked based on relevance and top-k evidence paths are selected as input for downstream relation extraction models. alternatively, a causality-guided global reasoning algorithm is also used to filter confusing information and achieve global reasoning to solve cross-document relation extraction @xcite . proposed models for crossdocre until now does not consider background knowledge. ## experimental setup metrics. we used f1 and area under curve (auc) scores following the baseline @xcite for a fair comparison. for the open setting, we retrieve the top k document paths from the wikipedia corpus foot_0 and use the models trained in the closed setting to predict a relation. these paths are scored using three conditions. 1) entity count: the number of occurrences of the source entity in the document containing the source and likewise with the target entity. 2) shared entity: the number of shared entities that appear in source and target documents 3) tf-idf: the tf-idf similarity between the two documents. similar to baseline, we selected the top 16 paths with the highest scores. hyper-parameters used in our model are shown in baseline model for comparison. we used all the baseline models available in crossdocre for comparison. 1) codred @xcite used, which extracts text snippets surrounding the two entities in the document as input and feeds it into a bert-based model 2) ecrim @xcite ; an entity-based document filter is constructed and then fed into a bert-based model. we conducted our evaluation in response to the following research questions. rq1: what is the effectiveness of kxdocre in combining context knowledge with reasoning in solving crossdocre? rq2: does the explanation generated by our approach provide sufficient grounds to support the inferred relation? ## conclusion in this paper, we introduced a novel model kx-docre with three settings (ec, cc, ecc) that incorporate domain knowledge in crossdocre. our results suggest that model performance is improved by integrating diverse domain knowledge. also, we provide an explanation text for each prediction that makes our model interpretable. as a future work, researchers can create a versatile model that can work on diverse document sets, accumulating domain knowledge.