Retrieval from graph data is crucial for augmenting large language models (LLM) with both open-domain knowledge and private enterprise data, and it is also a key component in the recent GraphRAG system (Edge et al., 2024).Despite decades of research on knowledge graphs and knowledge base question answering, leading LLM frameworks (e.g., Langchain and LlamaIndex) have only minimal support for retrieval from modern encyclopedic knowledge graphs like Wikidata.In this paper, we analyze the root cause and suggest that modern RDF knowledge graphs (e.g., Wikidata, Freebase) are less efficient for LLMs due to overly large schemas that far exceed the typical LLM context window, use of resource identifiers, overlapping relation types and lack of normalization.As a solution, we propose property graph views on top of the underlying RDF graph that can be efficiently queried by LLMs using Cypher.We instantiated this idea on Wikidata and introduced CypherBench, the first benchmark with 11 large-scale, multi-domain property graphs with 7.8 million entities and over 10,000 questions.To achieve this, we tackled several key challenges, including developing an RDF-to-property graph conversion engine, creating a systematic pipeline for textto-Cypher task generation, and designing new evaluation metrics.

Existing work that augment question answering (QA) models with external knowledge (e.g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model's prediction rationale.In this paper, we propose a novel knowledge-aware approach that equips pretrained language models (PTLMs) with a multi-hop relational reasoning module, named multi-hop graph relation network (MHGRN).It performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs.The proposed reasoning module unifies path-based reasoning methods and graph neural networks and results in better interpretability and scalability.We also empirically show its effectiveness and scalability on CommonsenseQA and OpenbookQA datasets, and interpret its behaviors with case studies, with the code for experiments released 1 .

Sentiment analysis in low-resource languages suffers from the lack of training data.Crosslingual sentiment analysis (CLSA) aims to improve the performance on these languages by leveraging annotated data from other languages.Recent studies have shown that CLSA can be performed in a fully unsupervised manner, without exploiting either target language supervision or cross-lingual supervision.However, these methods rely heavily on unsupervised cross-lingual word embeddings (CLWE), which has been shown to have serious drawbacks on distant language pairs (e.g.English -Japanese).In this paper, we propose an end-to-end CLSA model by leveraging unlabeled data in multiple languages and multiple domains and eliminate the need for unsupervised CLWE.Our model applies to two CLSA settings: the traditional cross-lingual in-domain setting and the more challenging cross-lingual cross-domain setting.We empirically evaluate our approach on the multilingual multi-domain Amazon review dataset.Experimental results show that our model outperforms the baselines by a large margin despite its minimal resource requirement. 1