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Found 20 papers, 14 papers with code
EcomEdit: An Automated E-commerce Knowledge Editing Framework for Enhanced Product and Purchase Intention Understanding
However, there are naturally occurring scenarios that make KE necessary in this domain, such as the timely updating of product features and trending purchase intentions by customers, which necessitate further exploration.
CLR-Fact: Evaluating the Complex Logical Reasoning Capability of Large Language Models over Factual Knowledge
While large language models (LLMs) have demonstrated impressive capabilities across various natural language processing tasks by acquiring rich factual knowledge from their broad training data, their ability to synthesize and logically reason with this knowledge in complex ways remains underexplored.
On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions
Entity- and event-level conceptualization, as fundamental elements of human cognition, plays a pivotal role in generalizable reasoning.
MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding
Improving user experience and providing personalized search results in E-commerce platforms heavily rely on understanding purchase intention.
IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce
Enhancing Language Models' (LMs) ability to understand purchase intentions in E-commerce scenarios is crucial for their effective assistance in various downstream tasks.
Towards Subgraph Isomorphism Counting with Graph Kernels
Subgraph isomorphism counting is known as #P-complete and requires exponential time to find the accurate solution.
Federated Neural Graph Databases
However, existing NGDBs are typically designed to operate on a single graph, limiting their ability to reason across multiple graphs.
CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning
The sequential process of conceptualization and instantiation is essential to generalizable commonsense reasoning as it allows the application of existing knowledge to unfamiliar scenarios.
Privacy-Preserved Neural Graph Databases
Neural graph databases (NGDBs) have emerged as a powerful paradigm that combines the strengths of graph databases (GDBs) and neural networks to enable efficient storage, retrieval, and analysis of graph-structured data which can be adaptively trained with LLMs.
Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation
Although many applications require the use of knowledge for explanations, the utilization of abductive reasoning in conjunction with structured knowledge, such as a knowledge graph, remains largely unexplored.
Understanding Inter-Session Intentions via Complex Logical Reasoning
Existing research on session understanding has not adequately addressed making product or attribute recommendations for such complex intentions.
Knowledge Graph Reasoning over Entities and Numerical Values
To address the difference between entities and numerical values, we also propose the framework of Number Reasoning Network (NRN) for alternatively encoding entities and numerical values into separate encoding structures.
Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
Traditional neural complex query answering (CQA) approaches mostly work on entity-centric KGs.
Sequential Query Encoding For Complex Query Answering on Knowledge Graphs
Instead of parameterizing and executing the computational graph, SQE first uses a search-based algorithm to linearize the computational graph to a sequence of tokens and then uses a sequence encoder to compute its vector representation.
FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery
Understanding users' intentions in e-commerce platforms requires commonsense knowledge.
Query2Particles: Knowledge Graph Reasoning with Particle Embeddings
The query embedding method is proposed to answer these queries by jointly encoding queries and entities to the same embedding space.
Multi-Relational Graph based Heterogeneous Multi-Task Learning in Community Question Answering
To tackle this challenge, we develop a multi-relational graph based MTL model called Heterogeneous Multi-Task Graph Isomorphism Network (HMTGIN) which efficiently solves heterogeneous CQA tasks.
Joint Coreference Resolution and Character Linking for Multiparty Conversation
Character linking, the task of linking mentioned people in conversations to the real world, is crucial for understanding the conversations.
Multiplex Word Embeddings for Selectional Preference Acquisition
Therefore, in this paper, we propose a multiplex word embedding model, which can be easily extended according to various relations among words.
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
Moreover, it is shown that reasonable performance can be obtained when ZEN is trained on a small corpus, which is important for applying pre-training techniques to scenarios with limited data.
Ranked #1 on
Chinese Part-of-Speech Tagging
on CTB5 Dev
Chinese Named Entity Recognition
Chinese Word Segmentation
+5
