Approximate Answering of Graph Queries
- URL: http://arxiv.org/abs/2308.06585v1
- Date: Sat, 12 Aug 2023 14:47:21 GMT
- Title: Approximate Answering of Graph Queries
- Authors: Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan, Max Berrendorf,
Daniel Daza, Mikhail Galkin, Pasquale Minervini, Mathias Niepert, Hongyu Ren
- Abstract summary: In this chapter, we will give an overview of several methods which have been proposed to answer queries in such a setting.
We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation.
Then, we give an overview of the different approaches and describe them in terms of expressiveness, supported graph types, and inference capabilities.
- Score: 40.375299599925924
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Knowledge graphs (KGs) are inherently incomplete because of incomplete world
knowledge and bias in what is the input to the KG. Additionally, world
knowledge constantly expands and evolves, making existing facts deprecated or
introducing new ones. However, we would still want to be able to answer queries
as if the graph were complete. In this chapter, we will give an overview of
several methods which have been proposed to answer queries in such a setting.
We will first provide an overview of the different query types which can be
supported by these methods and datasets typically used for evaluation, as well
as an insight into their limitations. Then, we give an overview of the
different approaches and describe them in terms of expressiveness, supported
graph types, and inference capabilities.
Related papers
- FedCQA: Answering Complex Queries on Multi-Source Knowledge Graphs via
Federated Learning [55.02512821257247]
Complex logical query answering is a challenging task in knowledge graphs (KGs)
Recent approaches are proposed to represent KG entities into embedding vectors and find answers to logical queries from the KGs.
It remains unknown how to answer queries on multi-source KGs.
arXiv Detail & Related papers (2024-02-22T14:57:44Z) - G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [61.93058781222079]
We develop a flexible question-answering framework targeting real-world textual graphs.
We introduce the first retrieval-augmented generation (RAG) approach for general textual graphs.
G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem.
arXiv Detail & Related papers (2024-02-12T13:13:04Z) - Open-Set Knowledge-Based Visual Question Answering with Inference Paths [79.55742631375063]
The purpose of Knowledge-Based Visual Question Answering (KB-VQA) is to provide a correct answer to the question with the aid of external knowledge bases.
We propose a new retriever-ranker paradigm of KB-VQA, Graph pATH rankER (GATHER for brevity)
Specifically, it contains graph constructing, pruning, and path-level ranking, which not only retrieves accurate answers but also provides inference paths that explain the reasoning process.
arXiv Detail & Related papers (2023-10-12T09:12:50Z) - A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic,
and Multimodal [57.8455911689554]
Knowledge graph reasoning (KGR) aims to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs)
It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc.
arXiv Detail & Related papers (2022-12-12T08:40:04Z) - Semantic Structure based Query Graph Prediction for Question Answering
over Knowledge Graph [5.5332967798665305]
This paper focuses on query graph generation from natural language questions.
Existing approaches for query graph generation ignore the semantic structure of a question.
We develop a novel Structure-BERT to predict the semantic structure of a question.
arXiv Detail & Related papers (2022-04-15T20:35:00Z) - Graph-augmented Learning to Rank for Querying Large-scale Knowledge
Graph [34.774049199809426]
Knowledge graph question answering (i.e., KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph.
We first propose to partition the retrieved KSG to several smaller sub-KSGs via a new subgraph partition algorithm.
We then present a graph-augmented learning to rank model to select the top-ranked sub-KSGs from them.
arXiv Detail & Related papers (2021-11-20T08:27:37Z) - AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer
Summarization [73.91543616777064]
Community Question Answering (CQA) fora such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of community-based questions.
One goal of answer summarization is to produce a summary that reflects the range of answer perspectives.
This work introduces a novel dataset of 4,631 CQA threads for answer summarization, curated by professional linguists.
arXiv Detail & Related papers (2021-11-11T21:48:02Z) - Query Embedding on Hyper-relational Knowledge Graphs [0.4779196219827507]
Multi-hop logical reasoning is an established problem in the field of representation learning on knowledge graphs.
We extend the multi-hop reasoning problem to hyper-relational KGs allowing to tackle this new type of complex queries.
arXiv Detail & Related papers (2021-06-15T14:08:50Z) - Approximate Knowledge Graph Query Answering: From Ranking to Binary
Classification [0.20999222360659608]
Structured querying on incomplete graphs will result in incomplete sets of answers.
Several algorithms for approximate structured query answering have been proposed.
We argue that performing a ranking-based evaluation is not sufficient to assess methods for complex query answering.
arXiv Detail & Related papers (2021-02-22T22:28:08Z) - Understanding Knowledge Gaps in Visual Question Answering: Implications
for Gap Identification and Testing [20.117014315684287]
We use a taxonomy of Knowledge Gaps (KGs) to tag questions with one or more types of KGs.
We then examine the skew in the distribution of questions for each KG.
These new questions can be added to existing VQA datasets to increase the diversity of questions and reduce the skew.
arXiv Detail & Related papers (2020-04-08T00:27:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.