Rule Induction and Reasoning over Knowledge Graphs

@inproceedings{Stepanova2018RuleIA,
  title={Rule Induction and Reasoning over Knowledge Graphs},
  author={Daria Stepanova and Mohamed H. Gad-Elrab and Vinh Thinh Ho},
  booktitle={Reasoning Web},
  year={2018}
}
Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue. We put a particular emphasis on the problems of learning exception-enriched rules from highly biased and incomplete… 
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning
TLDR
A cross-graph representation learning framework is proposed, i.e., CrossVal, which can leverage an external KG to validate the facts in the target KG efficiently and achieves the best performance compared with the state-of-the-art methods on large-scale KGs.
RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools
TLDR
This work presents a tool for generating different kinds of datasets and for evaluating rule learning systems, including new performance measures.
Tracy: Tracing Facts over Knowledge Graphs and Text
TLDR
Tracy is presented, a novel tool that generates human-comprehensible explanations for candidate facts by relying on background knowledge in the form of rules to rewrite the fact in question into other easier-to-spot facts.
Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases
TLDR
The knowledge representation that has emerged as a pragmatic consensus in the research community of entity-centric knowledge bases is discussed and how entities can be represented alternatively as vectors in a vector space, by help of neural networks is discussed.
Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases
Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as
Synthetic Datasets and Evaluation Tools for Inductive Neural Reasoning
TLDR
This paper provides a tool for generating diverse datasets and for evaluating neural rule learning systems, including novel performance metrics, and argues that existing datasets and evaluation approaches are lacking in various dimensions.
A Birds Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges
TLDR
Existing KGC approaches are discussed, including the state-of-the-art Knowledge Graph Embeddings (KGE), not only on static graphs but also for the latest trends such as multimodal, temporal, and uncertain knowledge graphs.
Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?
  • A. Ozaki
  • Computer Science, Philosophy
    KI - Künstliche Intelligenz
  • 2020
TLDR
This work highlights classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies, and provides an overview of each approach and how it has been adapted for dealing with DL ontologies.
Reasoning Web. Explainable Artificial Intelligence: 15th International Summer School 2019, Bolzano, Italy, September 20–24, 2019, Tutorial Lectures
TLDR
This course provides an in-depth description and analysis of the main reasoning and explanation methods for ontologies: tableau procedures and axiom pinpointing algorithms.
Knowledge Graph-Based Clinical Decision Support System Reasoning: A Survey
TLDR
This paper hopes to serve as a basis for future research concerning knowledge graph-based Clinical Decision Support Systems and its role in health care.
...
...

References

SHOWING 1-10 OF 81 REFERENCES
Exception-Enriched Rule Learning from Knowledge Graphs
TLDR
This work presents a method for effective revision of learned Horn rules by adding exceptions (i.e., negated atoms) into their bodies and demonstrates the effectiveness of the developed method and the improvements in accuracy for KG completion by rule-based fact prediction.
Towards Nonmonotonic Relational Learning from Knowledge Graphs
TLDR
This work makes the first steps towards extending a rule-based approach to KGs in their original relational form, and provides preliminary evaluation results on real-world KGs, which demonstrate the effectiveness of the proposed method.
Completeness-Aware Rule Learning from Knowledge Graphs
TLDR
This paper proposes to use (in-)completeness meta-information to better assess the quality of rules learned from incomplete KGs, and introduces completeness-aware scoring functions for relational association rules.
Rule Learning from Knowledge Graphs Guided by Embedding Models
TLDR
A rule learning method that utilizes probabilistic representations of missing facts is proposed that iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and external information sources including text corpora.
Expanding Wikidata's Parenthood Information by 178%, or How To Mine Relation Cardinality Information
TLDR
For the sample of the hasChild relation in Wikidata, it is shown that simple regular-expression based extraction from Wikipedia can increase the size of the relation by 178%.
Ontological Pathfinding
TLDR
The Ontological Pathfinding algorithm (OP) is proposed that scales to web-scale knowledge bases via a series of parallelization and optimization techniques: a relational knowledge base model to apply inference rules in batches, a new rule mining algorithm that parallelizes the join queries, a novel partitioning algorithm to break the mining tasks into smaller independent sub-tasks, and a pruning strategy to eliminate unsound and resource-consuming rules before applying them.
Estimating Rule Quality for Knowledge Base Completion with the Relationship between Coverage Assumption
TLDR
This work proposes a novel score function for evaluating the quality of a first-order rule learned from a knowledge base, and attempts to include information about the tuples not in the KB when evaluating thequality of a potential rule.
Knowledge graph refinement: A survey of approaches and evaluation methods
TLDR
A survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.
RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles
TLDR
This paper proposes a novel rule learning approach named RDF2Rules for RDF knowledge bases, which uses the entity type information when generates and evaluates rules, which makes the learned rules more accurate.
Fast rule mining in ontological knowledge bases with AMIE$$+$$+
TLDR
This paper shows how the approach AMIE (Galárraga et al. in WWW, 2013) can be optimized to mine even larger KBs with more than 12M statements, and extends to areas of mining that were previously beyond reach.
...
...