6 research outputs found
Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs
Many systems have been developed in recent years to mine logical rules from
large-scale Knowledge Graphs (KGs), on the grounds that representing
regularities as rules enables both the interpretable inference of new facts,
and the explanation of known facts. Among these systems, the walk-based methods
that generate the instantiated rules containing constants by abstracting
sampled paths in KGs demonstrate strong predictive performance and
expressivity. However, due to the large volume of possible rules, these systems
do not scale well where computational resources are often wasted on generating
and evaluating unpromising rules. In this work, we address such scalability
issues by proposing new methods for pruning unpromising rules using rule
hierarchies. The approach consists of two phases. Firstly, since rule
hierarchies are not readily available in walk-based methods, we have built a
Rule Hierarchy Framework (RHF), which leverages a collection of subsumption
frameworks to build a proper rule hierarchy from a set of learned rules. And
secondly, we adapt RHF to an existing rule learner where we design and
implement two methods for Hierarchical Pruning (HPMs), which utilize the
generated hierarchies to remove irrelevant and redundant rules. Through
experiments over four public benchmark datasets, we show that the application
of HPMs is effective in removing unpromising rules, which leads to significant
reductions in the runtime as well as in the number of learned rules, without
compromising the predictive performance
Towards Learning Instantiated Logical Rules from Knowledge Graphs
Efficiently inducing high-level interpretable regularities from knowledge
graphs (KGs) is an essential yet challenging task that benefits many downstream
applications. In this work, we present GPFL, a probabilistic rule learner
optimized to mine instantiated first-order logic rules from KGs. Instantiated
rules contain constants extracted from KGs. Compared to abstract rules that
contain no constants, instantiated rules are capable of explaining and
expressing concepts in more details. GPFL utilizes a novel two-stage rule
generation mechanism that first generalizes extracted paths into templates that
are acyclic abstract rules until a certain degree of template saturation is
achieved, then specializes the generated templates into instantiated rules.
Unlike existing works that ground every mined instantiated rule for evaluation,
GPFL shares groundings between structurally similar rules for collective
evaluation. Moreover, we reveal the presence of overfitting rules, their impact
on the predictive performance, and the effectiveness of a simple validation
method filtering out overfitting rules. Through extensive experiments on public
benchmark datasets, we show that GPFL 1.) significantly reduces the runtime on
evaluating instantiated rules; 2.) discovers much more quality instantiated
rules than existing works; 3.) improves the predictive performance of learned
rules by removing overfitting rules via validation; 4.) is competitive on
knowledge graph completion task compared to state-of-the-art baselines
Explainable Fact Checking by Combining Automated Rule Discovery with Probabilistic Answer Set Programming
abstract: The goal of fact checking is to determine if a given claim holds. A promising ap- proach for this task is to exploit reference information in the form of knowledge graphs (KGs), a structured and formal representation of knowledge with semantic descriptions of entities and relations. KGs are successfully used in multiple appli- cations, but the information stored in a KG is inevitably incomplete. In order to address the incompleteness problem, this thesis proposes a new method built on top of recent results in logical rule discovery in KGs called RuDik and a probabilistic extension of answer set programs called LPMLN.
This thesis presents the integration of RuDik which discovers logical rules over a given KG and LPMLN to do probabilistic inference to validate a fact. While automatically discovered rules over a KG are for human selection and revision, they can be turned into LPMLN programs with a minor modification. Leveraging the probabilistic inference in LPMLN, it is possible to (i) derive new information which is not explicitly stored in a KG with a probability associated with it, and (ii) provide supporting facts and rules for interpretable explanations for such decisions.
Also, this thesis presents experiments and results to show that this approach can label claims with high precision. The evaluation of the system also sheds light on the role played by the quality of the given rules and the quality of the KG.Dissertation/ThesisMasters Thesis Computer Science 201