1,354 research outputs found
Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs
Current methods for embedding-based query answering over incomplete Knowledge
Graphs (KGs) only focus on inductive reasoning, i.e., predicting answers by
learning patterns from the data, and lack the complementary ability to do
deductive reasoning, which requires the application of domain knowledge to
infer further information. To address this shortcoming, we investigate the
problem of incorporating ontologies into embedding-based query answering models
by defining the task of embedding-based ontology-mediated query answering. We
propose various integration strategies into prominent representatives of
embedding models that involve (1) different ontology-driven data augmentation
techniques and (2) adaptation of the loss function to enforce the ontology
axioms. We design novel benchmarks for the considered task based on the LUBM
and the NELL KGs and evaluate our methods on them. The achieved improvements in
the setting that requires both inductive and deductive reasoning are from 20%
to 55% in HITS@3
Pseudo-contractions as Gentle Repairs
Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Maybe Eventually? Towards Combining Temporal and Probabilistic Description Logics and Queries: Extended Version
We present some initial results on ontology-based query answering with description logic ontologies that may employ temporal and probabilistic operators on concepts and axioms. Speci_cally, we consider description logics extended with operators from linear temporal logic (LTL), as well as subjective probability operators, and an extended query language in which conjunctive queries can be combined using these operators. We first show some complexity results for the setting in which either only temporal operators or only probabilistic operators may be used, both in the ontology and in the query, and then show a 2ExpSpace lower bound for the setting in which both types of operators can be used together.This is an extended version of an article accepted at Description Logics 2019
Ontology-Based Query Answering for Probabilistic Temporal Data: Extended Version
We investigate ontology-based query answering for data that are both temporal and probabilistic, which might occur in contexts such as stream reasoning or situation recognition with uncertain data. We present a framework that allows to represent temporal probabilistic data, and introduce a query language with which complex temporal and probabilistic patterns can be described. Specifically, this language combines conjunctive queries with operators from linear time logic as well as probability operators. We analyse the complexities of evaluating queries in this language in various settings. While in some cases, combining the temporal and the probabilistic dimension in such a way comes at the cost of increased complexity, we also determine cases for which this increase can be avoided.This is an extended version of the article to appear in the proceedings of AAAI 2019
First-Order Rewritability and Complexity of Two-Dimensional Temporal Ontology-Mediated Queries
Aiming at ontology-based data access to temporal data, we design
two-dimensional temporal ontology and query languages by combining logics from
the (extended) DL-Lite family with linear temporal logic LTL over discrete time
(Z,<). Our main concern is first-order rewritability of ontology-mediated
queries (OMQs) that consist of a 2D ontology and a positive temporal instance
query. Our target languages for FO-rewritings are two-sorted FO(<) -
first-order logic with sorts for time instants ordered by the built-in
precedence relation < and for the domain of individuals - its extension FOE
with the standard congruence predicates t \equiv 0 mod n, for any fixed n > 1,
and FO(RPR) that admits relational primitive recursion. In terms of circuit
complexity, FOE- and FO(RPR)-rewritability guarantee answering OMQs in uniform
AC0 and NC1, respectively.
We proceed in three steps. First, we define a hierarchy of 2D DL-Lite/LTL
ontology languages and investigate the FO-rewritability of OMQs with atomic
queries by constructing projections onto 1D LTL OMQs and employing recent
results on the FO-rewritability of propositional LTL OMQs. As the projections
involve deciding consistency of ontologies and data, we also consider the
consistency problem for our languages. While the undecidability of consistency
for 2D ontology languages with expressive Boolean role inclusions might be
expected, we also show that, rather surprisingly, the restriction to Krom and
Horn role inclusions leads to decidability (and ExpSpace-completeness), even if
one admits full Booleans on concepts. As a final step, we lift some of the
rewritability results for atomic OMQs to OMQs with expressive positive temporal
instance queries. The lifting results are based on an in-depth study of the
canonical models and only concern Horn ontologies
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