scholarly journals Query Answering With Non-Monotonic Rules: A Case Study of Archaeology Qualitative Spatial Reasoning

10.29007/6ph5 ◽  
2019 ◽  
Author(s):  
Mohamed Ben Ellefi ◽  
Pierre Drap ◽  
Laurent Garcia ◽  
Fabien Garreau ◽  
Claire Lefèvre ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Knowledge Base ◽  
Spatial Reasoning ◽  
Answer Set Programming ◽  
Spatial Relations ◽  
Knowledge Bases ◽  
Query Answering ◽  
Qualitative Spatial Reasoning ◽  
Large Dataset

This paper deals with querying ontology-based knowledge bases equipped with non-monotonic rules through a case study within the framework of Cultural Heritage. It focuses on 3D underwater surveys on the Xlendi wreck which is represented by an OWL2 knowledge base with a large dataset. The paper aims at improving the interactions between the archaeologists and the knowledge base providing new queries that involve non-monotonic rules in order to perform qualitative spatial reasoning. To this end, the knowledge base initially represented in OWL2-QL is translated into an equivalent Answer Set Programming (ASP) program and is enriched with a set of non-monotonic ASP rules suitable to express default and exceptions. An ASP query answering approach is proposed and implemented. Furthermore due to the increased expressiveness of non-monotonic rules it provides spatial reasoning and spatial relations between artifacts query answering which is not possible with query answering languages such as SPARQL and SQWRL.

scholarly journals A Cognitively-Inspired Model for Making Sense of Hasse Diagrams

2021 ◽  
Author(s):  
Dimitra Bourou ◽  
Marco Schorlemmer ◽  
Enric Plaza
Keyword(s):  
Spatial Reasoning ◽  
Geometric Configuration ◽  
Sense Making ◽  
Qualitative Spatial Reasoning ◽  
Diagram Geometry ◽  
Hasse Diagrams ◽  
Image Schemas ◽  
Making Sense ◽  
Logical Semantics

In this paper, we present a model of the sense-making process for diagrams, and describe it for the case of Hasse diagrams. Sense-making is modeled as the construction of networks of conceptual blends among image schemas and the diagram’s geometric configuration. As a case study, we specify four image schemas and the geometric configuration of a Hasse diagram, with typed FOL theories. In addition, for the diagram geometry, we utilise Qualitative Spatial Reasoning formalisms. Using an algebraic specification language, we can compute conceptual blends as category-theoretic colimits. Our model approaches sense-making as a process where the image schemas and the diagram geometry both structure each other through a complex network of conceptual blends. This yields a final blend in which the sort of inferences we confer to diagrammatic representations emerge. We argue that this approach to sense-making in diagrams is more cognitively apt than the mainstream view of a diagram being a syntactic representation of some underlying logical semantics. Moreover, our model could be applied to various types of stimuli and is thus valuable for the general field of AI.

scholarly journals Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise

2019 ◽  
Vol 33 ◽  
pp. 2911-2918 ◽  
Author(s):  
Nikhil Krishnaswamy ◽  
Scott Friedman ◽  
James Pustejovsky
Keyword(s):  
Deep Learning ◽  
Heuristic Search ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Search Space ◽  
Training Data ◽  
Learning Approaches ◽  
Qualitative Spatial Reasoning ◽  
Heuristic Search Algorithms ◽  
Novel Approach

Many modern machine learning approaches require vast amounts of training data to learn new concepts; conversely, human learning often requires few examples—sometimes only one—from which the learner can abstract structural concepts. We present a novel approach to introducing new spatial structures to an AI agent, combining deep learning over qualitative spatial relations with various heuristic search algorithms. The agent extracts spatial relations from a sparse set of noisy examples of block-based structures, and trains convolutional and sequential models of those relation sets. To create novel examples of similar structures, the agent begins placing blocks on a virtual table, uses a CNN to predict the most similar complete example structure after each placement, an LSTM to predict the most likely set of remaining moves needed to complete it, and recommends one using heuristic search. We verify that the agent learned the concept by observing its virtual block-building activities, wherein it ranks each potential subsequent action toward building its learned concept. We empirically assess this approach with human participants’ ratings of the block structures. Initial results and qualitative evaluations of structures generated by the trained agent show where it has generalized concepts from the training data, which heuristics perform best within the search space, and how we might improve learning and execution.

scholarly journals Qualitative Spatial Representation for the Humanities

2019 ◽  
Vol 13 (1-2) ◽  
pp. 2-27 ◽  
Author(s):  
John G. Stell
Keyword(s):  
Geographical Information Systems ◽  
Spatial Representation ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Geographical Information ◽  
Computational Techniques ◽  
Computational Technique ◽  
Qualitative Spatial Reasoning ◽  
Gis Technology ◽  
Geographical Space

‘Qualitative spatial reasoning and representation’ is a range of techniques developed in Artificial Intelligence to meet the need for a computational treatment of qualitative spatial relations. Examples of such relations include ‘next to’, ‘overlapping’, ‘to the left of’, ‘separate from’, ‘including’, and so on. These relations occur within the data found in the spatial humanities, but the computational techniques described here do not appear to have been used in connection with this context. While Geographical Information Systems (GIS) are widely used as a means of visualizing and exploring material in the spatial humanities, GIS technology is acknowledged to be ill-suited to information that is vague, uncertain, ambiguous, imprecise or having other qualities that in a scientific setting could be regarded as imperfections. In the humanities such ‘imperfections’ are of course important, and qualitative spatial relations are one source of data that challenges scientifically based GIS. This article reviews the origin of qualitative spatial reasoning and representation in A. N. Whitehead's mereotopology and argues for exploring how these methods could complement GIS as a computational technique in the humanities. Qualitative representation is applicable to modelling spatial arrangements in many domains, not just geographical space. This is demonstrated through an example of spatial relations in lines of printed text.

Mining Geo-Referenced Databases

2011 ◽  
pp. 113-150 ◽  
Author(s):  
Maribel Yasmina Santos ◽  
Luís Alfredo Amaral
Keyword(s):  
Knowledge Discovery ◽  
Spatial Data ◽  
Domain Knowledge ◽  
Relational Databases ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Semantic Knowledge ◽  
Knowledge Discovery In Databases ◽  
Data Sets ◽  
Qualitative Spatial Reasoning

Knowledge discovery in databases is a process that aims at the discovery of associations within data sets. The analysis of geo-referenced data demands a particular approach in this process. This chapter presents a new approach to the process of knowledge discovery, in which qualitative geographic identifiers give the positional aspects of geographic data. Those identifiers are manipulated using qualitative reasoning principles, which allows for the inference of new spatial relations required for the data mining step of the knowledge discovery process. The efficacy and usefulness of the implemented system — Padrão — has been tested with a bank dataset. The results support that traditional knowledge discovery systems, developed for relational databases and not having semantic knowledge linked to spatial data, can be used in the process of knowledge discovery in geo-referenced databases, since some of this semantic knowledge and the principles of qualitative spatial reasoning are available as spatial domain knowledge.

scholarly journals Complexity of Approximate Query Answering under Inconsistency in Datalog+/-

2018 ◽  
Author(s):  
Thomas Lukasiewicz ◽  
Enrico Malizia ◽  
Cristian Molinaro
Keyword(s):  
Knowledge Base ◽  
Standard Setting ◽  
Knowledge Bases ◽  
Query Answering ◽  
Conjunctive Query ◽  
Approximate Query Answering ◽  
Wide Range ◽  
Approximate Query

Several semantics have been proposed to query inconsistent ontological knowledge bases, including the intersection of repairs and the intersection of closed repairs as two approximate inconsistency-tolerant semantics. In this paper, we analyze the complexity of conjunctive query answering under these two semantics for a wide range of Datalog+/- languages. We consider both the standard setting, where errors may only be in the database, and the generalized setting, where also the rules of a Datalog+/- knowledge base may be erroneous.

scholarly journals Reverse Engineering Queries in Ontology-Enriched Systems: The Case of Expressive Horn Description Logic Ontologies

2018 ◽  
Author(s):  
Víctor Gutiérrez-Basulto ◽  
Jean Christoph Jung ◽  
Leif Sabellek
Keyword(s):  
Reverse Engineering ◽  
Knowledge Base ◽  
Description Logic ◽  
Knowledge Bases ◽  
Query Answering ◽  
Conjunctive Queries ◽  
Complexity Bounds ◽  
Query By Example

We introduce the query-by-example (QBE) paradigm for query answering in the presence of ontologies. Intuitively, QBE permits non-expert users to explore the data by providing examples of the information they (do not) want, which the system then generalizes into a query. Formally, we study the following question: given a knowledge base and sets of positive and negative examples, is there a query that returns all positive but none of the negative examples?  We focus on description logic knowledge bases with ontologies formulated in Horn-ALCI and (unions of) conjunctive queries. Our main contributions are characterizations, algorithms and tight complexity bounds for QBE.  

Mining Geo-Referenced Databases

2008 ◽  
pp. 880-912 ◽  
Author(s):  
Maribel Yasmina Santos ◽  
Luís Alfredo Amaral
Keyword(s):  
Knowledge Discovery ◽  
Spatial Data ◽  
Domain Knowledge ◽  
Relational Databases ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Semantic Knowledge ◽  
Knowledge Discovery In Databases ◽  
Data Sets ◽  
Qualitative Spatial Reasoning

Knowledge discovery in databases is a process that aims at the discovery of associations within data sets. The analysis of geo-referenced data demands a particular approach in this process. This chapter presents a new approach to the process of knowledge discovery, in which qualitative geographic identifiers give the positional aspects of geographic data. Those identifiers are manipulated using qualitative reasoning principles, which allows for the inference of new spatial relations required for the data mining step of the knowledge discovery process. The efficacy and usefulness of the implemented system — Padrão — has been tested with a bank dataset. The results support that traditional knowledge discovery systems, developed for relational databases and not having semantic knowledge linked to spatial data, can be used in the process of knowledge discovery in geo-referenced databases, since some of this semantic knowledge and the principles of qualitative spatial reasoning are available as spatial domain knowledge.

scholarly journals Explanations for Inconsistency-Tolerant Query Answering under Existential Rules

2020 ◽  
Vol 34 (03) ◽  
pp. 2909-2916
Author(s):  
Thomas Lukasiewicz ◽  
Enrico Malizia ◽  
Cristian Molinaro
Keyword(s):  
Knowledge Base ◽  
Complexity Analysis ◽  
Knowledge Bases ◽  
Query Answering ◽  
Complexity Measures ◽  
Rule Languages ◽  
Wide Range ◽  
Inconsistent Knowledge

Querying inconsistent knowledge bases is a problem that has attracted a great deal of interest over the last decades. While several semantics of query answering have been proposed, and their complexity is rather well-understood, little attention has been paid to the problem of explaining query answers. Explainability has recently become a prominent problem in different areas of AI. In particular, explaining query answers allows users to understand not only what is entailed by an inconsistent knowledge base, but also why. In this paper, we address the problem of explaining query answers for existential rules under three popular inconsistency-tolerant semantics, namely, the ABox repair, the intersection of repairs, and the intersection of closed repairs semantics. We provide a thorough complexity analysis for a wide range of existential rule languages and for different complexity measures.

Sign in / Sign up

Export Citation Format

Share Document