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Title
Abstract
Figures
Introduction
Experimental Results
Related Work
Conclusion and Future Work
References

Ontology inference using spatial and trajectory domain rules

Profile image of Rouaa WannousRouaa Wannous

Abstract

Capture devices give rise to a large scale spatiotemporal data describing moving object's trajectories. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and sensors techniques. Although capture technologies differ, the captured data share common spatial and temporal features. Thus, relational database management systems (RDBMS) can be used to store and query the captured data. For this, RDBMS define spatial data types and spatial operations. Recent applications show that the solutions based on traditional data models are not sufficient to consider complex use cases that require advanced data models. A complex use case refers to data, but also to domain knowledge, to spatial reasoning or others. This article presents a sample application based on trajectories that require three types of independent data models: a domain data model, a semantic model and a spatial model. We analyze each of them and propose a modeling approach based on ontologies. This work introduces a high-level trajectory ontology and a generic spatial ontology. Also, we present our ontology matching approach for integrating the sample trajectory domain to both defined ontologies. This work has a special focus to the problem of defining ontology inference using business rules combined with spatial rules. We present an implementation framework for declarative and imperative parts of ontology rules using an RDF data store. We discuss various experiments based on spatial inference calculation. We discuss these results and present some solutions to improve the complexity of calculating spatial ontological inferences.

Figures (10)
In this work, we propose a Semantic Domain Ontology (Figure 2) based on activities organized as general ones linked to trajectory, and a hierarchy of basic activities linked to sequences of the trajectory domain ontology. The Seal Domain Ontology (Figure 2) is dealing with seal’s activities. According to the domain expert, four activities (resting, traveling, forag- ing and traveling-foraging) are related to the three states of a seal. The seal trajectory ontology sequences are associated with these main activities.
In this work, we propose a Semantic Domain Ontology (Figure 2) based on activities organized as general ones linked to trajectory, and a hierarchy of basic activities linked to sequences of the trajectory domain ontology. The Seal Domain Ontology (Figure 2) is dealing with seal’s activities. According to the domain expert, four activities (resting, traveling, forag- ing and traveling-foraging) are related to the three states of a seal. The seal trajectory ontology sequences are associated with these main activities.
Figure 2. Overview of Seal Trajectory Ontology
Figure 2. Overview of Seal Trajectory Ontology
Figure 3. The OGC geometry object model hierarchy
Figure 3. The OGC geometry object model hierarchy
Oracle Semantic Technologies is a rule-based system where rules are based on IF-THEN patterns and new as- sertions are placed into working memory. Thus, the rule- based system is said to be a deduction system. In de- duction systems, the convention is to refer to each IF pattern an antecedent and to each THEN pattern a conse- quent. SEM_APIS.CREATE_RULEBASE procedure defines user-defined rules in a rulebase. Our rulebase is called sealActivities_rb. The system automatically associates a view called MDSYS.SEMR_rulebase-name to insert, delete or modify rules in a rulebase. Code 4 gives foraging_rule definition based on domain expert’s condi- tions. From line 4 to 10, we construct a subgraph and necessary variables needed by the IF part of the foraging_rule. Line 11 gives the THEN part of the rule. Line 12 defines the namespace of ontology. Code 4. Implementation of the foraging rule B. Spatial ontology rules
Oracle Semantic Technologies is a rule-based system where rules are based on IF-THEN patterns and new as- sertions are placed into working memory. Thus, the rule- based system is said to be a deduction system. In de- duction systems, the convention is to refer to each IF pattern an antecedent and to each THEN pattern a conse- quent. SEM_APIS.CREATE_RULEBASE procedure defines user-defined rules in a rulebase. Our rulebase is called sealActivities_rb. The system automatically associates a view called MDSYS.SEMR_rulebase-name to insert, delete or modify rules in a rulebase. Code 4 gives foraging_rule definition based on domain expert’s condi- tions. From line 4 to 10, we construct a subgraph and necessary variables needed by the IF part of the foraging_rule. Line 11 gives the THEN part of the rule. Line 12 defines the namespace of ontology. Code 4. Implementation of the foraging rule B. Spatial ontology rules
Figure 4. A view of owlOGCSpatial ontology
Figure 4. A view of owlOGCSpatial ontology
In this work, we consider topological relationships. Each relationship has a declarative part as an RDF, and an imperative part, formally, an associated rule as IF—THEN pattern. We create a rulebase named owlOGCSpatial_rb to hold spatial relationships rules. For example, the rule (Code 5) presents the imperative part of the spatial relationship Contains. Lines 4 to 10 in Code 5 represent the IF side of the rule. We construct a subgraph and necessary variables, namely, the two spatial ob- jects sObj1 and sObj2, respectivity, their strings coordinates wktSObj1 and wktSObj2, and the srid which is the Spa- tial Reference System Identifier. The IF side of the rule evalu- ates the spatial relationship between the two spatial objects us- ing a function called evalSpatialRelationship. This function builds a bridge between the ontology spatial rules and spatial operators in Oracle DBMS. Line 11 in Code 5 is the consequent or the THEN part of the rule. Code 5. Implementation of the Contains_rule
In this work, we consider topological relationships. Each relationship has a declarative part as an RDF, and an imperative part, formally, an associated rule as IF—THEN pattern. We create a rulebase named owlOGCSpatial_rb to hold spatial relationships rules. For example, the rule (Code 5) presents the imperative part of the spatial relationship Contains. Lines 4 to 10 in Code 5 represent the IF side of the rule. We construct a subgraph and necessary variables, namely, the two spatial ob- jects sObj1 and sObj2, respectivity, their strings coordinates wktSObj1 and wktSObj2, and the srid which is the Spa- tial Reference System Identifier. The IF side of the rule evalu- ates the spatial relationship between the two spatial objects us- ing a function called evalSpatialRelationship. This function builds a bridge between the ontology spatial rules and spatial operators in Oracle DBMS. Line 11 in Code 5 is the consequent or the THEN part of the rule. Code 5. Implementation of the Contains_rule
Figure 5. Activity diagram for spatial inference process
Figure 5. Activity diagram for spatial inference process
We observe a decrease in both of the spatial ontology rules computation and DBMS spatial operator calls. For example, considering 250 dives, in the normal case of inference, the executions of the spatial ontology rules is 1000 000 and DBMS spatial operator calls is 125 000. However in the refinement case Figure 6, the executions of the rules is 130 000 and DBMS operator calls is 16 000. Figure 6. Enhancement of the spatial ontology inference with constraints refinement
We observe a decrease in both of the spatial ontology rules computation and DBMS spatial operator calls. For example, considering 250 dives, in the normal case of inference, the executions of the spatial ontology rules is 1000 000 and DBMS spatial operator calls is 125 000. However in the refinement case Figure 6, the executions of the rules is 130 000 and DBMS operator calls is 16 000. Figure 6. Enhancement of the spatial ontology inference with constraints refinement
Figure 7. Evaluation of the spatial ontology inference over the proposed refinement
Figure 7. Evaluation of the spatial ontology inference over the proposed refinement

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