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Discovering Spatial and Temporal Links among RDF Data Panayiotis - - PowerPoint PPT Presentation

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Discovering Spatial and Temporal Links among RDF Data Panayiotis Smeros and Manolis Koubarakis WWW2016 Workshop: Linked Data on the Web (LDOW2016) April 12, 2016 - Montral, Canada Outline Introduction Background Developed Methods

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Discovering Spatial and Temporal Links among RDF Data

WWW2016 Workshop: Linked Data on the Web (LDOW2016) April 12, 2016 - Montréal, Canada

Panayiotis Smeros and Manolis Koubarakis

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Outline

  • Introduction
  • Background
  • Developed Methods
  • Implementation
  • Experimental Evaluation
  • Conclusions
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Spatial and Temporal Link Discovery

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Source Source

Enrich the information of datasets with Geospatial and Temporal characteristics Establish semantic relations (links) between entities

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From Locations to Complex Geometries

  • Geonames, OpenStreetMap, etc. are dominated by

location (point) information

  • GeoSPARQL Standard
  • Datasets with rich geospatial and temporal information

– Corine Land Cover (http://datahub.io/dataset/corine-land-cover) – Urban Atlas (http://datahub.io/dataset/urban-atlas) – Products from Satellite Images (http://datahub.io/dataset/sentinel2)

  • State-of-the-art works focus on distance based (similarity)

relations More spatial and temporal relations can be discovered!

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Link Discovery in Fire Monitoring (Example)

Land Cover Municipalities Fire

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Link Discovery in Fire Monitoring (Example)

Land Cover Municipalities Fire

threatens

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Link Discovery in Fire Monitoring (Example)

Land Cover Municipalities Fire

intersects

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Heterogeneity: Geospatial Datasets

_:1 rdf:type geo:Geometry . _:1 geo:hasGeometry "<http://www.opengis.net/def/crs/EPSG/0/4326> POINT(10 20)"^^geo:wktLiteral . _:1 rdf:type strdf:Geometry . _:1 strdf:hasGeometry "<gml:Point crsName="EPSG:2100"><gml:coordinates>10,20 </gml:coordinates></gml:Point>"^^strdf:GML . _:1 rdf:type wgs84Geo:Point . _:1 wgs84Geo:lat “10“^^xsd:double . _:1 wgs84Geo:long “20“^^xsd:double .

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Heterogeneity: Geospatial Datasets

_:1 rdf:type geo:Geometry . _:1 geo:hasGeometry "<http://www.opengis.net/def/crs/EPSG/0/4326> POINT(10 20)"^^geo:wktLiteral . _:1 rdf:type strdf:Geometry . _:1 strdf:hasGeometry "<gml:Point crsName="EPSG:2100"><gml:coordinates>10,20 </gml:coordinates></gml:Point>"^^strdf:GML .

  • Different Vocabularies
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Heterogeneity: Geospatial Datasets

_:1 rdf:type geo:Geometry . _:1 geo:hasGeometry "<http://www.opengis.net/def/crs/EPSG/0/4326> POINT(10 20)"^^geo:wktLiteral . _:1 rdf:type strdf:Geometry . _:1 strdf:hasGeometry "<gml:Point crsName="EPSG:2100"><gml:coordinates>10,20 </gml:coordinates></gml:Point>"^^strdf:GML .

  • Different Vocabularies
  • Different Serializations of Geometries
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Heterogeneity: Geospatial Datasets

_:1 rdf:type geo:Geometry . _:1 geo:hasGeometry "<http://www.opengis.net/def/crs/EPSG/0/4326> POINT(10 20)"^^geo:wktLiteral . _:1 rdf:type strdf:Geometry . _:1 strdf:hasGeometry "<gml:Point crsName="EPSG:2100"><gml:coordinates>10,20 </gml:coordinates></gml:Point>"^^strdf:GML .

  • Different Vocabularies
  • Different Serializations of Geometries
  • Geometries expressed in Different Coordinate

Reference Systems (CRS)

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Heterogeneity: Geospatial Datasets

source

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Heterogeneity: Geospatial Datasets

  • Different Sampling Values
  • Different Granularity
  • Different Rounding Effects

source

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Heterogeneity: Temporal Datasets

_:1 ex:hasBirthday "1989-09- 24T11:05:00+01:00"xsd:dateTime . _:1 ex:hasAffiliation ex:UoA "[2007-09-01T00:00:00+03:00, 2015-08-31T00:00:00+04:00)"^^strdf:Period .

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Heterogeneity: Temporal Datasets

_:1 ex:hasBirthday "1989-09- 24T11:05:00+01:00"xsd:dateTime . _:1 ex:hasAffiliation ex:UoA "[2007-09-01T00:00:00+03:00, 2015-08-31T00:00:00+04:00)"^^strdf:Period .

  • Different Vocabularies
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Heterogeneity: Temporal Datasets

_:1 ex:hasBirthday "1989-09- 24T11:05:00+01:00"xsd:dateTime . _:1 ex:hasAffiliation ex:UoA "[2007-09-01T00:00:00+03:00, 2015-08-31T00:00:00+04:00)"^^strdf:Period .

  • Different Vocabularies
  • Different Time Zones
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Heterogeneity: Temporal Datasets

_:1 ex:hasBirthday "1989-09- 24T11:05:00+01:00"xsd:dateTime . _:1 ex:hasAffiliation ex:UoA "[2007-09-01T00:00:00+03:00, 2015-08-31T00:00:00+04:00)"^^strdf:Period .

  • Different Vocabularies
  • Different Time Zones
  • Time Instants and Periods
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Outline

  • Introduction
  • Background
  • Developed Methods
  • Implementation
  • Experimental Evaluation
  • Conclusions

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Link Discovery (Definition)

Let 𝑇 and 𝑈 be two sets of entities and 𝑆 the set of relations that can be discovered between entities. For a relation 𝑠 ∈ 𝑆, w.l.o.g., we define a distance function 𝑒' and a distance threshold 𝜄*+ as follows: 𝑒': S × T → [0,1] , 𝜄*+ ∈ 0,1 We define the set of discovered links for relation 𝑠 (𝐸𝑀') as follows: 𝐸𝑀' = s, r, t 𝑡 ∈ 𝑇 ⋀ 𝑢 ∈ 𝑈 ⋀ 𝑒' 𝑡,𝑢 ≤ 𝜄*+}

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State-of-the-art Spatial Relations

  • Dimensionally Extended

9-Intersection Model

  • Egenhofer’s Model
  • OGC Simple Features Model
  • Region Connection Calculus

– e.g., RCC8

  • Cardinal Direction Calculus

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Intersects, Overlaps, Equals, Touches, Disjoint, Contains, Crosses, Covers, CoveredBy and Within

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State-of-the-art Temporal Relations

  • Allen’s Interval Calculus
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Outline

  • Introduction
  • Background
  • Developed Methods
  • Implementation
  • Experimental Evaluation
  • Conclusions

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Introduced Relations

  • Spatial (𝑆A), Temporal (𝑆B), Spatiotemporal (𝑆AB) relations
  • Subsets of Boolean relations (𝑆C)

𝑆A, 𝑆B, 𝑆AB⊂ 𝑆C⊂ 𝑆

  • 𝑆C constitutes a special subset of 𝑆. The distance function

𝑒' and the distance threshold 𝜄*+ for a relation 𝑠 ∈ 𝑆C are defined as follows: 𝑒'(s,t) = G0 𝑗𝑔 𝑠 ℎ𝑝𝑚𝑒𝑡 1 𝑓𝑚𝑡𝑓𝑥ℎ𝑓𝑠𝑓 , 𝜄*+ = 0

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Introduced Transformations (1/2)

  • Vocabulary Transformation

– converts the vocabulary of geometry literals into GeoSPARQL

  • Serialization Transformation

– converts the serialization of geometries into WKT

  • CRS Transformation

– converts the CRS of geometries into the World Geodetic System (WGS 84)

  • Validation Transformation

– converts not valid geometries (e.g., self-intersecting polygons) to valid ones

  • Simplification Transformation

– simplifies geometries according to a given distance tolerance

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Introduced Transformations (2/2)

  • Envelope Transformation

– computes the envelope (minimum bounding rectangle) of geometries

  • Area Transformation

– computes the area of geometries in square metres

  • Points-To-Centroid Transformation

– computes the centroid of a cluster of points

  • Time-Zone Transformation

– converts the time zone of time elements to Coordinated Universal Time (UTC)

  • Period Transformation

– converts time instants to periods with the same starting and ending point

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Techniques for Checking the Relations

  • Cartesian Product Technique (Naive)

– Exhaustive checks between the pairs of the entities of datasets – Complete – Complexity: O(|S||T|) checks

  • Blocking Technique

– Decreases the number of checks – Divides the entities into blocks – Complexity: O(|S||T|) checks (worst case), O(|L|) checks (best case)

* |S|, |T|: number of entities in datasets S and T; |L|: number of links between datasets S and T

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Blocking Technique (algorithm)

1. Divide the surface of the earth into curved rectangles / the time into intervals (blocks) 2. Adjust the size of the blocks with a blocking factor (𝑡𝑐𝑔 or t𝑐𝑔) 3. Insert the entities into the corresponding blocks 4. Check for the actual relation within each block 5. Aggregate the links from all the blocks to construct 𝐸𝑀'

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Blocking Technique (algorithm)

b1 b2 b3 b4 e1 e2

e1: b1, b2 e2: b2, b4

b1 b2 e1 e2

e1: b1, b2 e2: b2

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Blocking Technique (algorithm)

1. Divide the surface of the earth into curved rectangles / the time into intervals (blocks) 2. Adjust the size of the blocks with a blocking factor (𝑡𝑐𝑔 or t𝑐𝑔) 3. Insert the entities into the corresponding blocks 4. Check for the actual relation within each block 5. Aggregate the links from all the blocks to construct 𝐸𝑀'

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Blocking Technique (accuracy)

  • Sound and complete
  • 𝑄𝑠𝑓𝑑𝑗𝑡𝑗𝑝𝑜 =

STU STUVWTU = STU STU = 100%

  • 𝑆𝑓𝑑𝑏𝑚𝑚 =

STU STUVWZTU = STU STU = 100%

TDL: True Discovered Links FDL: False Discovered Links FNDL: False Not Discovered Links

Guaranteed 100% accurate links

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Outline

  • Introduction
  • Background
  • Developed Methods
  • Implementation
  • Experimental Evaluation
  • Conclusions

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Extensions to the Silk Framework

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Extensions to the Silk Framework

  • Implemented as Plugins
  • Transparent to all the applications of Silk (Single

Machine, MapReduce and Workbench)

  • Included in the the default Silk distribution (from

release 2.6.1 and above)

  • https://github.com/silk-framework/silk
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Outline

  • Introduction
  • Background
  • Developed Methods
  • Implementation
  • Experimental Evaluation
  • Conclusions

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Real-world Scenario (Fire Monitoring)

  • Which fires (hotspots) threaten forests?
  • Which municipalities are threatened by fires?
  • Using Silk: Discover the relation intersects between

HG-GAG and HG-CLCG

Dataset #Entities Geometries Time Elements Type #Points Type #Instants Municipalities from Greek Administrative Geography (GAG) 325 Polygons 979,929 Periods 650 Forests from CORINE Land Cover of Greece (CLCG) 4,868 Polygons 8,004,058 Periods 9,736 Hotspots of Greece (HG) 37,048 Polygons 148,192 Instants 37,048

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Real-world Scenario (Fire Monitoring)

Land Cover (CLCG) Municipalities (GAG) Fire (HG)

intersects

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Environment of Experiments

  • Single machine environment

– 2 Intel Xeon E5620 processors, 12MB L3 cache, 2.4 GHz, 32 GB RAM, RAID-5. 4 disks, 32 MB cache, 7200 rpm

  • Distributed environment

– cluster provided by the European Public Cloud Provider Interoute (1 Master Node + 20 Slave Nodes: 2 CPUs, 4GB RAM, 10GB disk)

  • More details: http://silk.di.uoa.gr
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Experiment 1: Adjusting the Spatial Blocking Factor (sbf)

50000 100000 150000 200000 1000 2000 3000 4000 5000 6000 7000 8000 0.5 1 5 10 20 50 100

Links Time (seconds) Spatial Blocking Factor

HG-CLCG HG-GAG Links

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Experiment 2: Adjusting the number of Entities per Dataset

0.1 1 10 100 1000 10000 100000 1000000 0.1 1 10 100 1000 10000 100000 1000000 10 100 1000* all

Links Time (seconds) Entities per Dataset

Silk (Baseline) Silk (Best sbf) Strabon Silk (MR) Links

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Outline

  • Introduction
  • Background
  • Developed Methods
  • Implementation
  • Experimental Evaluation
  • Conclusions

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Conclusions & Future Work

  • Methods for Spatial and Temporal Link Discovery
  • Implementation on the Silk framework
  • Employed efficiently in Real-World Applications
  • Support more relation models/calculi
  • Make the algorithm parameter free

– Estimate the optimal value for the 𝑐𝑔𝑡 – Pose preprocessing queries

  • Use approximate blocking techniques
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Thanks for your attention! Questions?