Computing Geo-Spatial Motives from Linked Data for Search ...€¦ · Making web and data...

A. Both – KNOW LOD workshop, ESWC 2015 — Portoroz, 2015-05-31 Slide 1

Computing Geo-Spatial Motives from LinkedData for Search-driven Applications

Andreas Both, Liliya Avdiyenko, Christiane Lemke (Unister GmbH)

Unister Group: Multi-brand strategy

eCommerce segments

travel

comparison

ventures

R&D Department

Making web and data techolo-gies useful, focus: B2C search

Unister Group: Multi-brand strategy

eCommerce segments

travel

comparison

ventures

R&D Department

Making web and data techolo-gies useful, focus: B2C search

making the web an exploratory place for geospatial data

GeoKnow in a nutshell

For whom

Bring geospatial knowledge integration to the Linked DataWeb

Billion-triple geospatial reasoning and data provenance

Qualitative interlinking and fusing of geospatial and semanticinformation

Adaptive geospatial exploration, authoring and curation

For whom

Unlock isolated islands of geographic information

80% of all data has some spatial dimension, most of it is notprocessable today

Geographic data authoring with millions of users requirespowerful tools

For whom

Added value for the companies and the Linked Data Webcommunity

Cost-effective data integration for SMEs

Enterprises can add value to their data with volunteeredgeographic information

Users from travel industry will benefit from more backgroundinformation

GeoKnow Consortium

Example

winter holidays with culture

Possible Interpretation

winter holidays: places in the mountains with at least threeski lifts

culture: places that offer culture points of interests

Example

Problem Description

Users Perspektive

data needs to be accessible

B2C context → need easy-to-use interface

Linked Data Perspektive

many data sets are available

data sets are interlinked → many features for training

Problem Description

Users Perspektive

data needs to be accessible

B2C context → need easy-to-use interface

Linked Data Perspektive

many data sets are available

data sets are interlinked → many features for training

Linked Data Life Cycle

Research Questions

Geospatial Regions of Interest

A motive can be defined as the reason for a search as wellas particular conditions like how much a product shouldcost.

Research Questions

Can one use machine learning techniques on features of linkeddata for computing motives?

Is it beneficial to aggregate data from different sources?

Research Questions

Data Sets (Geo-spatial Entities)

DBpedia

collections of entities with geospatial position

Natural Earth

curated populated places

polygons for countries

Geonames

attributes for geospatial entities

Note: Data was interlinked already (manually curated).

Approach

Compute Regions of Interest

1 extract related entities

2 define training data by experts for the given motive

3 use training set to learn relevant places

→ check in case study

Approach

Compute Regions of Interest

→ check in case study

Case Study

Educational Regions

regional character → analyses per country

Case Study

Educational Regions

Case Study

Educational Regions in Central Europe

Case Study: Education

Process: 3 Steps

extract all entities of DBpedia with typehttp://dbpedia.org/ontology/EducationalInstitution

3 expertsquestion: Is the considered place important w.r.t. education inthis country?

Process: 3 Steps

Training

Reminder: Research Questions

Approach: iterative integration of linked data

1 DBpedia (educational entities) and populated places (NaturalEarth)

2 add all features of populated places (Natural Earth)

3 add all features of GeoNames

feature selection after each step

Training

Experimental Settings

Training Data

881 samples of training data

average expert rating normalized from [0, 2] to relevant orirrelevant

Training Process

200 runs

Breiman’s random forest (cross validation)

Experimental Settings

Training Data

881 samples of training data

average expert rating normalized from [0, 2] to relevant orirrelevant

Training Process

200 runs

Breiman’s random forest (cross validation)

Results

Datasets D1 D2 D3

features entities nearby 10kmentities nearby 25kmentities nearby 50km

all features of D1and

max of statese in countryworldcitypop maxrank maxmax areami

gn pop

TP rate 0.73 0.79 0.80FP rate 0.57 0.42 0.41

F-measure 0.71 0.78 0.79Precision 0.70 0.78 0.79

AUC 0.64 0.82 0.83Accurancy 0.73 0.79 0.80

Results

Datasets D1 D2 D3

gn pop

TP rate 0.73 0.79 0.80FP rate 0.57 0.42 0.41

AUC 0.64 0.82 0.83Accurancy 0.73 0.79 0.80

Results

Datasets D1 D2 D3

gn pop

TP rate 0.73 0.79 0.80FP rate 0.57 0.42 0.41

AUC 0.64 0.82 0.83Accurancy 0.73 0.79 0.80

Conclusion and Future Work

presented an approach for answering commonly questions

focus: geo-spatial motives in search queries

approach based on machine learning

iterative data integration (linked data)

→ winter holidays with culture

presented an approach for answering commonly questions

focus: geo-spatial motives in search queries

approach based on machine learning

iterative data integration (linked data)

→ winter holidays with culture

Reseach Questions

Can one use machine learning techniques on features of linked datafor computing motives?

approach for deriving regions of interest

geospatial search motives are covered with a good quality

however, time-consuming approach

quality increased

even duplicate attributes support

Reseach Questions

Can one use machine learning techniques on features of linked datafor computing motives?

approach for deriving regions of interest

geospatial search motives are covered with a good quality

however, time-consuming approach

quality increased

even duplicate attributes support

Major Open Research tasks

How to reduce the manual effort?

How to handle low quality data sets?

How to compute regions unrelated to populated places?

Take Away

search-driven applications need tobridge the user intentions

linked data helps at getting the data

geo-spatial motives can be covered

reduction of manual effort is needed

Dr. Andreas Both

Head of Researchand Development

Unister GmbH,Leipzig, Germany

[email protected]

+49 341 65050 24496

http://www.unister.de

Metrics

Evaluation metrics for a binary classifier (defined in terms ofpositives (P), negatives (N), true positives (TP), true negatives

(TN) and false positives (FP))

Metric Definition

True positive rate (recall) TPR = TP/P

False positive rate FPR = FP/P

Precision Pr = TP/(TP + FP)

F-measure F = 2 ∗ Pr ∗ TPR/(Pr + TPR)

AUC area under the curve depicting TPR plotted against FPR

Accuracy Acc = (TP + TN)/(P + N)

Computing Geo-Spatial Motives from Linked Data for Search ...€¦ · Making web and data...

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