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Semantic Integration of Data Yannis Tzitzikas Computer Science Department, University of Crete, GREECE & Information Systems Laboratory (ISL) Institute of Computer Science (ICS) Foundation for Research and Technology – Hellas (FORTH)

ITN-DCH summer school 2016 (@CGI'16), Heraklion, Crete, Greece, June 27, 2016

Outline

• Motivation

• Requirements

• Case Study: Marine Species Data

• Challenges and Conclusions

Yannis Tzitzikas, ITN-DCH summer school 2016 (@CGI'16)

Time plan: 25’ presentation, 5’ questions and discussion

Slides: will be publicly available after the school

2

Motivation

Huge amounts of data are available and this amount constantly increases.

Almost everyone produces data (and everything will produce data).

Almost everyone needs data (and everything will need data).


Motivation

However data and information is not integrated.


Motivation


Hundreds or thousands of CKAN catalogs each containing hundreds

or thousands of datasets

However data and information is not integrated.

Motivation In several domains and applications one has to fetch and assemble pieces of information coming from more than one sources for being able to answer complex queries (that are not answerable by individual sources) or analyze the integrated data. This important for science but also for our daily life.

This is true in science in general

Biodiversity domain

Cultural Domain

E-Government

Science in general

…

Personal data

Yannis Tzitzikas, ITN-DCH summer school 2016 (@CGI'16) 6

50%-90% of time for data collection and cleaning

It has been written that

Data scientists spend from 50 percent to 80 percent of their time in collecting and preparing unruly digital data, before it can be explored for useful nuggets.

If you’re trying to reconcile a lot of sources of data that you don’t control it can take 80% of your time

One-Third of BI Pros Spend Up to 90% of Time Cleaning Data


Indicative Complex Queries

Marine Domain

Given the scientific name of a species (say Thunnus Albacares), find the ecosystems, waterareas and countries that this species is native to, and the common names that are used for this species in each of the countries, as well as their commercial codes


Thunnus Albacares

El Greco

Cultural Domain Give me all paintings of El Greco that are now

exhibited in Greece and their location , as well as all articles or books about these paintings between 2000 and 2016.

Give me the paintings of El Greco referring to persons that were born between 0 and 300 BC.

Give me all events related to El Greco that will take place this month in Heraklion.

Why integration is difficult?

Datasets are kept or produced by different organizations in different formats, models, locations, systems.

The same real world entities or relationships are referred with different names and in different natural languages (natural languages have synonyms and homonyms)

Datasets usually contain complementary information

Datasets can contain erroneous or contradicting information

Datasets about the same domain may follow different conceptualizations of the domain


… names

348 common names in 82 different languages!


Thunnus Albacares

ed Pa'ak Pukeu Sisek kuneng Geelvin-tuna Geelvin-tuna Tuna Tambakol Gubad Jaydher Kababa Shak zoor Tuna sirip kuning Rambayan Tambakol Bangkulis Bankulis

Bronsehan Buyo Kikyawon Paranganon Manguro O'maguro Tag-hu Taguw Taguw peras Taguw tangir Barelis Bariles Barilis Carao Karaw Pak-an Pala-pala Panit Panitto Pirit

Tulingan Kacho Bariles Karaw Panit M'Bassi Mbasi bankudi Mibassi mibankundri Thon a nageoires jaunes Thon jaune Ton zonn Z'ailes jaunes Albacora Atum olede Chefarote

Rabo-seco Gulfinnet tun Gulfinnet tunfisk Bariles Bugo Karaw Geelvintonijn 'Fin Albacore Allison tuna Allison tuna Allison tuna Allison's tuna Atlantic yellowfin tuna Autumn

albacore Long fin tunny Longfin Pacific long-tailed tuna Tuna Tuna Yellow fin tuna Yellow tunny Yellow-fin tuna Yellow-fin tuna Yellow-fin tuna Yellow-fin tunny Yellowfin

Yellowfin Yellowfin tuna Yellowfinned albacore Kulduim-tuun Gegu Tuna Yatu Yatunitoga Keltaevatonnikala Albacore Gegu Grand fouet Guegou Thon Thon a nageoires jaunes

Thon jaune Thon rouge Atu igu mera Albacore Gelbflossen-Thunfisch Gelbflossenthun Tonnos macrypteros Gedar Gedara Ahi Kahauli Kanana Maha'o Palaha Shibi Bantala-

an Panit Oriles Tambakul Tonno albacora Tonno monaco Tunnu monicu Tiklaw Vahuyo Kihada Panit Baewe Baibo Baiura Te baewe Te baibo Te bairera Te baitaba Te

ingamea Te ingimea Te inginea Bokado Olwol Malaguno Tambakol Gantarangang Lamatra Aya Aya tuna Bakulan Gelang kawung Kayu Tongkol Tuna Tuna ekor kuning Tuna

sirip kuning Poovan-choora Kannali-mas Bariles Panit Bugudi Gedar Kuppa Pimp Bwebwe Tetena keketina Vahakula Albacore Albakor To'uo Balang kuni Ghidar Albakora

Tunczyk zoltopletwy a. albakora Albacora Albacora Albacora Albacora Albacora Albacora da laje Albacora de lage Albacora-cachorra Albacora-da-lage Albacora-de-laje

Albacora-lage Albacora-lajeira Alvacor Alvacora Alvacora-lajeira Atum Atum Atum albacora Atum albacora Atum albacora Atum rabil Atum-albacora Atum-amarelo Atum-de-

barbatana-amarela Atum-de-galha-a-re Atum-galha-amarela Galha a re Ielofino Peixe de galha a re Peixe-de-galha-a-re Peixinho da ilho Rabao Rabil Rabo-seco Albacora Ton

galben Albacor Tikhookeanskij zheltoperyj tunets Zheltokhvostyj tunets Asiasi Gaogo Ta'uo To'uo Tuna zutoperka Zutorepi tunj As geddi kelawalla Howalla Howalla Kelawalla

Kelawalla Pihatu kelawalla Yajdar-baal-cagaar Albacora Albacora Albacora aleta amarilla Aleta amarilla Aleta amarilla Atun aleta amarilla Atun aleta amarilla Atun aleta amarilla

Atun aleta amarilla Atun aleta amarilla Atun de aleta amarilla Atun de aleta amarilla Atun de aleta amarilla Atun de aleta amarilla Atun de aleta amarilla Atun de aletas amarillas

Rabil Rabil Rabil Rabil Bariles Jodari Albacora Gulfenad tonfisk Albakora Badla-an Barilis Buyo Tambakol A'ahi A'ahi 'oputea A'ahi 'oputi'i A'ahi hae A'ahi mapepe A'ahi maueue

A'ahi patao A'ahi tari'a'uri A'ahi tatumu A'ahi teaamu A'ahi tiamatau A'ahi vere Otara Kelavai Soccer Soccer Tekuu Atutaoa Kahikahi Kakahi Kakahi/lalavalu Takuo Takuo

Kahikahi Kakahi Sari kanat orkinos Sar?kanatorkinoz bal?g? Sar?kanatton bal?g? Te kasi Ca bo vang Ca Ng? vay vang Nkaba Badla-an Balarito Malalag Painit Panit Baliling

Panit Tiwna melyn Doullou-doullou Ouakhandar Wakhandor Waxandor Wockhandor

… names


argentina

…. complementary views


dataset

reality

dataset

dataset dataset

dataset

dataset

…different conceptualizations


reality

General Requirements or Tasks related to Information Integration Dataset discovery

Dataset selection (or sub-dataset selection)

Dataset access and query

Fetch and transformation of data

Data and dataset linking

Data cleaning

Data completion (through context, inference, prediction or other methods)

Management of data provenance

Measuring and testing the quality of datasets (especially the integrated)

Management (and understanding) of the evolution of datasets

Monitoring, production of overviews, visualization of datasets

Interactive browsing and exploration of datasets

Data summarization, preservation


focus


Outline

• Main approaches for integration

• The notion of Semantic Warehouse

• Case Study: Integrating information about marine species • The role Top-Level Ontologies

• Automating the process

• Measuring the quality of semantic integration

• Provenance Issues

• Exploitation

Main approaches for Integration

In general there are two main approaches for integration

Warehouse approach (materialized integration)

• Design Phase:

• The underlying sources (and their parts) have to be selected

• Creation Phase:

• Process for getting and creating the warehouse

• Maintenance Phase:

• Ability to create the warehouse from scratch, and/or ability to update parts of it

• Mappings are exploited to extract information from data sources, to transform it to the target model and then to store it at the central repository

Mediator approach (virtual integration)

• The mediator receives a query formulated in terms of the unified model/schema. The mappings are used to enable query translation. The derived sub-queries are sent to the wrappers of the individual sources, which transform them into queries over the underlying sources. The results of these sub-queries are sent back to the mediator where they are assembled to form the final answer


Main approaches for integration (cont.)

Warehouse • Benefit: Flexibility in transformation

logic (including ability to curate and fix problems)

• Benefit: Decoupling of the release management of the integrated resource from the management cycles of the underlying sources

• Benefit: Decoupling of access load from the underlying sources.

• Benefit: Faster responses (in query answering but also in other tasks, e.g. if one wants to use it for applying an entity matching technique).

• Shortcomings You have to pay the cost for hosting the warehouse. You have to refresh periodically the warehouse

Mediator • Benefit: One advantage (but in some

cases disadvantage) of virtual integration is the real-time reflection of source updates in integrated access

• Comment: The higher complexity of the system (and the quality of service demands on the sources) is only justified if immediate access to updates is indeed required.


Case Study: Marine Species


Context: iMarine


Id: An FP7 Research Infrastructure Project (2011-2014)

Final goal: launch an initiative aimed at establishing and operating an e-infrastructure supporting the principles of the Ecosystem Approach to fisheries management and conservation of marine living resources.

Partners:

19

Continuation in BlueBRIDGE

BlueBRIDGE (Building Research environments for fostering Innovation, Decision making, Governance and Education to support Blue growth), H2020-EINFRA-2015-1

Sept. 2015- Feb. 2018


Marine Information: in several sources

WoRMS: World Register of Marine Species

Registers more than 200K species

ECOSCOPE- A Knowledge Base About Marine Ecosystems (IRD, France)

FLOD (Fisheries Linked Data) of

Food and Agriculture Organization (FAO) of the United Nations

FishBase: Probably the largest and most extensively

accessed online database

of fish species.

DBpedia



Taxonomic information

Ecosystem information (e.g. which fish eats which fish)

Commercial codes

General information, occurrence data, including information from other sources

General information, figures


Storing

complementary information

22


Web services (SOAP/WSDL)

RDF + OWL files

SPARQL Endpoint

Relational Database

SPARQL Endpoint


Accessed through

different technologies

23

..


How to integrate

24

Scope Control (how to control it?)

We use the notion of competency queries.

A competency query is a query that is useful for the community at hand, e.g. for a human member , or for building applications for that domain

Indicative competency queries for our running example:


Materialization or Mediation?

In both cases we need a unified model/schema


The Top Level Ontology: MarineTLO MarineTLO aims at being a global core model that – provides a common, agreed-upon and understanding of the concepts and

relationships holding in the marine domain to enable knowledge sharing, information exchanging and integration between heterogeneous sources

– covers with suitable abstractions the marine domain to enable the most fundamental queries, can be extended to any level of detail on demand, and

– allows data originating from distinct sources to be adequately mapped and integrated

• MarineTLO is not supposed to be the single ontology covering the entirety of what exists

Benefits:

reduced effort for improving and evolving : the focus is given on one model, rather than many (the results are beneficial for the entire community)

reduced effort for constructing mappings: this approach avoids the inevitable combinatorial explosion and complexities that results from pair-wise mappings between individual metadata formats and/or ontologies


MarineTLO: Query capabilities

It should allow formulating the competency queries.

Indicative examples of queries that can be formulated

1.Given the scientific name of a species, find its predators with the related taxon-rank

classification and with the different codes that the organizations use to refer to them.

2. Given the scientific name of a species, find the ecosystems, waterareas and countries that this species is native to, and the common names that are used for this species in each of the countries

The MarineTLO currently contains around

90 classes and 40 properties.

More in www.ics.forth.gr/isl/MarineTLO


Materialization or Mediation?


We will focus on the materialization case

i.e on the construction and maintenance of a MarineTLO-based semantic warehouse

Semantic warehouse

..


Integration process

30

The warehouse construction and evolution process


Define requirements in terms

of competency queries

Fetch the data from the selected sources

(SPARQL endpoints, services, etc)

Queries

Transform and Ingest to the Warehouse

Inspect the connectivity of the

Warehouse

Formulate rules creating sameAs

relationships

Apply the rules to the warehouse

Rules for Instance Matching

sameAs triples Ingest the sameAs relationships

to the warehouse

Test and evaluate the Warehouse (using

the competency queries and the conn. metrics)

creates

Warehouse

produces

Triples

uses

uses

uses

MatWare

MatWare

MatWare

MatWare

MatWare

Expressed over

MarineTLO

31

How to connect the fetched pieces of information?

The Semantic Approach

Use URIs instead of strings

You can establish links in this way

You can avoid the problem of homonyms

Use owl:sameAs to connect equivalent URIs

Various other semantic relationships


Linked Data is a method of publishing structured data so that it can be interlinked and become more useful through semantic queries. It builds upon standard Web technologies such as HTTP, RDF and URIs. This enables data from different sources to be connected and queried

https://en.wikipedia.org/wiki/Semantic_query



https://en.wikipedia.org/wiki/Hypertext_Transfer_Protocol

https://en.wikipedia.org/wiki/Resource_Description_Framework

https://en.wikipedia.org/wiki/Uniform_resource_identifier

How to link

We need Entity Matching

Both automatic methods and handcrafted rules are required


Example: Suffix-based URI equivalence


http://www.dbpedia.com/Thunnus_Albacares http://www.ecoscope.com/thunnus_albacares

Thunnus_Albacares thunnus_albacares

ThunnusAlbacares thunnusalbacares

thunnusalbacares thunnusalbacares

≡

=

prefix removal

underscore removal

lower case conversion

last(u): is the string obtained by (a) getting the substring after the last "/" or "\#",

and turning the letters of the picked substring to lowercase and deleting the

underscore letters that might exist. 34

http://www.dbpedia.com/Thunnus_Albacares









Example: Entity Matching-based URI Equivalence


Yannis Tzitzikas et al., LWDM 2014, Athens

http://www.ecoscope.com/thunnus_albacares http://www.fao.org/figis/flod/entities/codedentity/

636cdcea-c411-43ad-97ff-00c9304f5e60

Matching Rule: If an Ecoscope individual's preflabel in lower case is the same with the

attribute label of a FLOD individual then these two individuals are

the same.

Thunnus Albacares thunnus albacares

skos:preflabel label

sameAs

http://www.fao.org/figis/flod/entities/codedentity/

..


How to measure the quality of the warehouse?

36

Connectivity Assessment

In general Connectivity has two main aspects: Schema and Instance.

Regarding Schema Connectivity our running example uses a top level ontology (MarineTLO) and schema mappings in order to associate the fetched data with the schema of the top level ontology.

As regards Instance Connectivity one has to inspect and test the connectivity of the “draft” warehouse through the competency queries, and a number of connectivity metrics that we have defined and then formulate rules for instance matching


Why it is useful to measure Connectivity? For assessing how much the aggregated content is connected

For getting an overview of the warehouse

For quantifying the value of the warehouse (query capabilities)

o Poor connectivity affects negatively the query capabilities of the warehouse.

For making easier its monitoring after reconstruction

For measuring the contribution of each source to the warehouse, and hence deciding which sources to keep or exclude (there are already hundreds of SPARQL endpoints). Identification of redundant or unconnected sources

Connectivity Metrics Definition


Connectivity Metrics:

Increase in the average Degree


The average degree is increased from 18.72 to 23.92.

The average degree, of all sources is significantly

bigger than before.

Suffix canonicalization

Entity Matching

39

Connectivity Metrics: Exchanging

The metrics can also be exchanged for assisting dataset discovery or dataset selection (in a mediator-based architecture). We have extended VoID (Vocabulary of Interlinked Datasets) for representing and exchanging such metrics (VoIDWarehouse)


VoIDWarehouse

Connectivity Metrics: Exchanging (cont).


1. Compute of the Connectivity Metrics-Production of Matrixes

2. Describe the Connectivity Metrics with the proposed VoID extension

3. Store these triples in a separate graph space

4. Retrieve/Query these values from the warehouse using SPARQL queries

41

..


Provenance

42

Provenance

It is important to keep the provenance of each data in the warehouse.

We have realized that the following 4 levels of provenance support are usually required:

[a] Conceptual level

[b] URIs and Values level

[c] Triple Level

[d] Query level

Level [a] can be supported by the conceptual model level. In our application context we use the MarineTLO and the transformation rules do the required transformations.

Matware offers support also for levels [b]-[d]


Provenance

a) Conceptual modeling level Example: Assignment of identifiers to species

MarineTLO models the provenance of species names, codes etc, and the Transformation rules of MatWare transform the ingested data according to this model.


Thunnus albacares

YFT

127027

isIdentifiedBy

isIdentifiedBy

FAOCode

WoRMSCode hasCodeType

hasCodeType

44

Provenance

b) URIs and Literals : i. Adopting the namespace mechanism for URIs:

- The prefix of the URI provides information about the origin of the data.

- e.g. www.fishbase.org/entity/ecosystem#mediterannean_sea

ii. Ability to attach @Source to every literal coming from a Source:

- e.g. select scientific name and authorship of Yellow Fin Tunna

- This policy allows formulating source-centric queries in a relative simple way:

SELECT ?speciesname

WHERE {

?species tlo:has_scientific_name ?scientificname

FILTER(langMatches(lang(?scientificname), “worms"))

}


http://www.fishbase.org/entity/ecosystem

Provenance

c) Triple Level Provenance

• Store the fetched triples in a separate graphspace: FISHBASE: http://www.ics.forth.gr/isl/Fishbase

DBpedia: http://www.ics.forth.gr/isl/DBpedia

FLOD: http://www.ics.forth.gr/isl/FLOD

Ecoscope: http://www.ics.forth.gr/isl/Ecoscope

WoRMS: http://www.ics.forth.gr/isl/WoRMS

• By asking for the graph that each triple is coming from we retrieve the provenance of the data.

• This enables refreshing only one part of the warehouse


http://83.212.113.62:8890/conductor/sparql_graph.vspx?mode=edit&graph_name=http://www.ics.forth.gr/isl/Fishbase&sid=a1a28f30b07f261c9f33585af51d8b56&realm=virtuoso_admin

Provenance

d) Query Level Provenance • Matware offers a query rewriting functionality that exploits the contents of the

graphspaces for returning the sources that contributed to the query results (including those that contributed to the intermediate steps).

• Let q be a SPARQL query that has n parameters in the select clause and contains k triple patterns of the form (?s_i, ?p_i, ?o_i) :

SELECT {?o_1 ?o_2} WHERE {

?s_1 ?p_1 ?o_1 .

?s_2 ?p_2 ?o_2 .

?s_k ?p_k ?o_k

}

• The rewriting produces a query q’ that has n+k parameters in the select clause and each triple pattern (?si ?pi ?oi) has been replaced by: graph ?gi {?si ?pi ?oi}. Eventually the rewritten query q’ is:

SELECT {?o_1 ?o_2 ?g_1 ?g_2 ?g_k} WHERE {

graph ?g_1 {?s_1 ?p_1 ?o_1 }.

graph ?g_2 { ?s_2 ?p_2 ?o_2} .

graph ?g_3 {?s_k ?p_k ?o_k}

}


Provenance

Example of Query Level Provenance:

Query: For a scientific name of a species (e.g. Thunnus Albacares) find the FAO codes of the waterareas in which the species is native.

Query in SPARQL:

select ?faocode

where {

graph ?source1 {

ecoscope:thunnus_albacares MarineTLO:isNativeAt ?waterarea

}.

graph ?source2 {

?waterarea MarineTLO:LXrelatedIdentifierAssignment ?faocode

} }

RESULT:

?source1 ?source2


Architecture of Matware

Actions in order to create a Warehouse from scratch one should specify

the type of the repository

the names of the graphs that correspond to the different sources

URL, username and password in order to connect to the repository

Actions in order to add a new source

(a) include the fetcher class for the specific source as plug in

(b) provide the mapping files (schema mappings)

(c) include the transformer class for the specific source as a plug in

(d) provide the SILK rules as xml files Yannis Tzitzikas, ITN-DCH summer school 2016 (@CGI'16) 49

The resulted MarineTLO-based Warehouse(1/2)


Integrated information about Thunnus albacares from different sources

50

The resulted MarineTLO-based Warehouse(2/2)

iMarine 2nd Review, September 2013,Brussels

Concepts Ecoscope FLOD WoRMS DBpedia Fishbase

Species

Scientific Names

Authorships

Common Names

Predators

Ecosystems

Countries

Water Areas

Vessels

Gears

EEZ


Evolution over Time

< some plots>

Need for visualization


Exploitation of the Semantic Warehouse


Exploitation of the Semantic Warehouse

A) Semantic Processing of Search Results (e-infrastructure service)

B) Fact Sheet Generator (web application)

C) Species Identification Tool

D) Interactive 3D visualization


A) For Semantic Post-Processing of Search Results: The process

query terms (top-L) results

(+ metadata)

Entity Mining

Semantic Analysis

Visualization/Interaction (faceted search, entity

exploration, annotation, top-k graphs, etc.)

entities / contents

semantic data

web browsing

contents

• Grouping, • Ranking • Retrieving more

properties

MarineTLO

Warehouse


A) For Semantic Post-Processing of Search Results: Example (X-Search)

Search Results

Result of Entity

Mining

Result of textual

clustering


The Warehouse is used


56

From FLOD

From DBpedia

From Ecoscope

From WoRMS

Example of the

EntityCard of Thunnus

Albacares



A’) XSearch as a bookmarklet Dynamic annotating of entities over any Web page

Entity exploration



58

B) Fact Sheet Generator & Android Application


Fact Sheet Generator Ichthys

59

C) Species Identification Tool

Species identification through Preference-enriched Faceted Search over the semantic descriptions of fish species


D) Interactive 3D Visualization of Datasets


The metrics are exploited for producing interactive 3D visualization of datasets

This approach is general


Integrated information about Thunnus albacares from different sources

62

Datasets about

Crete

Datasets about

Toledo

Datasets about

Art

The big picture (core concepts and relations)

Y. Marketakis and Y. Tzitzikas (FORTH), Edinburg, March 2012 63

Molecular world

and parts

Species (bio,geo)

Activities

Records Observations

from

cross reference

exemplifies

describe

use to

appear in

Samples or

Specimen

exemplifies collections

part of

databases

Publications

Simulation

Place

Time

is

about

global

indices

services

Forecasts

Complex

System

Situation

occurs in create maintain

from

mathem.

Models

based on

Products of Human Activities

Human Activities

The core conceptualization of Earth Sciences

..


What’s next

64

Challenges and our ongoing research


Emphasis on

Dataset discovery, dataset recommendation, dataset selection (e.g. in mediator-based integration)

Finding all URIs of an entity

Finding all triples of an entity

More effective visualizations, monitoring, quality testing, trust estimation

Concluding Remarks

Semantic integration could boost data-intensive scientific discovery but requires tackling several challenging issues

We have discussed main requirements and challenges in designing, building, maintaining and evolving a real and operational semantic warehouse for marine resources

We have presented the process and related tools that we have developed for supporting this process with emphasis on Scope control, Connectivity assessment, Provenance, Reconstructability, Extensibility

Currently we focus on applying this approach for large number of datasets


Links (1/2)

MatWare (for automating the warehouse construction process)

• http://www.ics.forth.gr/isl/MatWare/

MarineTLO (top-level ontology)

• http://www.ics.forth.gr/isl/MarineTLO/

Semantic Warehouses MarineTLO-Warehouse: http://virtuoso.i-marine.d4science.org:8890/sparql

– also browsable through http://virtuoso.i-marine.d4science.org:8890/fct

XSearch (exploiting semantic warehouses in searching)

• http://www.ics.forth.gr/isl/X-Search/

Xlink (exploiting semantic warehouses for entity identification in texts)

• http://www.ics.forth.gr/isl/X-Link

<xsearch and Xlink>


http://www.ics.forth.gr/isl/MatWare/

http://www.ics.forth.gr/isl/MarineTLO/

http://virtuoso.i-marine.d4science.org:8890/sparql



http://virtuoso.i-marine.d4science.org:8890/fct






http://www.ics.forth.gr/isl/X-Link



Links (2/2)

Hippalus: Preference-enriched Faceted Search www.ics.forth.gr/isl/Hippalus

o Select a dataset from the Marine Biology domain for enacting the species identification through PFS

Interactive 3D Visualization of the LOD Cloud www.ics.forth.gr/isl/3DLod/

LODSyndesis: (measuring the commonalities in the entire LOD) www.ics.forth.gr/isl/LODsyndesis


http://www.ics.forth.gr/isl/Hippalus

http://www.ics.forth.gr/isl/3DLod/

http://www.ics.forth.gr/isl/3DLod/

http://www.ics.forth.gr/isl/LODsyndesis

References

On connectivity metrics

• Y. Tzitzikas, et al, Quantifying the Connectivity of a Semantic Warehouse, 4th International Workshop on Linked Web Data Management, LWDM'14@ EDBT'14)

• M. Mountantonakis et al, Extending VoID for Expressing the Connectivity Metrics of a Semantic Warehouse, 1st International Workshop on Dataset Profiling & Federated Search for Linked Data (PROFILES'14), ESWC'14,

• M. Mountantonakis, N. Minadakis, Y. Marketakis, P. Fafalios and Y. Tzitzikas, Quantifying the Connectivity of a Semantic Warehouse and Understanding its Evolution over Time, International Journal on Semantic Web and Information Systems (IJSWIS), (accepted for publication in 2016), will appear with DOI:

Recent work on for integrating large number of datasets

M. Mountantonakis and Y. Tzitzikas, On Measuring the Lattice of Commonalities Among Several Linked Datasets, VLDB’16, Sept 2016


Acknowledgements

Joint work with

Michalis Mountantonakis

Nikos Minadakis

Yannis Marketakis

Pavlos Fafalios

Panagiotis Papadakos

Chryssoula Bekiari

Martin Doerr

Maria Papadaki


Thank you for your attention


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