# Teasing journalistic findings out of heterogeneous sources: a data/AI journey (keynote)

Ioana Manolescu

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## To cite this version:

Ioana Manolescu. Teasing journalistic findings out of heterogeneous sources: a data/AI journey (keynote). DASFAA 2022 - Database Systems for Advanced Applications, Apr 2022, Hyderabad, India. , Proceedings of Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Lecture Notes in Computer Science 13245. hal-04216384

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Submitted on 24 Sep 2023

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# Teasing journalistic findings out of heterogeneous sources: a data/AI journey

Ioana Manolescu

Inria Saclay-Île-de-France, Institut Polytechnique de Paris

U

LISBOA

UNIVERSIDADE
DE LISBOA

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INSTITUT
POLYTECHNIQUE
DE PARIS
# Outline

1. Motivation: why journalism?

2. Data integration problems raised by data journalism

3. Graph-based data integration in ConnectionLens

4. Related work and perspectives
Motivation:
Why journalism?
Bad memories: Romania, 1989

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Bad memories: Romania, 1989

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Ceauşescu re-elected
at the 14th congress!
Bad memories: Romania, 1989

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Ceauşescu re-elected
at the 14th congress!

He had held power
since 1965.
Bad memories: Romania, 1985

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1990: things got better

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... kind of

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1000 dead (approx.)
No one convicted.
# Democratic societies crucially need the press

☐ To debate and express dissent

☐ To analyze, confim or refute public statements

☐ To expose and explain society functioning

<figure><img src="image_12.png" /><figcaption>Socialist Romania, 1984</figcaption></figure>

Fact-checking

(Data) journalism

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Fact-checking

Not everyone agrees, however, that Democrats are not flip-flopping on the issue.

Mark Krikorian, executive director of the Center for Immigration Studies, a think tank that advocates for lower immigration, said that because the public doesn't know exactly what border barriers the Trump administration wants to build, Mulvaney's statement is not an "exact" comparison. But, he said, to dismiss it simply on that basis would be "tendentiously literal."

"The fact is that, other than the 'Mexico will pay for it' stuff, Trump is simply channeling the 2006 Secure Fence Act, and Schumer et al. who voted for it out of political calculation are indeed hypocrites for opposing the attempt to finally bring that law to fruition," Krikorian told us via email.

At the surface level, it is true in a broad sense that Democrats including Schumer, Obama and Clinton have in the past supported border fencing. All three voted for the Secure Fence Act of 2006, and all three supported the 2013 Senate immigration overhaul that passed the Senate, and which called for tougher border security including some additional fencing. But to claim that those measures are the same as what Trump is proposing is a stretch.

Share The Facts

<figure><img src="image_14.png" /><figcaption>Mick Mulvaney</figcaption></figure>

Director, Office of Management and Budget

MISLEADING

"We don't understand why the Democrats are so wholeheartedly against [President Trump's border wall]. They voted for it in 2006."

Fox News Sunday – Sunday, April 23, 2017

SHARE

READ MORE

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FACTCHECK.ORG

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# Data journalism

## Panama Papers (International Consortium of Investigative Journalism,ICIJ)

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Projects and collaborations

Google Award (2015) with U. Paris Sud

ANR ContentCheck (2016-2020) with
Les Décodeurs (Le Monde) https://contentcheck.inria.fr

Inria Associated Team WebClaimExplain (2017-2019),
with AIST Japan

Collaboration with H. Galhardas (University of Lisbon),
A. Anadiotis, O. Balalau, E. Pietriga (Inria, Polytechnique)

ANR SourcesSay AI Chair (2020-2023),
with Le Monde and WeDoData https://sourcessay.inria.fr

Fact-checking for RadioFrance (2022-2023)

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Le Monde

LES DÉCODEURS

VENONS-EN AUX FAITS

Inria

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AIST

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TÉCNICO
LISBOA

radiofrance

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# Project discussed with Les Décodeurs: fake new detection and propagation on Twitter

**Online fact-checks:** (semi)structured data sources (JSON, XML) listing

* Link to claim (media, social network etc.), **claim author**

* **Fact-check**, containing: analysis (details), final assessment, fc author, date, institution

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Among the first published: https://www.lemonde.fr/webservice/decodex/updates

Years later: **ClaimReview** by Google and others (https://www.claimreviewproject.com/)
# Project discussed with Les Décodeurs: fake new detection and propagation on Twitter

**Online fact-checks:** (semi)structured data sources (JSON, XML) listing

* Link to claim (media, social network etc.), **claim author**

* **Fact-check**, containing: analysis (details), final assessment, fc author, date

**Décodeurs' database of French public figures (Excel)**

* First name, last name, Twitter ID, position, political party when known

**Question:** When does a fake news post first cross into a community, e.g., members of the Parliament?

* Looking for tweets connected to a fake news author, and to a community member; arbitrary paths (chains of author/likes/retweets/inParty/...)

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Integrating heterogeneous
journalistic datasets
with ConnectionLens

https://team.inria.fr/cedar/connectionlens/
Graph-based integration of
heterogeneous data sources

☐ The sources are **not RDF**. They can be
**(semi)structured**, or **unstructured** (text).

☐ The sources may be very **dynamic** (projects started and abandoned as per news cycle and data availability).

☐ There is no schema. Data producers often uncollaborative.

☐ For most journalists, databases do not come naturally, and IT support is limited. They know keyword-based search...

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Integrate heterogeneous sources within a graph, query w/ keywords

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neo4j
# ConnectionLens: graph-based integration of heterogeneous data sources

https://team.inria.fr/cedar/connectionlens/

**Joint work with:** J. Leblay (AIST Japan),
H. Galhardas and C. Conceição (U. Portugal),
A. Anadiotis, O. Balalau, N. Barret, T. Bouganim,
F. Chimienti, M.-Y. Haddad, T. Merabti,
P. Upadhyay (CEDAR) + interns

S.Horel (Le Monde, European Press Prize
"Investigative Reporting Award 2018")

Ongoing work in ANR/DGA AI Chair SourcesSay
(https://sourcessay.inria.fr), DIM RFSI

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ConnectionLens principles [VLDBdemo2018]

Integrate **any kind of data** into a **graph**

**Extract entities** from any text node (regardless of the model of the data source where the text comes from)

☐ Same entity in two different text nodes =
link among the text nodes (*densification* of the graph)

The graph is **heterogeneous** and **irregular** →

Query it through **keywords**: find trees that connect 1 node matching each kwd

☐ Closely related to the Group Steiner Tree Problem (GSTP)
ConnectionLens principles [VLDBdemo2018]

Integrate **any kind of data** into a **graph**

**Extract entities** from any text node (regardless of the model of the data source where the text comes from)

☐ Same entity in two different text nodes =
link among the text nodes (*densification* of the graph)

The graph is **heterogeneous** and **irregular** →

Query it through **keywords**: find trees that connect 1 node matching each kwd

☐ Closely related to the Group Steiner Tree Problem (GSTP)

Rest of the talk based on current state of the project:
[CIKMdemo2021, Elsevier InfSys 2022, IEEE DataEngBull 2021, ongoing works]
ConnectionLens graph
construction
# The Balkany and their African connections

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# The Balkany and their African connections

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ENQUETE

## Troisième villa saisie pour les Balkany

Par Emmanuel Fansten — 13 août 2015 à 14:58

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https://www.lepoint.fr/politique/villas-a-marrakech-fonds-occultes-les-epoux-balkany-juges-lundi-12-05-2019-2312

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Actualité > Politique

### Villas à Marrakech, fonds « occultes »... : les époux Balkany jugés lundi

Soupçonnés d'avoir dissimulé 13 millions d'euros d'avoirs au fisc, les édiles de Levallois-Perret comparaissent pour fraude fiscale et blanchiment.

Source AFP

Publié le 12/05/2019 à 11:19 | Le Point.fr

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PROFITEZ DE VOTRE ABONNEMENT À LE LE IER MOIS !

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De somptueuses villas à Marrakech et dans les Caraïbes, des fonds « occultes » transitant par le Panama ou Singapour... Soupçonnés d'avoir dissimulé plus de 13 millions d'euros d'avoirs au fisc, les édiles de Levallois-Perret Patrick et Isabelle Balkany sont jugés à partir de

## 3 RÉSULTATS CORRESPONDANT À VOTRE RECHERCHE

### RESPONSABLES PUBLICS

### REPRESENTANTS D'INTÉRÊTS

### ACTIVITÉS DE REPRÉSENTATION D'INTÉRÊTS

Perte de l'autorité morale : demande d'Anticor au Président de la République pour que soient révoqués un maire et son adjointe qui ont avoué avoir fraudé l'administration fiscale

ANTICOR

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# The Balkanys and their African connections

## Public officials transparency high authority (CSV)

<table><thead><tr><td>Name</td><td>Owner</td><td>Location</td><td>Type</td></tr></thead><tbody><tr><td>Dar Gyucy</td><td>P. Balkany</td><td>Marrakech</td><td>Real Estate</td></tr><tr><td>Moulin Cossy</td><td>I. Balkany</td><td>Giverny</td><td>Real Estate</td></tr></tbody></table>
# The Balkanys and their African connections

## Public officials transparency high authority (CSV)

<table><thead><tr><td>Name</td><td>Owner</td><td>Location</td><td>Type</td></tr></thead><tbody><tr><td>Dar Gyucy</td><td>P. Balkany</td><td>Marrakech</td><td>Real Estate</td></tr><tr><td>Moulin Cossy</td><td>I. Balkany</td><td>Giverny</td><td>Real Estate</td></tr></tbody></table>

## National Directory of Elected Officials (JSON)

```json
[
    {
        name: "Levallois-Perret",
        mayor: "P. Balkany",
        city-council: [
            {
                name: "I. Balkany"
            }
        ],
        ...
    },
    ...
]
```
# The Balkanys and their African connections

## Public officials transparency high authority (CSV)

<table><thead><tr><td>Name</td><td>Owner</td><td>Location</td><td>Type</td></tr></thead><tbody><tr><td>Dar Gyucy</td><td>P. Balkany</td><td>Marrakech</td><td>Real Estate</td></tr><tr><td>Moulin Cossy</td><td>I. Balkany</td><td>Giverny</td><td>Real Estate</td></tr></tbody></table>

dbpedia.org (RDF)

```json
{
  dbr:Marrakech
dbr:name "Marrakech"
rdf:type dbo:City ;
dbo:country dbr:Morrocco .

dbr:Morocco
dbr:name "Morocco"
rdf:type dbo:Country
dbo:locatedIn dbr:Africa .

dbr:CentralAfricanRepublic
dbr:name "Central African Republic"
dbo:locatedIn dbr:Africa .
}
```

## National Directory of Elected Officials (JSON)

```json
[
  {
    name: "Levallois-Perret",
    mayor: "P. Balkany",
    city-council: [
      {name: "I. Balkany"},
      ...
    ]
  }, ...
]
```
# The Balkanys and their African connections

<table><caption>## Public officials transparency high authority (CSV)</caption><thead><tr><td>Name</td><td>Owner</td><td>Location</td><td>Type</td></tr></thead><tbody><tr><td>Dar Gyucy</td><td>P. Balkany</td><td>Marrakech</td><td>Real Estate</td></tr><tr><td>Moulin Cossy</td><td>I. Balkany</td><td>Giverny</td><td>Real Estate</td></tr></tbody></table>

dbpedia.org (RDF)

```json
{
  dbr:Marrakech
dbr:name "Marrakech"
rdf:type dbo:City ;
dbo:country dbr:Morocco .
}
```

```json
dbr:Morocco
dbr:name "Morocco"
rdf:type dbo:Country
dbo:locatedIn dbr:Africa .
```

```json
dbr:CentralAfricanRepublic
dbr:name "Central African Republic"
dbo:locatedIn dbr:Africa .
}
```

## National Directory of Elected Officials (JSON)

```json
[
  {
    name: "Levallois-Perret",
    mayor: "P. Balkany",
    city-council: [
      {name: "I. Balkany"},
      ...
    ]
  }, ...
]
```

### Libération – Nov. 13, 2014 (Text)

## Balkany mineur de fonds

L'élu de Levallois-Perret est soupçonné d'avoir touché 5 millions de dollars de commission en 2009 grâce à son rôle d'intermédiaire entre Areva et la Centrafrique dans le dossier Uramin. [...]
# How is Levallois-Perret connected to Africa and "real estate"?

## Public officials transparency high authority (CSV)

<table><thead><tr><td>Name</td><td>Owner</td><td>Location</td><td>Type</td></tr></thead><tbody><tr><td>Dar Gyucy</td><td>P. Balkany</td><td>Marrakech</td><td>Real Estate</td></tr><tr><td>Moulin Cossy</td><td>I. Balkany</td><td>Giverny</td><td>Real Estate</td></tr></tbody></table>

dbpedia.org (RDF)

```json
{
  dbr:Marrakech
dbr:name "Marrakech"
rdf:type dbo:City ;
dbo:country dbr:Morocco .
dbr:Morocco
dbr:name "Morocco"
rdf:type dbo:Country
dbo:locatedIn dbr:Africa .
dbr:CentralAfricanRepublic
dbr:name "Central African Republic"
dbo:locatedIn dbr:Africa .
}
```

## National Directory of Elected Officials (JSON)

```json
[
  {
    name: "Levallois-Perret",
    mayor: "P. Balkany",
    city-council: [
      {name: "I. Balkany"},
      ...
    ]
  }, ...
]
```

### Libération – Nov. 13, 2014 (Text)

## Balkany mineur de fonds

L'élu de Levallois-Perret est soupçonné d'avoir touché 5 millions de dollars de commission en 2009 grâce à son rôle d'intermédiaire entre Areva et la Centrafrique dans le dossier Uramin. [...]
Idea: integrate all data sources into a heterogeneous graph

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# Graph construction stages

## 1. Primary node and edge construction

- Direct for XML, JSON, RDF, HTML

- 1 relational tuple=1 node;
  primary keys-foreign keys as links

- Convert information from PDF into:

- JSON for text content

- RDF describing tables

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# Graph construction stages

## 1. Primary node and edge construction

- Direct for XML, JSON, RDF, HTML

- 1 relational tuple=1 node; PK-FKs as links

- [Optional] segment text documents

- Extract information from PDF into: (a) JSON, and (b) RDF describing tables

## 2. Entity extraction

- From all text nodes of all the sources: **entity node** child of text node

- [VLDB2018]: based on Stanford NER

- [InfSys2021] Developed and trained new entity extractor FR/EN, based on Flair framework

- Date extraction based on HeidelTime

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# Graph construction stages

## 2. Entity extraction

* From all text nodes of all the sources: **entity node** child of text node

* [VLDB2018]: based on Stanford NER

* [BDA2020] Developed and trained new entity extractor from French, based on Flair framework

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## 3. Entity disambiguation

* For each recognized entity, e.g., "Hollande" the place or the person?

* Built novel disambiguation pipeline for French, based on Ambiverse framework

* Based on knowledge bases (WikiData, YAGO) and Wikipedia

* Helpful on well-known entities

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# Graph construction stages

## 4. Node matching

- ☐ To create **sameAs edges**:

- ☐ Strong sameAs edges: equivalent nodes 1.

- ☐ Weak sameAs edges: similar nodes .85

- ☐ Label normalization, distance functions

- ☐ Remains quadratic at the core 😞, so...

**Node factorization (heuristic):** create only one node per label per document (or per graph)

More complex entity matching/cleaning pipelines for: Person, Organization, Location

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ConnectionLens graph
querying
Querying problem statement

- Given the graph G = (N, E) built out of the datasets D and a query Q= {w1, ...,wm},
  return the k highest-score minimal answer trees.

- An answer tree is a set of edges which (i) form a tree (ii) contain at least one node
  whose label matches each keyword wi.

- We are interested in **minimal answer trees**, that is:

- Removing an edge from the tree should make it lack some keyword(s).

- If a keyword matches more than one nodes in the answer tree, then all these
  matching nodes must be equivalent.
# Search space and complexity

☐ Problem related to the **(Group) Steiner Tree Problem**

☐ Given graph G, and nodes n1, ..., nm, the Steiner Tree Problem (STP) requires the smallest tree in G that connects all the nodes. Known NP-hard problem in |G|

☐ Group STP: start with m groups of nodes

☐ **Differences with our problem:**

☐ Each edge can be taken in **both directions**: exponential increase in search space size

☐ We need the **k smallest-cost trees**, not just one.

☐ Support arbitrary score functions

☐ **Large literature (30+) on kwd search in data graphs.**

☐ Differ in search space and/or make limitative assumptions on score

☐ **Our approach: enumerate solutions until time-out or max number of solutions reached.**

☐ Return best k solutions found, for given score function
# GAM (Grow and Aggressive Merge) Algorithm

* ☐ Builds trees exploring around the keyword matches

* ☐ **GROW** adds an edge to the root of a tree, **MERGE** merges trees with the same root
Sample search (tree roots underlined):

<figure><img src="image_60.png" /></figure>
# GAM (Grow and Aggressive Merge) Algorithm

- ☐ Builds trees exploring around the keyword matches

- ☐ **GROW** adds an edge to the root of a tree, **MERGE** merges trees with the same root

Every solution is a **conection (edge set)**: the root is not meaningful to users

Ideally, we should develop each edge set only once... But:

- ☐ Grow and Merge depend on the tree root → Many edge sets with the same root

- ☐ Also, there are many Grow-Merge combinations that produce the same rooted tree!

Optimizations and careful pruning to keep exploration highly efficient (ongoing)
Sample query answers

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ConnectionLens

Database: default

Import

About

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briand tonolli ruffin

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Search Q

Rank: #1 Size: 10 Score: 0.28 2 sources

<figure><img src="image_67.png" /></figure>

Rank: #2 Size: 9 Score: 0.28 1 source

Rank: #3 Size: 9 Score: 0.28 1 source

Rank: #4 Size: 9 Score: 0.28 1 source

Rank: #5 Size: 10 Score: 0.27 1 source

Rank: #6 Size: 10 Score: 0.26 1 source

Rank: #7 Size: 11 Score: 0.25 1 source

Rank: #8 Size: 10 Score: 0.23 1 source

<figure><img src="image_68.png" /></figure>
ConnectionLens architecture
and performance
# Implementation

- ☐ Java (230 classes/46K LOC), Python (24 classes/2800 LOC), JS + CSS

- ☐ Available online: https://gitlab.inria.fr/cedar connectionlens

<figure><img src="image_69.png" /></figure>
Implementation [InfSys2022]

☐ Graph creation time mostly **linear in the size of the data**

☐ Costliest operations involve ML (disambiguation, extraction)

☐ **Batch extraction**: 20x speed-up on GPU, 2x speed-up on regular server

☐ **Extraction policies** replace or avoid extraction in some parts of the data
Graph creation performance: storage, extraction, disambiguation [InfSys2022]

<figure><img src="image_70.png" /><figcaption>Figure 6: Graph construction time (seconds).</figcaption></figure>
Graph creation performance: batch extraction
[InfSys2022]

<figure><img src="image_71.png" /><figcaption>Figure 7: YAGO loading time (minutes) using Flair.</figcaption></figure>

<figure><img src="image_72.png" /></figure>
# Application: conflicts of interest in the biomedical domain [IEEE DataEngBull 2021, CIKM2021]

Collaboration with Stéphane Horel (Le Monde)

Data: XML, PDF→JSON, HTML

<table><caption>**Table 3: Statistics on Conflict of Interest application graph.**</caption><thead><tr><th>|N|</th><th>|E|</th><th>|N|</th><th>|N<sub>P</sub>|</th><th>|N<sub>O</sub>|</th><th>|N<sub>L</sub>|</th></tr></thead><tbody><tr><td>XML</td><td>32,028,429</td><td>19,851,904</td><td>1,483,631</td><td>584,734</td><td>126,629</td></tr><tr><td>JSON</td><td>1,025,307</td><td>432,303</td><td>75,297</td><td>7,320</td><td>4,139</td></tr><tr><td>HTML</td><td>246,636</td><td>185,479</td><td>3,726</td><td>7,227</td><td>320</td></tr><tr><td>Total</td><td>33,300,372</td><td>20,469,686</td><td>1,562,654</td><td>665,167</td><td>131,088</td></tr></tbody></table>
# Application: conflicts of interest in the biomedical domain [IEEE DataEngBull 2021, CIKM2021]

## Collaboration with Stéphane Horel (Le Monde)

<figure><img src="image_73.png" /></figure>
Graph quality experiments [InfSys2022]

☐ PDF extraction accuracy: 63%

☐ F1 score for entity extraction from French:

☐ Flair stacked forward and backward embeddings with French fastText embeddings: 73%

☐ Spacy: 63%

☐ StanfordNER: 45%

☐ F1 score of disambiguation: 86%
ConnectionLens in the
scientific landscape
# ConnectionLens in the scientific landscape (1)

**Data integration** for structured, semistructured and unstructured data

- "Ad-hoc" (combinations of sources to be unioned, joined, or chained)

- No schema, ontologies, queries known in advance

- Reachability queries instead of join: price to pay for powerful integration

- Comparable work by R. S. Roy, G. Weikum: GST querying of RDF graph enriched on-the-fly with content extracted from Web sources

- Web info may be missing or not trusted

**Data cleaning:** Similarity links require value or entity matching

- Use reference data sources, heuristics, interactive cleaning (under integrated)
# ConnectionLens in the scientific landscape (2)

## Graph construction

- ☐ Users of **entity extraction** modules, trained a model for French

## Keyword search on structured data

- ☐ Intensely studied for relational, graph, or XML databases: DISCOVER, Banks & Banks2, DPBF, ObjectRank, BLINKS, Spark, ... QGPT (WWW 2021)

- ☐ Some assume regularity in the graph and translate to SQL or SPARQL

- ☐ Consider more limited search spaces (edge direction; one tree per root; ...)

- ☐ Exploit favorable properties of fixed score function; establish approximations...

- ☐ We focus on efficient keyword search for arbitrary graphs (up to time-out) w/o assumptions on score, w/o sub-optimal structure prop., w/ bidirectional search

- ☐ In-memory graph store and parallel query processor (200x speed-up)
Ongoing work

☐ Learning to predict extraction success ~ detecting disguised nulls (BDA 2021, invited TLDKS 2022)

☐ Integrating keyword search into structured graph querying (with A. Anadiotis, M. Mohanty)

☐ Relationship extraction based on OpenIE (with O. Balalau, P. Upadhyay)

☐ Natural language graph querying (with O. Balalau, K. Zhang)

☐ Improving the quality of graph linking (with T. Bouganim, H. Galhardas)

☐ Abstracting CL graphs (with N. Barret, P. Upadhyay) https://team.inria.fr/cedar/projects/abstra/

☐ Applications:

☐ Conflicts of Interests in the biomedical domain (w/ S. Horel and G. Fooks, Aston U., UK)

☐ France's business x political sphere (w/ Radio France Investigation)
# Why data journalism?

Because I grew up in a dictatorship, and I value free press

Because journalists are threatened and killed still today in Europe

<figure><img src="image_74.png" /><figcaption>Daphne Galizia, 1964-2017</figcaption></figure>

<figure><img src="image_75.png" /><figcaption>Jan Kuciak, 1990-2018</figcaption></figure>

Because the press' economic model is threatened by IT giants

Because this industry is currently underserved by IT – and we could really make an impact!
Thank you

Questions?

ConnectionLens: https://team.inria.fr/cedar/connectionlens/