=Paper=
{{Paper
|id=Vol-1211/paper6
|storemode=property
|title=Offshore Holdings Analytics Using Datalog+ RuleML Rules
|pdfUrl=https://ceur-ws.org/Vol-1211/paper6.pdf
|volume=Vol-1211
|dblpUrl=https://dblp.org/rec/conf/ruleml/ManirB14
}}
==Offshore Holdings Analytics Using Datalog+ RuleML Rules==
https://ceur-ws.org/Vol-1211/paper6.pdf
Offshore Holdings Analytics Using Datalog+
RuleML Rules
Mohammad Sadnan Al Manir and Christopher J.O. Baker
Department of Computer Science and Applied Statistics
University of New Brunswick, Saint John, Canada
{sadnan.almanir,bakerc}[at]unb.ca
Abstract. In April 2013, the International Consortium of Investigative
Journalists (ICIJ) exposed the details of 130,000 offshore accounts. Al-
though there are legitimate businesses which use such accounts, there
exist a number of accounts which are possibly linked to international
tax fraud and money laundering. The ICIJ investigation was based on
2.5 million records of offshore holdings linked to 170 countries. All these
records have been made available for further examination and analysis.
Based on these records, a set of facts, rules and queries have been formu-
lated in Datalog+ RuleML 1.01/XML and these rules have been tested
and validated against the Relax NG schema for Datalog+ in RuleML
1.01/XML. The usefulness of such rules for offshore holdings analytics is
demonstrated here via incremental step-by-step approach, through the
discovery of relationships between persons hiding large sums of assets
and the offshore companies where the assets are purported to be hidden.
1 Introduction
The International Consortium of Investigative Journalists (ICIJ) [1] has exposed
the details of ownership of more than 100,000 offshore entities located in ‘tax
havens’ in 2013. The publicly released Offshore Leaks Database [2] shows rela-
tionships and networks among people or companies and offshore entities, often
hiding their true owners. The database contains data on the companies and
people identified as a director, a shareholder, a beneficiary, or a trustee. Pre-
cisely, ten offshore jurisdictions have been investigated: British Virgin Islands,
Cayman Islands, Cook Islands, Singapore, Hong Kong, Samoa, Seychelles, Mau-
ritius, Labuan, and Malaysia.
In this article, we demonstrate the effectiveness of expressive Datalog+ RuleML
1.01 [3] by showcasing a rulebase containing a set of rules and queries over a sub-
set of the Offshore Leaks Database. This rulebase will be used for analytics to
infer new knowledge by deriving relations among different entities which are not
easy to find manually. Our main motivation comes from the article published in
[4] on big data visualization based on the database. In our rulebase, the formu-
lation of rules have been simplified to the extent possible to ensure maximum
comprehensibility to a broader user base and unambiguous interpretation of de-
rived relations. Our method is introduced with an illustrative example, where
the goal is to find out if any connection can be established between the top-level
officials such as the president or the prime minister of a country and their possi-
ble offshore investments, which would usually be managed via immediate family
members or distant relatives and the intermediary companies they deal with.
Here, it is assumed that top-level officials are very careful about exposing their
holdings abroad and are not willing to be directly involved in a list of offshore
holdings owners. Hence, any enquiry regarding their direct involvements in own-
ing offshore holdings will almost certainly be in vein, and a search for indirect
involvement might be more fruitful.
The article is organized as follows: In section 2, the formalization of our
rulebase is illustrated in detail by authoring facts, rules, and executing queries
on them, often by checking the consistency of the rulebase. In section 3, the
expressivity and the effectiveness of rules used here have been briefly discussed.
Finally, we conclude by summarizing the tools and procedures used for rule
authoring, schema design, and their validation in Appendix A.
2 Rulebase
Our Datalog+ RuleML 1.01/XML Document incrementally asserts into, retracts
from, and queries, a rulebase (within the element) for a total of 37
transactions: 14 Asserts, 3 retracts, and 20 distinct Queries. The rules formalized
in this document not only cover the smallest Datalog sublanguage, but also cover
the highlights of RuleML 1.01, the superlanguage of the decidable Datalog+/- [5]
called Datalog+ [6] as well as Disjunctive Datalog features. The rules include all
three Datalog extensions which allow existentially quantified variables, “Equal”
predicate, and falsity in rule heads.
The fact “The president of Azerbaijan is Ilham” is formalized with a binary
president relation in RuleML. A query immediately follows, formalizing “Who
is the president of Azerbaijan?”, and resulting in an x binding to Ilham.
president
Ilham
Azerbaijan
president
x
Azerbaijan
A variation of the above query can be formalized as “Which country does
Ilham preside over?”, resulting in a y binding to Azerbaijan.
2
president
Ilham
y
A generic formalization of the above query can be expressed as “List all the
individuals and countries where the individual is the president of a country”.
Given the only ground fact, x and y represent the individual and the country
binding to Ilham and Azerbaijan, respectively.
president
x
y
The rule “No one is both the president and the prime minister of a country”
is formalized as an integrity rule with falsity in rule head.
x
president
x
y
primeMinister
x
y
The formalization of the fact “Ilham is the prime minister of Azerbaijan”,
when asserted with the previous rule, creates an inconsistency because of the
already existing assertion “Ilham is the president of Azerbaijan”.
primeMinister
Ilham
Azerbaijan
3
The inconsistency can be detected by the query “Is there any inconsistency?”.
It succeeds because, according to the rule with falsity, Ilham cannot hold both
the post of a president and a prime minister of Azerbaijan.
To remove inconsistency from the rulebase, the fact “Ilham is the prime
minister of Azerbaijan” is retracted.
primeMinister
Ilham
Azerbaijan
The consistency of the rulebase is checked again by the same query. This
time, the query fails and the rulebase regains consistency due to the retraction.
Universally quantified equations are useful for expressing more complex for-
mulas. The following rule “Two persons have family ties if they are either mar-
ried to each other, or there is a parent-child relationship between them, or they
are distant relatives” is formalized using the binary relations hasFamilyTies,
marriedTo, hasChild, and hasDistantRelative. The facts “Ilham is married
to Mehriban”, “Ilham has a daughter Arzu”, and “Ilham has a daughter Leyla”
are formalized using the relations.
x
y
marriedTo
x
y
hasChild
x
y
hasDistantRelative
x
y
4
hasFamilyTies
x
y
marriedTo
Ilham
Mehriban
hasChild
Ilham
Leyla
hasChild
Ilham
Arzu
The query, formalizing “List the individuals having family ties to Ilham?”,
results in a y binding to three individuals named Mehriban, Leyla, and Arzu.
hasFamilyTies
Ilham
y
On the other hand, the query “Which individuals are tied to Mehriban as
family members?” can be formalized to retrieve Ilham using the marriedTo
relation. Similarly, the binding of x to Ilham establishes family ties for both
Leyla and Arzu because of the hasChild relation.
hasFamilyTies
x
Mehriban
hasFamilyTies
x
Leyla
hasFamilyTies
x
Arzu
5
The query “Find all individuals having family ties to each other”, results
in multiple combinations of x and y where Ilham binds to an x and each of
Mehriban, Leyla, and Arzu binds to a y, respectively.
hasFamilyTies
x
y
The rule “Everything which operates either as an intermediary company or
as an offshore company is designated as a company” is formalized as a univer-
sally quantified rule with disjunctions in the rule body and it asserts a subclass
relationship. Three ground facts “ArborInvestmentsLtd operates as an interme-
diary company”, “NaziqAndPartners operates as an intermediary company”, and
“HarvardManagementLtd operates as an offshore company” are also formalized
and added below.
x
intermediaryCompany
x
offshoreCompany
x
company
x
intermediaryCompany
ArborInvestmentsLtd
intermediaryCompany
NaziqAndPartners
offshoreCompany
HarvardManagementLtd
The above rule implies that ArborInvestmentsLtd, NaziqAndPartners, and
HarvardManagementLtd are all designated as companies. The formalization of
6
the query “Which entities are designated as companies?” results in ArborInvest-
mentsLtd, NaziqAndPartners, and HarvardManagementLtd as companies bind-
ing to the variable x.
company
x
From the above assertions, we can also find out if a particular company
operates as an intermediary company or as an offshore company. For instance,
the query “Does ArborInvestmentsLtd operate as an intermediary company?”
succeeds as the company asserts as an intermediary company.
intermediaryCompany
ArborInvestmentsLtd
On the other hand, the query “Does ArborInvestmentsLtd operate as an off-
shore company?” fails as its operation does not assert as an offshore company
but as an intermediary company.
offshoreCompany
ArborInvestmentsLtd
Another integrity rule with falsity in rule head is formalized as “Nothing is
both an onshore company and an offshore company”.
x
onshoreCompany
x
offshoreCompany
x
The fact “HarvardManagementLtd is an onshore company” creates an incon-
sistency.
7
onshoreCompany
HarvardManagementLtd
Inconsistencies can be detected by the query “Is there any inconsistency?”.
It succeeds because, according to the rule above, HarvardManagementLtd
cannot be both an onshore company and an offshore company.
To remove inconsistency from the rulebase, the fact “HarvardManagementLtd
is an onshore company” is retracted.
onshoreCompany
HarvardManagementLtd
When consistency of the rulebase is checked by the same query, it fails as the
rulebase became consistent with the retraction.
The rule “It is unlikely that an offshore company reveals their clients’ invest-
ment information” is formalized as an example of a negative integrity constraint.
x
offshoreCompany
x
revealInvestmentInfo
x
The fact “HarvardManagementLtd reveals investment information” creates
inconsistency when added to the rulebase.
8
revealInvestmentInfo
HarvardManagementLtd
The query “Is there any inconsistency?” succeeds because HarvardManagementLtd
does not reveal investment information while operating as an offshore company.
The fact “HarvardManagementLtd reveals investment information” is re-
tracted to remove inconsistency from the rulebase.
revealInvestmentInfo
HarvardManagementLtd
Now that the consistency is checked again by the same query, it fails.
The following assertion contains a rule and a ground fact. The rule “Any-
one is a shareholder of a company if and only if there exists a certain level of
stocks of that company such as Small-cap level or Medium-cap level or Large-cap
level, which he owns” is formalized as an equivalence formula. The ground fact
“Arzu owns Medium-cap level of stocks in ArborInvestmentsLtd” is formalized
and added below.
x
y
shareHolderOf
x
y
z
ownsStockin-atLevel
x
y
z
ownsStockin-atLevel
Arzu
9
ArborInvestmentsLtd
Medium-cap
The benefit of this equivalence formula is that the ownsStockin-atLevel
relation can be used when the level of stock ownership is important, otherwise
shareHolder relation is sufficient as it does not care about the level of stocks
to become a shareholder.
The ownsStockin-atLevel relation is defined as a functional relation, hence
together with its existential formalization in the equivalence formula above, it
must have uniqueness. This is achieved by formalizing the rule “Every stock
owner owns at most one level of stocks of a company” with equality in the head.
x
y
z1
z2
ownsStockin-atLevel
x
y
z1
ownsStockin-atLevel
x
y
z2
z1
z2
The query “Is there any individual who owns Small-cap level of stocks in
ArborInvestmentsLtd?” fails because there are no individuals binding to an x
who own Small-cap level of stocks. A similar query with a different quantity
Large-cap in place of Small-cap also fails.
ownsStockin-atLevel
x
ArborInvestmentsLtd
Small-cap
10
On the other hand, the query “Is there any individual who owns Medium-cap
level of stocks in ArborInvestmentsLtd?” succeeds by providing the answer Arzu
as the owner binding to an x.
ownsStockin-atLevel
x
ArborInvestmentsLtd
Medium-cap
The query “List all shareholders of ArborInvestmentsLtd” provides the indi-
vidual Arzu as the only shareholder binding to an x.
shareHolderOf
x
ArborInvestmentsLtd
The rule “There is a link between a person and a company if the person is
a director or a shareholder of the company or he owns a level of stock in the
company” is formalized as a universally quantified rule with disjunctions in rule
body.
x
y
directorOf
x
y
shareHolderOf
x
y
z
ownsStockin-atLevel
x
y
z
hasLinksTo
x
y
11
hasLinksTo
x
y
The query “Which individuals and companies are linked to each other?” re-
sults in the name Arzu binding to the variable x and the company ArborInvestmentsLtd
to a y.
The rule “If a company C1 manages assets of another company C2 and the
latter i.e. C2 manages assets of yet another company C3 , then company C1 man-
ages the assets of the company C3 ” is formalized as a universally quantified
rule implying a transitive relation. With this rule, relations between two distant
companies can be established even if there is a chain of companies sitting in
the middle managing assets. The facts “ArborInvestmentsLtd manages assets of
NaziqAndPartners” and “NaziqAndPartners manages assets of HarvardManage-
mentLtd” are formalized using the relation manageAssets and are added at the
end of the assertion.
x
y
z
manageAssets
x
y
manageAssets
y
z
manageAssets
x
z
manageAssets
ArborInvestmentsLtd
NaziqAndPartners
manageAssets
NaziqAndPartners
HarvardManagementLtd
12
From the transitive rule and two facts mentioned above, it can be deduced
that the company ArborInvestmentsLtd is, in fact, managing assets of the
distant company HarvardManagementLtd. The query “Find all the companies
whose assets are being managed and the companies managing them” can be used
to infer such relations among companies.
manageAssets
x
z
Finally, by formalizing the rule “The president of a country (possibly) has
offshore investments in a company if his family members have links to companies
managing assets in that offshore company”, it can be discovered if a president has
invested in an offshore company to hide his assets using someone in his family.
p
c
fm
ic
oc
president
p
c
hasFamilyTies
p
fm
hasLinksTo
fm
ic
manageAssets
ic
oc
possiblyHasOffshoreInvestmentIn
p
oc
13
Based on the facts and rules provided, the query “Find the presidents and
companies in which the presidents possibly have offshore investments” is formal-
ized to find the presidents who have invested in offshore companies to hide their
assets from the concerned national and international authority.
possiblyHasOffshoreInvestments
p
oc
The answer to the above query is president Ilham of Azerbaijan whose
daughter Arzu has links with the company ArborInvestmentsLtd, which indi-
rectly manages the assets in the offshore company called HarvardManagementLtd.
3 Conclusion
A rulebase formulated using Datalog+ RuleML 1.01/XML rules is presented in
this article. The incremental formalization of rules demonstrates how interesting
relationships can be discovered among different entities from financial records,
which are difficult to deduce in plain sight. The discovery of relationships is used
for offshore holdings analytics.
Our rulebase is comprised of expressive RuleML 1.01 rules which, in addi-
tion to the existing RuleML sublanguages, cover two newly introduced anchor
sublanguages called datalogplus min and disdatalogplus min. Thus our rules not
only include positive facts and universally quantified implications, but also al-
lows existential variables, “Equal” predicates, and falsity in the heads of impli-
cations. Moreover, conjunctions within existetials and non-empty disjunctions,
arbitrarily nested with existentials and conjunctions are permitted in the heads
of implications. Some of the rules in our rulebase have disjunctions both in the
body and head of implications.
With the addition of new features from the two sublanguages, expressive
rules are now easier to formulate in RuleML 1.01. The ability to use existential
variables in the heads of implications lets us make some progress in solving old
problems in semantic technologies. For instance, an earlier attempt mentioned in
[7] for combining negation-free rules and ontologies needed a workaround called
DL-safety because those rules did not allow existentials in heads. As RuleML
1.01 permits existentials in heads of implications, our intuition is that DL-safety
won’t be required if RuleML 1.01 rules are used instead of negation-free rules.
Further research in this direction would be interesting and is left for future work.
Moreover, because of the modularity in RuleML sublanguages, fine-grained rules
can be authored, which might fall into a customized sublanguage. The availability
of these choices gives the user the advantage of identifying appropriate resources
for the sublanguage such as the inference engine saving significant time.
14
A Rulebase Authoring, Schema Design, and Validation
For authoring our rulebase1 , we used XML Copy Editor2 which is a free software
released under the GNU General Public License. The RuleML community has
provided Modular sYNtax confiGurator (MYNG 1.01)3 , a highly customizable
GUI for generating RELAX NG Compact Syntax (RNC) schemas or approxi-
mating XSD schemas. We formalized the rulebase based on RNC schemas and
validated using a validation webservice4 . The webservice can validate Datalog+
RuleML 1.01/XML instances against schemas, including Relax NG schemas and
Namespace-Based Validation Dispatching Language NVDL5 . We validated ev-
ery assert and query separately using the webservice against the appropriate
RNC schema and retracted any assertions causing inconsistency in the rulebase.
Finally, the complete rulebase containing all asserts, retracts, and queries is val-
idated against the RNC schema.
References
1. The International Consortium of Investigative Journalists. http://www.icij.org/.
2. ICIJ Offshore Leaks Database. http://offshoreleaks.icij.org/about/caveats.
3. Datalog+ RuleML 1.01. http://deliberation.ruleml.org/1.01.
4. Analysing the Offshore Leaks with graphs. http://linkurio.us/
analysing-the-offshore-leaks-with-graphs/.
5. Andrea Calı̀, Georg Gottlob, and Thomas Lukasiewicz. A general datalog-
based framework for tractable query answering over ontologies. In Proceedings of
the Twenty-eighth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of
Database Systems, PODS ’09, pages 77–86, New York, NY, USA, 2009. ACM.
6. Georg Gottlob, Giorgio Orsi, Andreas Pieris, and Mantas Simkus. Datalog and its
extensions for semantic web databases. In Thomas Eiter and Thomas Krennwallner,
editors, Reasoning Web, volume 7487 of Lecture Notes in Computer Science, pages
54–77. Springer, 2012.
7. Mohammad Sadnan Al Manir. Towards rif-owl combination: An effective reason-
ing technique in integrating owl and negation-free rules. In Frank Olken, Monica
Palmirani, and Davide Sottara, editors, RuleML America, volume 7018 of Lecture
Notes in Computer Science, pages 33–48. Springer, 2011.
1
http://deliberation.ruleml.org/1.01/exa/RulebaseCompetition2014/OffshoreHoldingAnalytics.ruleml
2
http://xml-copy-editor.sourceforge.net/
3
http://deliberation.ruleml.org/1.01/myng/
4
http://validator.nu
5
http://nvdl.org
15