=Paper=
{{Paper
|id=Vol-293/paper-6
|storemode=property
|title=Semantic Enterprise Technologies
|pdfUrl=https://ceur-ws.org/Vol-293/paper6.pdf
|volume=Vol-293
|authors=Massimo Ruffolo,Luigi Guadagno,and Inderbir Sidhu,pages 70-84
|dblpUrl=https://dblp.org/rec/conf/semweb/RuffoloGS07
}}
==Semantic Enterprise Technologies==
https://ceur-ws.org/Vol-293/paper6.pdf
FIRST - First Industrial Results of Semantic Technologies
Semantic Enterprise Technologies
Massimo Ruffolo1,2 , Inderbir Sidhu2 , Luigi Guadagno2
1
ICAR-CNR - Institute of High Performance Computing and Networking
of the Italian National Research Council
2
fourthcodex inc.
University of Calabria, 87036 Arcavacata di Rende (CS), Italy
e-mail: ruffolo@icar.cnr.it
e-mail: {lguadagno,isidhu}@fourthcodex.com
WWW home page: http://www.fourthcodex.com
Abstract. Nowadays enterprises request information technologies that lever-
age structured and unstructured information for providing a single integrated
view of business problems in order to foster better business process manage-
ment and decision making. The growing interest in semantic technologies is
due to the limitation of existing enterprise information technologies to answer
these new challenging needs. Semantic Web Technologies (SWT), the current
open standard approaches to semantic technologies based on semantic web
languages, provide some interesting answers to novel enterprise needs by al-
lowing to use domain knowledge within applications. However, SWT aren’t
well suited for enterprise domain because of some drawbacks and a lack of
compatibility with enterprise-class applications. This paper presents the new
Semantic Enterprise Technologies (SET) paradigm founded on the idea of Se-
mantic Models that are executable, flexible and agile representation of domain
knowledge. Semantic Models are expressed by means of the Codex Language
obtained combining Disjunctive Logic Programming (Datalog plus disjunc-
tion) and Attribute Grammars both extended by object-oriented and two-
dimensional capabilities. Semantic Models enable to exploit domain knowledge
for managing both structured and unstructured information. Since the Codex
Language derives from the database field, it allows SET to provide advanced
semantic capabilities well suited for enterprises. Differences and interoperabil-
ity issue between SET and SWT are briefly discussed in the paper that shows,
also the SET Reference Architecture (SETA), an application example and the
business value of SET.
1 Introduction
Nowadays enterprise knowledge workers need information technologies that lever-
age structured and unstructured information for providing a single integrated view of
business problems in order to foster better business process management and decision
making. They want answers to specific business requirements, not documents and
reports, search document base by concepts not simply by keywords, query databases
and unstructured information repositories in a uniform way taking into account the
meaning of data and information, obtain application integration and interoperability
exploiting semantic-aware services.
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FIRST - First Industrial Results of Semantic Technologies
The growing area of Semantic Technologies [2] could answer these novel needs
by providing a new enterprise-class of semantically-enabled business applications. In
this scenario semantics means the use of domain knowledge to affect computing by
allowing the meaning of associations among information to be known and processed
at execution time. Semantic Technologies must be able to:
– Allow computable semantic representations of domain knowledge, and provide
reasoning capabilities on it, in order to enable software to do useful tricks such as
finding hidden relationships in a complicated web of objects.
– Handle very large-scale knowledge bases containing both structured and unstruc-
tured information.
– Automate the capturing of events of events and entities to connect people, places,
and events using information in different formats coming from many different
sources.
– Assist human monitoring and analysis of situations, workflows, collaboration and
communication.
– Facilitate interoperability by exploiting enterprise concepts to link applications,
data sources, and services in easily to use composite views, providing real-time
interaction, analysis, and decision-support.
– Deliver their capabilities as value added ingredient components that are easy to
embed with existing enterprise applications and integration architectures.
These challenges exceed the capabilities and performance capacity of current open
standards approaches to semantic technologies based on semantic web languages (i.e.
OWL and RDF) [16]. Such approaches have the benefit to create interoperability
over the web but they suffer of the following important drawbacks when applied to
enterprise domains: (i) they have a lack of compatibility with relational databases,
the most widely adopted enterprise information technologies for representing, storing
and managing enterprise structured data; (ii) they allow for ”connecting the onto-
logical dots” by using predefined ontological-data model that assists to discover and
infer new knowledge (when proper reasoners are available). But ’real world’ enterprise
business practices are not neatly predefined and in fact are more likely to be dynamic,
emergent, or even chaotic. In other words, fully articulated knowledge models (ontolo-
gies) are not necessary for recognizing relevant facts in the ever-growing knowledge
bases, or inferring new useful related information, or ensuring that enough knowl-
edge is available just in time to improve decision-making; (iii) they do not propose
mechanisms for handling directly the already available huge amount of unstructured
information.
This paper describes the novel Semantic Enterprise Technologies (SET) paradigm
founded on a the idea of Semantic Models (SM) that are executable, flexible and agile
representation of domain knowledge (e.g. simple taxonomies equipped with few and
simple descriptors, very rich ontologies equipped with complex business rules and
descriptors). SM are represented by means of a new language, called Codex Language
obtained by combining Disjunctive Logic Programming (Datalog plus disjunction)
[4,6,9] and attributes grammars both extended by means of object-oriented [3,12] and
two-dimensional capabilities [13,14,15]. SM enable to exploit domain knowledge for
managing both structured (e.g. relational databases) and unstructured information
(e.g. document repositories).
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FIRST - First Industrial Results of Semantic Technologies
SET overcome the above mentioned SWT limitations by allowing the following
fundamental set of features: (i) they basically came from the database world. So they
propose query language and reasoning approaches well suited for the relation model.
The Codex language in fact, are based on the Closed World Assumption (CWA) and
the Unique Name Assumption (UNA) whereas semantic web languages are based on
Open World Assumption (OWA) and do not consider UNA. In order to make SET
interoperable with SWT a translation approach that takes into account the different
semantics of the Codex Language and OWL has been defined. This way SET can
be considered complementary to SWT, (ii) the Codex Language allows to represent
Semantic Models that can be composed of ”just enough” taxonomies and rules or of, if
required, complex ontologies containing also relationships, constraints, axioms, so the
knowledge representation process fits the very dynamic enterprise environments; (iii)
they provide powerful unstructured information management mechanisms that allow
concepts annotation and extraction from unstructured sources (semantic enterprise
metadata acquisition) via a pattern-based approach. So precise semantic information
extraction, classification and search, enabling semantic indexing and querying of the
enterprise knowledge, are also possible.
In order to make effective technological and business advantages of SET, as already
happens for existing enterprise-class information technologies, a reference architecture
that constitutes the framework for applying SET features to enterprise domains is
required. This paper proposes SETA (the SET reference Architecture) that describes
the technologies and architecture enabling the use of Semantics in an enterprise.
SETA aims at transforming multiple sources (structured and unstructured) and bits
of dynamic information with domain and concept coverage from disparate enterprise
systems into useful knowledge that fosters better enterprise performances.
SET have already been applied to contact-center software, CRM applications,
asset and content management repositories, news and media delivery services, health
care organizations and more. Their distinctive features help to shape the future of
new knowledge-powered computing solutions in many different traditional areas like
Competitive Intelligence, Document and Content Management, CRM, Text Analytics,
Information Extraction. The application of SETs to real cases shows that they can
improve value creation capabilities of enterprises allowing the definition and execution
of more efficient and effective business processes and decision making.
The remainder of this paper is organized has follows. Section 2 presents the struc-
ture of Semantic Models and describes the Codex Language, Section 3 provides a
comparison between Codex Language and OWL and drafts the interoperability ap-
proach, Section 4 describes the SET reference Architecture, Section 5 sketches an
application of SET to health care risk management, Section 6 contains a brief de-
scription of the business value of SET and Section 7 concludes the paper.
2 Semantic Models
SET are based on the concept of Semantic Model. A SM is a flexible and agile
representation of domain knowledge. Semantic Models can be constituted by either
just small pieces of a domain knowledge (e.g. small taxonomies equipped with few
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FIRST - First Industrial Results of Semantic Technologies
rules) or rich and complex ontologies (obtained, for example, by translating existing
ontologies and by adding rules and descriptors) that gives respectively weak or rich
and detailed representation of a domain. More formally a SM is a seven-tuple of the
form:
SM = hHC , HR , O, T, A, M, Di
where:
– HC and HR are sets of classes and relations schemas. Each schema is constituted
by a set of attributes, the type of each attribute is a class. In both HC and HR
are defined partial orders allowing the representation of concepts and relation
taxonomies (with multiple inheritance).
– O and T are sets of class and relation instances also called objects and tuples.
– A is a set of axioms represented by special rules expressing constraints (rules
always true) about the represented knowledge.
– M is a set of reasoning modules that are logic programs constituted by a set of
(disjunctive) rules that allows to reason about the represented and stored knowl-
edge, so new knowledge not explicitly declared can be inferred.
– D is a set of descriptors (i.e. production rules in an two-dimensional object-
oriented attribute grammar) enabling the recognition (within unstructured docu-
ments) of class (concept) instances contained in C, so their annotation, extraction
and storing is possible.
SM are represented by means of the novel powerful and very expressive Codex Lan-
guage described in the following.
2.1 The Codex Language
The Codex Language brings together the expressiveness of ontology languages
and the power of Disjunctive logic rules. The Codex Language combines notions com-
ing from Disjunctive Logic Programming (Datalog plus disjunction) and Attribute
Grammars both extended by means of object-oriented and two-dimensional capabili-
ties. The attribute grammars allow one to intuitively express patterns for recognizing
instances of the ontology concepts in structured and unstructured data. The language
has been defined as such in recognition of the fact that in order to leverage and apply
Semantic Models in the enterprise, it is important to find and retrieve appropriate
data from all kinds of data sources, such as, schema based structured databases, un-
structured documents and semi-structured documents containing implicit structure.
In order to understand the motivation for extending the capabilities of the Codex
Language beyond the modeling capabilities of most ontology languages to a language
that also makes it possible to describe how to recognize instances of the ontology
concepts in data, consider the following example. Imagine a financial analyst tasked
with researching some corporation, say, Microsoft. An ontology could describe that
a company may be owned by individuals or boards, and the knowledge base could
contain the facts that Microsoft is a company, and it is owned by Bill Gates. Besides
representing all of the above information, the codex language can also express the
rules and patterns for identifying instances of Microsoft Corp. in documents. One
possibility is for a document to mention Microsoft as “the company owned by Bill
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FIRST - First Industrial Results of Semantic Technologies
Gates”. The rules for recognizing the concept of company ownership can easily be
expressed in the codex language allowing for the identification of the instances of
companies owned by Bill Gates or anyone else in structured and unstructured data
sets. Hence, it is now possible to determine that when a document mentions “the
company owned by Bill Gates”, we have really discovered a document talking about
Microsoft Corp.
The Codex language supports the typical ontology constructs, such as, class, object
(class instance), object-identity, (multiple) inheritence and relations, tuple (relation
instance). It also supports powerful reasoning by means of reasoning modules that
are modular logic programs containing a set of (disjunctive) rules. The language aug-
ments these typical ontology modeling constructs with a mechanism that enables the
description of patterns and rules over an ontology for identifying meaningful data in
any data source. This part of the codex language extends classical attribute grammars
by means of two-dimensional and object-oriented capabilities allowing the expression
of concept descriptors. A descriptor represents a rule that “describes” the means for
recognizing instances (objects) of a concept in unstructured documents by means of
complex (two-dimensional) composition of other objects or in structured sources (e.g.
databases, structured files) by means of ad-hoc queries and reasoning tasks. When a
descriptor matches within an unstructured document, the document can be annotated
with respect to the related concept, and moreover, an instance of the matching class
can be created in the knowledge base. In order to empower unstructured information
management, the Codex language can also exploit sophisticated Natural Language
Processing capabilities.
In the Codex Language a class can be thought of as an aggregation of individuals
(objects or class instances) that have the same set of properties (attributes). A class
is defined by a name (which is unique) and an ordered list of attributes identifying
the properties of its instances. Each attribute is identified by a name and has a type
specified as a built-in or user-defined class. For instance, the classes country and
person can be declared as follows:
class country (name:string).
class person (name:string, age:integer, nationality:country).
The ability to specify user-defined classes as attribute types (nationality:country)
allows for the description of complex objects, i.e. objects made of other objects recur-
sively (a person could be a parent that is also a person). The language also supports
the definition of special classes called collection classes that ”collect” individuals that
belong together because they share some properties. Instances of these classes can
either be declared explicitly as in the case of normal classes, or specified by a rule
that defines the shared properties in an intensional way.
Objects (class instances) are declared by asserting new facts. Objects are unam-
biguously identified by their object-identifier (oid ) and belong to a class. An instance
for the class manager can be declared as follows:
bill:manager("Bill", 35, usa, 50000).
mario:manager("Mario", 37, italy, 40000).
Here, the strings ”Bill” and ”Mario” are the values for the attribute name; while
bill and mario are the object-identifier[s] (oid) of these instances (each instance
is identified by a unique oid). Instance arguments can be specified either by object
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identifiers (usa and italy), or by a nested class predicate (complex term) which works
like a function.
Relationships among objects are represented by means of relations, which, like
classes, are defined by a unique name and an ordered list of attributes (with name and
type). Relation instances (tuples) are specified by asserting a set of facts. For instance,
the relation managed by, and a tuple asserting that project newgen is managed by
bill (note that newgen and bill are OID), can be declared as follows:
relation managed_by(proj:project, man:manager).
managed_by(newgen, bill).
The Codex language makes it possible to specify complex rules and constraints over
the ontology constructs, merging, in a simple and natural way, the declarative style
of logic programming with the navigational style of ontologies. Additionally, the rules
and constraints are organized as reasoning modules, benefiting from the advantages of
modular programming. Eventually, in order to check the consistency of a knowledge
base the user can specify global integrity constraints called axioms. For example, the
following axiom expresses that each project can have only one manager:
::- managed_by(proj:A, man:M1), managed_by(proj:A, man:M2), M1 <> M2.
A descriptor can be viewed as an object-oriented production rule p ∈ Π in an
two-dimensional attribute grammar defined on a formal context free grammar G =
hΣ, VN , A ∈ VN , Πi over the alphabet Σ. In the Codex Language the domain of the
attributes is the set of classes declared in the Semantic Model whereas the alphabet
Σ is constituted by class names and object identifiers. More formally, a descriptor d
is a couple hh, bi such that h → b, where h is the head of d and b is its body. The
following example declares the company class, along with an instance and a descriptor
for this instance:
class company (name:string, nationality:country, market:market_area).
acme: company("Acme Inc.", usa, rocket_skiis).
-> .
The descriptor head represents the object (or the class of objects) that the user
desires to recognize within unstructured documents. The descriptor body represents
the rule “describing” the structure of the objects in the head in terms of regular
expressions (or other objects).
As another example, consider the following fragment of the Codex Language rep-
resenting the extraction of a table containing stock index:
collection class italian_stock_market_index_row(
stockMarketIndex: stock_market_index, [value]){}
->
{L:=add(L,V)}+.
collection class italian_stock_market_index_table
([italian_stock_market_index_row]){}
-> {L:=add(L,X)}+ DIRECTION = "vertical".
In the example, the collection class italian stock market index row represents ta-
ble rows composed of the stock market index and a sequence of numeric value[s];
the collection class italian stock market index table will contains the vertical se-
quence of rows that constitute the table.
The Codex language allows the expression of very complex patterns that utilize the
ability to treat any unstructured or semi-structured document as a two-dimensional
plane and exploit the full expressive power of semantic models. The above example
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FIRST - First Industrial Results of Semantic Technologies
in particular shows the ability to focus on complex and very specific information for
extraction from unstructured documents, which in this case happens to be a table of
stock indexes related to Italian companies only.
It is noteworthy that descriptor [s] can be expressed using a visual support and
that the instances of concepts matching the descriptors can be extracted and stored
in the Knowledge Base. These instances can also be serialized as XML, RDF, etc. to
be used for analytical and/or Web (Semantic Web) based applications.
3 The Semantic Enterprise and the Semantic Web
In order to motivate why Semantic Enterprise Technologies paradigm could rep-
resent an opportunity for improving current semantic technology capabilities, it is
important to compare and contrast the semantic enterprise approach against the se-
mantic web approach. For example, in the closed world of an enterprise, reasoning
using closed world and unique name assumptions offers certain advantages over the
open world assumption necessary when working with the Web. Another important
aspect for consideration is the need for performing semantic reasoning over enter-
prise data residing in relational databases. It is also important to be able to integrate
rules with ontologies in order to maximize the return from the ontology building ef-
fort. The benefits possible from the integration of logic programming and OWL have
already been described in [1,8,11]. In this section we provide a detailed description
of the advantages of the SET paradigm and its interoperability with semantic web
technologies.
3.1 Closed World and Unique Name Assumptions
Enterprise databases are founded on Unique Name Assumption (UNA) and Closed
World Assumption (CWA). The semantics of these databases are intuitive and familiar
to their users. The CWA is known only information explicitly stored in the Knowledge
Base, so a CWA-based rules entails false for information not explicitly declared in the
Knowledge Base. The UNA assumes that names of information elements stored in
the Knowledge Base are unique, so two elements with different names are necessarily
distinct. The codex language is based on closed world and unique name assumption.
This makes it highly suitable for modeling and reasoning in the enterprise. Conversely,
OWL is based on Open World Assumption which is better suited for the Semantic
Web.
A Semantic Model can be seen as an extension of the enterprise database. In
order to understand the effect the semantic assumptions of a database can have on
the behavior of a query over exactly the same data, consider the databases shown
in Fig. 1. When database (A) is queried for the company that has its headquar-
ters only in the USA, a CWA-based reasoner returns Intel, whereas an OWA-
based reasoner is unable to answer this query because there is no explicit state-
ment stating that Intel has its headquarters only in the US. Database (B) repre-
sents the relationship that for each project we can have at most one leader and
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that a project leader can lead many projects. The DL representation is the fol-
lowing TBox: PROJECT MANAGER hasLeader: PROJECT --> MANAGER and ABox: PROJECT(p1) PROJECT(p2)
MANAGER(John) MANAGER(Chris) hasLeader(p1,John) hasLeader(p2,Chris). In order to represent the
database constraint that a project may have at most one manager, we impose the
following DL constraint: > ≤ 1 hasLeader. In the case of a relational database, when
a user attempts to insert the fact that John is also a leader of the project p2, there
is a violation of referential integrity, and the action is not allowed by the database.
However, if we assert the same fact in a DL ABox as hasleader(p2,john), the system does
not complain about any possible violation. When the system is queried for the leader
of the project p2, the OWA-based reasoner infers that Chris and John are the project
leaders and that Chris=John because of the lack of UNA. In order to obtain the same
behavior as CWA in this case, the user must explicitly declare that Chris 6= John.
This is obviously counterintuitive for the normal enterprise user.
Fig. 1. Two simple relational databases
Negative Queries Negative queries are queries for data where the query condition
does not hold. For example, an airline database may contain all the pairs of airports
connected by its flights. The CEO of the airline might want to query for all the
airports not connected by its hub airport. An OWA based reasoner would have trouble
answering such a query, since the database does not, and for all practical purposes,
can not, contain information about every airport that is not serviced. In the absence of
asserted or explicitly derived negative information, the OWL based reasoner cannot
answer such query. On the other hand, answering such a query is trivial for CWA
based systems which correctly assume that if there doesn’t exist any record of a flight
between two airports, then it is save to assume that there are not flights between those
airports. Answering queries containing negative criteria in an intuitive way usually
requires some form of closed-world reasoning.
From the above discussions and examples it follows that CWA and UNA are
better suited for the enterprise domain because their semantics are more intuitive for
the users. Also, the reasoning based on such assumptions produces more useful new
information. So an approach to knowledge representation coming directly from the
database world is able to preserve the database semantics allowing the user to query
and reason on databases in a more natural way.
3.2 Rules and Integrity Constraints
Rules It is important to integrate rules with ontologies in order to fully capture the
knowledge of an enterprise or a domain. Enterprises need to realize a return on their
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investment in building ontologies, and a such, ontologies must not be perceived as
just a documentation technique for describing a domain, but also as a language and
system that can capture and execute domain rules expressed over the ontology. This
has also been recognized by the Semantic Web community which is actively working
on adding rules to the Semantic Web language stack [7]. The Rule Interchange For-
mat (RIF) working group of the World Wide Web Consortium (W3C) is currently
working on standardizing such a language. Responding to popular demand, the Se-
mantic Web Rule Language (SWRL) has been proposed. However, as the authors
point out, SWRL has been designed as a first-order language and straightforwardly
integrated with OWL as a simple extension. For these reasons SWRL is trivially
undecidable and does not address nonmonotonic reasoning tasks, such as expressing
integrity constraints. The codex language on the other hand has built-in support for
rules. These rules can operate on the concepts, relationships and constraints defined in
the ontology, as well as any other arbitrary atoms and predicates desired by the user.
The logical underpinning for codex is provided by Datalog, providing closed world
semantics such as default negation and unique name assumption as well as support
for recursive queries. As consequence of being Datalog based, codex also supports all
of the semantics of SQL easily extending the benefits of semantic technologies to
enterprise databases.
Integrity Constraints In OWL, domain and range restrictions constrain the type
of objects that can be related by a role. Also, participation restrictions specify that
certain objects have relationships to other objects.
For example, we can state that each student must have a student number as:
Student ⊆ ∃hasStudentN umber.StudentN umber [10]. However, even though this
restriction can be expressed, its semantics are quite different from those of an equiva-
lent constraint in a relational database. A relational database will not allow the user
to insert a student without specifying a student number. Because of its open world
assumption, OWL will allow a Student without a student number, since the assump-
tion will be that the student has a student number, but it hasn’t yet been added to
the ABox. Straightforward specification and enforcement of integrity constraints is
extremely important for enterprises as such constraints are an essential part of their
domain and business model. In the codex language it is trivial to specify and apply
such constraints.
3.3 Disjunctive Reasoning
The Codex Language is developed on top of the DLV system [4,9] that allows to
exploit the answer set semantics and stable model semantics [6] for disjunctive logic
programs. The possibility to exploit disjunction (in the head of rules) and constraints
enable to express reasoning tasks for solving complex real problems. The disjunction
allows to compute a set of models (search space) that can contain the possible solu-
tion for a given problem, whereas constraints allow to choice the solution by adopting
a brave or cautious reasoning approach. In the brave reasoning are considered the
solutions that are true in at least one model, in the cautious reasoning a solution to
be considered acceptable must be true in all the models. Disjunction allows to express
very rich business rules and to model reasoning task able to solve different kinds of
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problems like: planning problems (under incomplete knowledge), constraints satisfi-
ability, abductive diagnosis. In the following, in order to better explain disjunctive
capabilities of the Codex Language an example of team building is provided.
module(teamBuilding){
(r) inTeam(E, P) ∨ outTeam(E, P) :- E:employee(),P:project().
(c1) :- P:project(numEmp:N),not #count{E:inTeam(E, P)}=N.
(c2) :- P:project(numSk:S),not #count{Sk:E:employee(skill:Sk), inTeam(E, P)} ≥ S.
(c3) :- P:project(budget:B),not #sum{Sa,E:E:employee(salary:Sa), inTeam(E, P)} ≤ B.
(c4) :- P:project(maxSal:M),not #max{Sa:E:employee(salary:Sa), inTeam(E, P)} ≤ M. }
The reasoning module contain a disjunctive rule r that guesses whether an employee
is included in the team or not, generating the search space by exploiting the answer
set semantics. The constraints c1, c2, c3, and c4 model the project requirements,
filtering out those solutions that do not satisfy the constraints. So knowledge encoded
into the Semantic Model can be exploited for providing solutions to complex business
problems.
3.4 Interoperability With the Semantic Web
Currently OWL is the standard language on which the Semantic Web movements
is trying to really implement it [16]. A lot of dictionaries, thesaurus and ontologies,
expressed by means of this language, are already available. Many companies and
organizations have invested in the construction of semantic resource like enterprise
models and domain ontologies that they want use for building semantic applications.
So all the organizations deal with the problem to reuse these semantic resources,
developed by means of OWL, and to make them interoperable with already existing
databases and application.
The SET paradigm address the interoperability problem with SWT providing
an import-export approach that enable to ”translate” OWL ontologies in Semantic
Models (without descriptors) and viceversa. When a Semantic Model is obtained from
an already existing OWL ontologies descriptors can be added in order to enable SET
to exploit the model for semantic applications.
To make OWL and the Codex Language interoperable is a complex problem be-
cause it requires the translation from OWL (based on description logic) to the Codex
Language (based on Disjunctive Logic Programming). Problems related to the joining
of DL and DLP has been addressed by many authors [5,8,10]. They presented many
methods to translate OWL-DL to logic programming. All these methods require first
the definition of which fragment of OWL to deal with. For example, In [10] a detailed
description of how a considerable fragment of OWL-DL can be processed within logic
programming systems. To this end, the author derives an enhanced characterization of
Horn-SHIQ, the description logic for which this translation is possible, and explained
how the generated Datalog programs can be used in a standard logic programming
paradigm without sacrificing soundness or completeness of reasoning. For the trans-
lation from OWL to Codex Language some fragments of the languages for which the
semantic equivalence between the original OWL ontology and the obtained Semantic
Model is guaranteed have been identified. In this context semantic equivalence means
the possibility to obtain the same entailment in the source and in the destination
language despite the differences of semantic assumptions. A more detailed discussion
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FIRST - First Industrial Results of Semantic Technologies
around OWL-Codex Language translation is out of the scope of this paper, for further
details is possible to consult [3].
4 SETA: The SET reference Architecture
The SET reference Architecture describes the technologies and architecture en-
abling the use of Semantics in an enterprise. The various entities for enabling semantic
applications in an enterprise are shown in Fig. 2. below.
Fig. 2. Semantic Enterprise Fig. 3. Ontology Management
In this figure we identify the high level components composing the Semantic Enter-
prise. These include tools and technologies for:
– modeling and managing Semantic Models
– specifying rules over a Semantic Model
– extracting metadata from unstructured and semi-structured sources
– storing and indexing the extracted metadata
– reasoning over extracted metadata and existing structured data
The modeling environment usually includes a graphical interface (GUI) for creating,
modifying and managing Semantic Models. This interface also allows one to spec-
ify rules over the Semantic Model. These rules may include the concept descriptors
and the reasoning engine uses the constraints and relationships in conjunction with
these rules for performing its reasoning tasks. It is vital to include both structured
and unstructured data in order to enable the semantic enterprise. This requires two
important features from the architecture: (i) use of existing structured data sources,
(ii) extraction of useful information from unstructured data sources. The technologies
comprising the semantic architecture should be able to use the existing enterprise
databases for providing semantic capabilities since the enterprises have large amounts
of data and hence it is not feasible to convert all this data into a different format
for the purpose of enabling semantic enterprise. So far all of the technologies in the
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enterprise have only been able to use structured data for providing decision making
capabilities and enabling various applications. With the availability of ontologies and
reasoning capabilities it is now possible to leverage unstructured and semi-structured
data, since the semantics allow us to assign the correct interpretation. It is important
to remember that a large amount of information is locked up in such unstructured
sources, and it is important to include it in any decision making process and applica-
tions. Extracting useful information from unstructured data requires a multi-pronged
approach. This kind of approach includes:
– Natural Language Processing (NLP) including Part of Speech (POS) tagging,
sentence splitting, entity extraction, and other NLP related capabilities.
– Concept recognition and extraction.
– Supervised and unsupervised classification.
Supporting the Semantic Enterprise is a well defined process that requires the
management of Semantic Models. This goes beyond ontology modeling, and includes
the entire ontology life-cycle management comprising of ontology versioning, combina-
tion of ontologies into a higher level ontology, ontology comparison, and the addition
of rules and descriptors to ontologies as shown in Fig. 3. above.
5 An e-Health Application
In this section an application of SET to a real case is shown. The scope of the
application is to support wards to monitor errors and risks causes in lung cancer cares.
The application has been developed in the context of a project aimed at provide some
Italian hospitals with health care risk management capabilities.
The application takes as input Electronic Medical Records (EMRs) and risk re-
ports coming from different hospital wards. An EMR is generally a flat text document
(having usually 3 pages) written in Italian natural language. EMRs are weakly struc-
tured, for example, the personal data of the patient are in the top of the document,
clinical events (e.g medical exams, surgical operations, diagnosis, etc.) are introduced
by a date. Risk reports, filled at the end of clinical process, are provided to patients by
wards to acquire information about errors with or without serious outcomes, adverse
events, near misses.
The goal of the application is to extract semantic metadata about oncology ther-
apies and errors with temporal data. The application extracts personal information
(name, age, address), diagnosis data (diagnosis time, kind of tumor, body part affected
by the cancer, cancer progression level), care and therapies information. Extracted
information are exploited to construct, for each cared patient, an instance of lung
cancer clinical process. Acquired process instances are analyzed by means of data and
process mining techniques in order to discover if errors happen following patterns in
phases of drugs prescription, preparation or administration.
The application has been obtained by representing a medical Semantic Model
inherent to lung cancer that contains: (i) concepts and relationships referred to the
disease, its diagnosis, cares in term of surgical operations and chemotherapies with
the associated side effects. Concepts related to persons (patients), body parts and
risk causes are also represented. All the concepts related to the cancer come from the
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FIRST - First Industrial Results of Semantic Technologies
ICD9-CM diseases classification system, whereas the chemotherapy drugs taxonomy,
is inspired at the Anatomic Therapeutic Chemical (ATC) classification system. (ii) a
set of descriptors enabling the automatic acquisition of the above mentioned concepts
from Electronic Medical Records (EMRs). In the following a piece of the medical
Semantic Model that describes (and allows to extract) patient name, surname, age
and disease is shown.
class anatomy ().
class body_part (bp:string) isa {anatomy}.
class organ isa {body_part}.
lung: organ("Lung").
->.
...
...
class disease (name:string).
tumor: disease("Tumor").
->.
cancer: disease("Cancer").
->.
...
relation synonym (d1:disease,d2:disease)
synonym(cancer,tumor).
...
class body_part_desease () isa {disease}.
lung_cancer: body_part_disease("Lung cancer").
-> CONTAIN &
...
collection class patient_data (){}
collection class patient_name (name:string){}
-> {Y := X;}
SEPBY .
collection class patient_surname (surname:string){}
->
{Y:=X;} SEPBY .
collection class patient_age (age:integer){}
-> {Y := $str2int(Z);}
SEPBY .
...
collection class patient_data (name:string, surname:string,
age:integer, diagnosis:body_part_disease){}
->
CONTAIN {X:=X1}
& {Y:=Y1} & {Z:=Z1} & .
...
The classes diagnosis section and hospitalization section used in the above
descriptors represent text paragraphs containing personal data and diagnosis data
recognized by proper descriptors that aren’t shown for lack of space. The extraction
mechanism can be considered in a WOXM fashion: Write Once eXtract Many, in fact
the same descriptors can be used to enable the extraction of metadata related to
patient affected by lung cancer in unstructured EMRs that have different arrange-
ment. Moreover, descriptors are obtained by automatic writing methods (as happens,
for example, for the cancer and tumor concepts) or by visual composition (as happens
for patient data)
Metadata extracted by using the Semantic Model are stored as collection class
instances into a knowledge base. For the simple piece of Semantic Model shown above
the extraction process generates the following patient data class instance for an
EMR: "@1": patient data("Mario","Rossi","70",lung cancer).
The application is able to process many EMRs and risk reports in a single execu-
tion and to store extracted metadata in XML format.
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FIRST - First Industrial Results of Semantic Technologies
6 The Business Value of Semantic Enterprise Technologies
The SET paradigm allows value creation from different perspectives. From the
technological point of view, the value creation capabilities of the SET paradigm can
be explained by introducing the knowledge powered computing vision. This vision is
founded on the transformation of enterprise information into knowledge by converting
knowledge into software via a portfolio of embeddable semantic components. Seman-
tic Models transform information into knowledge and can be leveraged to directly
embed knowledge into software making it actionable. This way Semantic-aware enter-
prise applications can be obtained. In fact, to provide value SET cannot be separate,
external, or isolated, rather they have to interoperate with the complex portfolios
of applications and information repositories, owned by enterprises to enhance their
performances.
From the strategic point of view, turning knowledge into software enable to deliver
a new generation of knowledge powered features that allow to better assist in driving
business processes and in making decisions. A new categories of knowledge worker can
leverage such enhanced features to obtain functionalities and domain knowledge inter-
changes that creates information intelligence where knowledge powered applications
deliver more precise answers with adaptive responses. So better performances can be
achieved by improving decision support and making, exception handling, emergency
response, compliance, risk management, situation assessment, command and control.
From the tactical point of view, is important to note that SET features enable
a new way to use data and information. SET allow to leverage new information re-
sources like e-mail, web pages, forums, blogs, wikis, CRM transcripts, search logs,
organizational documents as already happens for database. To exploit executable Se-
mantic Models enables a better understanding of the interrelationships and shared
context of existing structured and unstructured enterprise information. So more in-
formed users can work smarter with better business process execution and monitoring,
decision-making and planning.
7 Conclusion
This paper presented the Semantic Enterprise Technologies (SET) paradigm. SET
are based on the concept of Semantic Model that are executable, flexible and agile
representation of domain knowledge (e.g. simple taxonomies equipped with few and
simple descriptors, very rich ontologies equipped with complex business rules and de-
scriptors) and to exploit it for managing both structured (e.g. relation databases) and
unstructured information (e.g. document repositories). Semantic Models are expressed
by means of the Codex Language obtained combining Disjunctive Logic Programming
(Datalog plus disjunction) and Attribute Grammars both extended by object-oriented
and two-dimensional capabilities. SET overcome the limitation of Semantic Web Tech-
nologies (SWT) in enterprise domains. SET provide mechanisms to address several
important modeling problems that frequently happens in enterprises and that are
hard, if not impossible to solve using OWL alone, but can easily be addressed us-
ing the Codex Language. SET are interoperable with SWT thank to a translation
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FIRST - First Industrial Results of Semantic Technologies
mechanism. Translation allows to import portions of already existing OWL ontolo-
gies to use in semantic enterprise applications and to export portions of Semantic
Models toward semantic Web applications. Leveraging enhanced semantic features of
SET, enterprises can transform information into knowledge in order to achieve better
business performances.
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