Automatic Semantic Annotation: Towards Resolution

of WFIO Incompatibilities

Chahrazed Tarabet, Meriem Mouzai and Ali Abbassene

Centre de Développement des Technologies Avancées, Baba Hassen, Algiers, Algeria

Keywords: IOWF, NLP, Automatic Semantic Annotation, Ontology.

Abstract: Inter-organizational workflows (IOWF) allow for orchestration of processes between different

organizations, but the incompatibility they reveal poses a serious problem. Nevertheless, there are

approaches that can remedy this problem, notably the semantic annotation. In this position paper, we will

present a study whose objective is to address the detection and correction of these incompatibilities between

workflow partners. For this purpose, amelioration, optimization and automation are necessary for the

semantic annotation phase of inter-organizational workflows, in order to achieve the IOWF incompatibility

resolution.

1 INTRODUCTION

An organization is a coordinating unit, with

identifiable boundaries, working to achieve a goal

shared by its participating members. Nowadays, the

company is no longer industrial but commercial, it is

no longer located in a single place, it is extended, it

even happens not to be fully visible but to be (in part)

virtual. These companies need inter-organizational

cooperative systems, that is, across multiple network

organizations. In this context, information and

communication technologies play an essential role in

enabling enterprises to exchange all types of data.

The inter-organizational workflows go in the

same direction, allowing an orchestration of

processes between several organizations. However,

the incompatibility of the latter poses a serious

problem (Abbassene, Alimazighi and Aouachria,

2015). Nevertheless, there are approaches that can

remedy this problem, notably the semantic

annotation. This approach represents a mechanism for

linking a data to its semantic description represented

by a concept derived from an ontology. It is an

effective way to detect and correct these

incompatibilities between workflow partners. For this

purpose, amelioration, optimization and automation

are necessary for the semantic annotation phase of

inter-organizational workflows.

In this position paper, we will present a critical

review of the works that deal with the automation of

semantic annotation phase, using techniques such as

NLP (Natural Language Processing). Thus, we

propose a solution to the problematic posed according

to the results obtained.

2 LITERATURE REVIEW

In this section, we will present the works of the

semantic annotation domain found in the literature

that we judge interesting.

Authors in (Davis et al., 2009) use Controlled

Natural Languages (CNLs) which are subsets of

natural language whose grammars and dictionaries

have been restricted in order to reduce or eliminate

both ambiguity and complexity.

These languages prompt the novice user to

annotate, while simultaneously creating, their

respective documents in a user-friendly way, while

protecting them from the formalisms of

representation of the complex underlying knowledge.

CNLs have already been applied successfully in the

context of authoring ontology, but very little research

has focused on CNLs for semantic annotation. They

describe here a user-friendly semantic annotator,

based on CLIE (Controlled Language for Information

Extraction) tools.

However, this annotator only allows non-expert

users to write and annotate minutes of meetings and

semi-automatic status reports using controlled natural

language.

Also, this other work (semantator) (Tao et al.,

556

Tarabet, C., Mouzai, M. and Abbassene, A.

Automatic Semantic Annotation: Towards Resolution of WFIO Incompatibilities.

DOI: 10.5220/0006377505560562

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 2, pages 556-562

ISBN: 978-989-758-248-6

2013), which is an annotation tool that allows to

annotate documents with semantic Web ontologies

using a loaded free text document and an ontology,

users can annotate document fragments with classes

in the ontology to create instances and link instances

created with ontology properties. Thus, it provides:

1) Basic manual annotation features: creation /

deletion of ontology instance, creation / deletion of

relationship, binding of equivalent instances and

export / reload of existing annotations;

2) Automatic annotation by connecting to the

annotator NCBO and cTAKES;

3) Basic reasoning support based on the

underlying semantics of the preachers owl:

disjointWith and owl: equivalentClass.

In contrast, this tool only allows semi-automatic

annotation and focuses on clinical documents.

The main objective of the works (Kiyavitskaya et

al., 2006) and (Kiyavitskaya et al., 2005) is to present

a methodology supported by tools that semi-

automates the semantic annotation process for a set of

documents in relation to a semantic model (ontology

or conceptual schema). It is proposed to address the

problem using highly efficient and proven methods

and tools in the field of software analysis for

processing billions of source code lines of legacy

software.

The semantic annotation method of documents

uses generalized syntactic analysis and the TXL

structural transformation system, the basis of the LS /

2000 (Kiyavitskaya et al., 2006) automated system.

TXL is a programming language specially designed

to allow, for example, rapid prototyping of language

descriptions, tools and applications. The system

accepts as input a grammar and a document,

generates an analysis tree for the input document and

applies transformation rules to generate an output in a

target format (Kiyavitskaya et al., 2006), this

transformation phase is not yet implemented in the

work (Kiyavitskaya et al., 2005).

The disadvantage of these approaches is that it

deals only with the semi-automatic annotation, so the

results of the document (Kiyavitskaya et al., 2005)

are adopted only in the tourism sector.

Antti Vehvilainen in this work (Vehvilainen,

Hyvonen and Alm, 2008) dealt with applications of

semantic Web technologies to help office services.

They focus on QA (Questions / Answers) support

services, where the service database is composed of

answers to the previous questions, that is, QA pairs.

They propose a semi-automatic semantic annotation

of natural language text for the question-answer (QA)

pairs annotation and case-based reasoning techniques

to find similar questions. The methodology consists

of using semantic Web technologies in content

annotation, using the QA repository and integrating

the information available online on the Web with the

creation process and responses. They consider here

the usefulness of CBR (Case-Based Reasonning) in

the indexing and the extraction of informations since

the similar pairs of QAs reproduce in the services of

QA. Case-based reasoning (CBR) is a problem-

solving paradigm in artificial intelligence where new

problems are solved based on previously experienced

similar problems. The CBR cycle consists of four

phases:

1) Retrieve the most similar case (s),

2) Reuse the recovered cases to solve the problem,

3) Revise the proposed solution, and

4) Retain the solution as a new case in the base

of cases.

Nevertheless, this approach is not generalized in

all domains and only allows the semi-automatic

annotation.

They treat here (Qacim and Salih, 213), the

semantic similarity between sentences based on

WordNet semantic dictionary. The proposed

algorithm will be based on a number of resources,

including Ontology and WordNet.

The goal of this research (Qacim and Salih, 213)

is to create an efficient automatic annotation platform

that develops a way to automatically generate

metadata to semantically annotate Web documents

that improve information retrieval. The proposed

system should be easily understood by non-technical

users who may not be familiar with the technical

language used to create ontologies. The proposed

system provides ontological similarity to determine

the relationships between words in sentences and

concepts in ontology. It has been found that the

meaning of the term similarity is ambiguous because

of its use in many different contexts, such as

biological, logical, statistical, taxonomic,

psychological, semantic, and many other contexts, to

resolve ambiguities, WordNet Must be used to

provide a lexical ontology.

The semi-automated annotation of texts in natural

language was approached in this work (Erdmann et

al., 2000) by designing an information extraction-

based approach for semi-automated annotation, which

was implemented on SMES (Saarbrucken Message

Extraction System), (Erdmann et al., 2000) which

includes a tokenizer based on regular expressions. It

is a generic component that respects several

principles that are crucial to its objectives. (i) it is fast

and robust, (ii) it maps terms to ontological concepts,

(iii) produces dependency relationships between

terms, and (iv) is easily adaptable to new domains. As

Automatic Semantic Annotation: Towards Resolution of WFIO Incompatibilities

557

in this approach, finite state technologies support

lexical acquisition as well as semantic marking. The

goal of the global process is the generation of so-

called lexical networks that can be used to enable

automatic and semi-automatic construction of texts

on the Web. Incoming documents are processed using

the SMES information retrieval system. SMES

associates simple words or complex expressions with

a concept of ontology, linked by the domain lexical

link. Recognized concepts and relationships between

concepts are underlined as suggested annotations.

This mechanism has the advantage that all relevant

informations in the ontology document are

recognized and proposed to the annotator, but it’s

applied only on text in natural language and is not

fully automated.

Aurélie Névéol

in this article (Névéol, Doğan and

Lu, 2011) dealt with the production of annotated data

which is a necessary step for many natural language

processing (NLP) or information processing tasks.

They have studied the semantic annotation of a

large number of biomedical requests. This study

shows that automatic pre-annotations are considered

useful by most annotators to speed up the annotation

rate and improve the consistency of the annotations

while maintaining a high quality of the final

annotations. The disadvantage of this work is that its

field of application is restricted to the biomedical

domain.

This work (Dingli, Ciravegna and Wilks, 2003)

proposes a methodology to learn to automatically

annotate specific domain information from large

repositories with minimal user intervention. The

methodology is based on a combination of

information retrieval, information integration and

automatic learning. Learning is sown by extracting

information from structured sources. The retrieved

information is then used to partially annotate

documents. These annotated documents are used for

learning bootstrap for simple information extraction

(IE). It will be used to form more complex IE engines

and the cycle will continue to repeat until the required

information is obtained. User intervention is limited

to providing an initial URL and correcting

information if this is the case when the calculation is

completed. The revised annotation can then be re-

used to provide additional training and thus obtain

more information and / or more precision.

The methodology was fully implemented in

Armadillo, a system for extracting and integrating

unsupervised data from large collections of

documents.

This other work (Kiryakov et al., 2004) seeks to

create an efficient, robust and scalable architecture

for automatic semantic annotation, and to implement

this architecture in a component-based platform for

indexing and semantic search on large collections of

documents. What is considered a primary innovative

contribution, is the fact that it offers an end-to-end

extensible system that processes the complete cycle

of creating metadata, storing and semantic search, for

online use that provide navigation semantically

improved. Another approach presented in (Tulasi et

al., 2017) deals with automatic semantic annotation

based on ontologies, where all documents are

collected from the Web and a database is created. The

documents are then given as an input to make a

semantic annotation on the ontology.

In this paper (Kiryakov et al., 2004) authors

present a holistic architecture view for automatic

semantic annotation with references to classes in

ontology and instances, on the basis of these semantic

annotations, it has indexed and retrieved documents.

A system (called KIM), implementing this concept

(Popov et al., 2003), provides a new infrastructure

and knowledge and information management services

for automated semantic annotation, indexing and

document retrieval, it provides also a mature

infrastructure for scalable and customizable

information extraction (IE) and annotation and

document management, based on GATE.

GATE (General Architecture for Text

Engineering) (Ranganathan, Biletskiy and

Kaltchenko, 2008), developed by the University of

Sheffield, is an efficient tool used to perform some

Natural Language Processing (NLP) operations. It

has many features, such as manual annotation,

automatic annotation using a variety of

nomenclatures, information retrieval, ontology-based

processing, and so on. GATE has played a major role

in text annotation, which can be presented in different

formats, such as: eml, mail, text, dhtm, pdf, xhtml,

xml, rtf, txt, sgm, Sgml, htm, etc. Annotations are

mainly performed to communicate semantics in

electronic documents and / or underlining text that

provides better human understanding.

From a technical point of view, the platform

allows KIM-based applications to use it for automatic

semantic annotation, retrieval of content based on

semantic restrictions, and querying and modifying

ontologies and underlying knowledge bases.

In this article (Leopold et al., 2015), they present

an approach to automatically annotate process models

with the concepts of a taxonomy. At this point, the

focus is on business-based taxonomies, such as the

Supply Chain Operations Reference Model (SCOR),

the MIT Process Manual, and the Process

Classification Framework (PCF). To do this, they

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propose an approach that combines the measurement

of semantic similarity with probabilistic optimization.

In particular, they use different types of similarity

between the process model and the taxonomy and the

distance between the concepts of taxonomy to guide

the matching with a formalization of the Markov

logic.

The semantic annotation is also used in the S-

CREAM project. The approach is interesting for the

strong implication of the automatic learning

techniques for an automatic extraction of the relations

between the annotated entities (Handschuh, Staab and

Ciravegna, 2002).

A similar approach is also taken in the MnM

project, where semantic annotations can be placed

online in the content of the document and refer to an

ontology and a KB (WebOnto) server, accessible via

an API (Vargas-Vera et al., 2002).

The QuizRDF module, used to provide

improvements to a standard full-text search with the

metadata extracted from OntoBuilder. QuizRDF is an

important source of inspiration for the design of KIM.

An interesting indexing of the named entity and a

system of questions / answers. Once indexed, the

content is queried via NL questions, with the NE

mark on the question used to determine the type of

response expected (Davies, Fensel and Van

Harmelen, 2003).

AeroDAML takes a similar approach to KIM,

but implements it as a prototype of research on a

much smaller scale (Kogut and Holmes, 2001).

SemTag is a platform closer in terms of

objectives and architecture to KIM, which performs

the semantic annotation on a large scale compared to

the TAP ontology which is very similar in size and

structure to the KIM and KB ontologies. SEMTAGS

gradually creates a first-order markov model based on

existing annotations and proposes a semantic

annotation, new syntactic trees. It first performs a

search phase, annotating all possible references to

instances of the TAP ontology. In the second phase of

disambiguation, SemTag uses a vector space model to

assign the correct ontological class or to determine

that this statement does not correspond to a class in

TAP. Disambiguation is performed by comparing the

context of the current statement (10 words on the left

and 10 on the right) with the case contexts in TAP

with compatible aliases (Dill et al., 2003) (Guha and

McCool, 2001).

With regard to semi-automatic semantic

annotation mechanisms, Pustejovsky describes the

approach for semantic indexing and typed hyperlinks

(Pustejovsky et al., 1997).

Another approach to semantic annotation of data

has improved the retrieval of information and

improved interoperability where a new approach

based on NLP ontology has been proposed and

applied on annual reports (Wang et al., 2009).

The results presented in this section are the result

of a detailed study on the state of the art on the

techniques and approaches used for the improvement,

optimization and automation of the semantic

annotation phase.

We have synthesized the results obtained in the

following table:

Table 1: Comparison of the presented works.

Semi-

automatic

Automatic Application Approach used

Suitable for

WFIO

(Davis et al., 2009) yes no

Administration (meeting

minutes - status report)

CNL no

(Tao et al., 2013) yes no Clinic Ontology / NLP no

(Kiyavitskaya et al.,

2006) (Kiyavitskaya

et al., 2005)

yes no Tourism Ontology / TXL no

(Vehvilainen,

Hyvonen and Alm,

2008)

yes no Help Desk Service CBR no

(Qacim and Salih,

213)

- yes Various fields Ontology / WordNet no

(Erdmann et al.,

2000)

yes no Text in natural language

Regular Expressions

/ SMES

(Névéol, Doğan and

Lu, 2011)

- yes Biomedical

NLP / pre-

annotations

Automatic Semantic Annotation: Towards Resolution of WFIO Incompatibilities

559

Table 1: Comparison of the presented works (cont.).

(Dingli, Ciravegna

and Wilks, 2003)

- yes Various fields

IE / Automatic

learning

(Kiryakov et al.,

2004) (Popov et al.,

2003)

- yes

Any type of text (web

page / regular document

(non web))

Ontology / KIM / IE no

(Leopold et al.,

2015)

- yes Various fields Markov Logic no

Based on the results obtained, we consider that the

works (Davis et al., 2009), (Tao et al., 2013),

(Kiyavitskaya et al., 2006), (Kiyavitskaya et al.,

2005), (Vehvilainen, Hyvonen and Alm, 2008) and

(Erdmann et al., 2000) partially automate the

semantic annotation phase while it is completely

automatic in (Qacim and Salih, 213), (Névéol, Doğan

and Lu, 2011), (Dingli, Ciravegna and Wilks, 2003)

and (Kiryakov et al., 2004). Thus, we note that some

works use common approaches including, NLP and

ontology. As for the application, most of the works

focus on a specific field. Finally, we consider

adaptation to inter-organizational workflows an

important criterion because they allow to answer the

problem posed in section 1, they also allow the

collaboration between organizations with a better

exchange of data while being flexible and effective

(Semar-Bitah and Boukhalfa, 2016). However, the

approaches used in the cited works in this paper do

not cover the notion of inter-organizational

workflows.

3 CONTRIBUTION

The aim of this study is to explore the different works

related to the domain of automation of semantic

annotation in order to define a solution whose

objective is to detect and to correct the

incompatibilities between the workflow partners.

To this end, so that organizations can collaborate

with better compatibility, we propose an approach

that aims to automate the semantic annotation phase

for inter-organizational workflows (IOWFs) using the

NLP approach that has proved to be successful. We

recommend the adoption of methods to improve and

optimize the IOWF semantic annotation, namely:

1) Hierarchy: It allows to improve the annotation by

providing a formal framework that allows to argue

on the consistency of the extracted information. In

particular, semantic hierarchies have proved to be

very useful in reducing the semantic gap. Three

types of hierarchies for image annotation and

classification have been recently explored:

1) Hierarchies based on textual knowledge;

2) Hierarchies based on visual (or perceptual)

information, i.e. low-level characteristics of the

image;

3) Hierarchies that we call semantic based on both

textual and visual information (Bannour and

Céline, 2013).

2) Indexing: It makes it possible to document the

knowledge represented by the semantic

annotations, or even to keep them up to date when

the reference texts evolve. In general, it is a

question of using the semantic structure

constructed by the annotations to identify

elements in a document and to navigate semantic

elements to fragments of text or vice versa.

However, an indexing process consists of

annotating text and gathering it by following a

semantic organization (Lévy, Nazarenko and

Guissé, 2010) (Guissé et al., 2010).

3) Learning: A learning process is characterized by

an interaction between the learner and the

environment by setting up a system capable of

learning how to annotate a given corpus. The goal

of this method is to acquire better or new

knowledge and / or a mechanism or procedure

(inference engine and knowledge) by deducing a

set of rules. There are three techniques of learning

in particular, learning patterns, digital learning

and active learning (Bannour and Audibert, 2012).

4 CONCLUSION AND FUTURE

WORKS

In this paper, we have presented the different

approaches to semantic annotation found in the

literature to remedy the problems of incompatibility

of inter-organizational workflows. On the basis of the

study carried out, we have recommended approaches

that can contribute to the automation phase of

semantic annotations while improving and optimizing

the semantic annotations.

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In the future, we want to concretise our vision by

adopting the approaches cited in Section 3 to ensure

better collaboration among workflow partners.

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