Ontology Evolution Research Articles

Structured chemical class definitions and automated matching for chemical ontology evolution

Ontologies encode the knowledge of human experts in order to allow computers to automate common tasks in a domain. They are hierarchically organised and backed by computational logic which allows automated inferences of the implicit consequences of explicitly stated knowledge. ChEBI is a database and ontology of chemical entities of biological interest [1]. Within the ontology, chemical entities are classified based on shared structural features and also based on their roles and activities in biological systems. For example, the chemical class ‘aminopyridine’ is defined as ‘Compounds containing a pyridine skeleton substituted by one or more amine groups’, while an example of a role based class is ‘antiviral drug’, which groups together chemical entities that are used as antiviral drugs, regardless of their chemical structure. We have developed a novel semi-automated system for creating structure-based chemical class definitions. Our tool allows curators to draw and visually define shared structural features for classes of chemicals, which definitions are then used to automatically detect class membership across the full chemical database. The front end is based on an extended JChemPaint [2] and the Google Web Toolkit, and the back-end on a custom extension of the Chemistry Development Kit [3]. With this tool, it is possible to define chemical classes based on molecular skeletons, substitute groups, arbitrary parts including cycles of arbitrary length, formulae and overall properties, and these features can be combined using nested logical operators. Matching these definitions to candidate structures from the database is accomplished by means of an in-memory matching procedure, validated against the existing manually curated classification in ChEBI, allowing us to iteratively refine both the definitions of classes as well as to evolve the quality of the classification in ChEBI.

Political awakenings in an artificial state: Iraq, 1914-20

Rather than addressing the dated debate of Iraq's 'artificiality', this article analyses the evolution of the term 'Iraq' and by extension the evolution in frames of self-definition in the years 1914-20. I use three key events (the anti-British jihad of 1914, the Najaf rebellion of 1918 and the rebellion of 1920) and examine the discourse that accompanied the events to analyse the changing categories of self-identification on the mid-Euphrates. A clearly discernible ontological evolution of 'Iraq' in the popular imagination is revealed thereby clarifying and explaining the rapid rise and adoption of Iraqi nationalism in the early twentieth century.

A Novel Global Measure Approach based on Ontology Spectrum to Evaluate Ontology Enrichment

the context of ontology evolution in real world applications, particularly in the field of semantic web, ontologies are called to change in their structure as well as their semantic. It is necessary to evaluate the quality based on stability to make analysis to get appropriate enrichment manner for ontology evolution. In this paper, we introduce a new approach with that aims making three contributions. First, we present a new aspect of ontology quality based on its stability. Second, we present a new notion called ontology spectrum which can be used for analyzing ontology stability. Third, we provide an experimental method to evaluate this new aspect of quality within two processes: individual measure based on semantic similarity measures and global measure based on ontology spectrum.

A model for ripple effects analysis of cascading problems in ontology evolution

Ontology engineering is one of semantic web's most important activities. Inconsistencies observed on cascading changes of nodes would arise simply as result of any small operation on an ontology component. It is therefore necessary to have tools to evaluate consequent flows on other components as result of an operation on ontology. In this paper, we present an approach that provides an algorithm and a support for ripple effects analysis of cascading problems in ontology evolution by derivation of changes based on corresponding pre-conditions, post-conditions and invariants.

Robust and Efficient Annotation based on Ontology Evolution for Deep Web Data

Among those researches in Deep Web, compared to research of data extraction which is more mature, the research of data annotation is still at its preliminary stage. Currently, although the approach of applying ontology in data annotating has been approved by most researchers, there are many weaknesses existed, such as the complexity of the ontology, as well as the limitation on static ontology’s ability to annotate new pages. Respond to those problems, this paper proposes a robust, highly efficient data annotation method based on ontology evolution. It needs to be noticed that this paper defines a simpler ontology which can improve annotating efficiency significantly. Experiments indicate that this method could improve the accuracy and efficiency of data annotation.

Data extraction and annotation based on domain-specific ontology evolution for deep web

Deep web respond to a user query result records encoded in HTML files. Data extraction and data annotation, which are important for many applications, extracts and annotates the record from the HTML pages. We proposed an domain-specific ontology based data extraction and annotation technique; we first construct mini-ontology for specific domain according to information of query interface and query result pages; then, use constructed mini-ontology for identifying data areas and mapping data annotations in data extraction; in order to adapt to new sample set, mini-ontology will evolve dynamically based on data extraction and data annotation. Experimental results demonstrate that this method has higher precision and recall in data extraction and data annotation.

GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution

BackgroundOntologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.ResultsWe present GOMMA, a generic infrastructure for managing and analyzing life science ontologies and their evolution. GOMMA utilizes a generic repository to uniformly and efficiently manage ontology versions and different kinds of mappings. Furthermore, it provides components for ontology matching, and determining evolutionary ontology changes. These components are used by analysis tools, such as the Ontology Evolution Explorer (OnEX) and the detection of unstable ontology regions. We introduce the component-based infrastructure and show analysis results for selected components and life science applications. GOMMA is available at http://dbs.uni-leipzig.de/GOMMA.ConclusionsGOMMA provides a comprehensive and scalable infrastructure to manage large life science ontologies and analyze their evolution. Key functions include a generic storage of ontology versions and mappings, support for ontology matching and determining ontology changes. The supported features for analyzing ontology changes are helpful to assess their impact on ontology-dependent applications such as for term enrichment. GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches.

Quality controls in integrative approaches to detect errors and inconsistencies in biological databases

SummaryNumerous biomolecular data are available, but they are scattered in many databases and only some of them are curated by experts. Most available data are computationally derived and include errors and inconsistencies. Effective use of available data in order to derive new knowledge hence requires data integration and quality improvement. Many approaches for data integration have been proposed. Data warehousing seams to be the most adequate when comprehensive analysis of integrated data is required. This makes it the most suitable also to implement comprehensive quality controls on integrated data. We previously developed GFINDer (http://www.bioinformatics.polimi.it/GFINDer/), a web system that supports scientists in effectively using available information. It allows comprehensive statistical analysis and mining of functional and phenotypic annotations of gene lists, such as those identified by high-throughput biomolecular experiments. GFINDer backend is composed of a multi-organism genomic and proteomic data warehouse (GPDW). Within the GPDW, several controlled terminologies and ontologies, which describe gene and gene product related biomolecular processes, functions and phenotypes, are imported and integrated, together with their associations with genes and proteins of several organisms. In order to ease maintaining updated the GPDW and to ensure the best possible quality of data integrated in subsequent updating of the data warehouse, we developed several automatic procedures. Within them, we implemented numerous data quality control techniques to test the integrated data for a variety of possible errors and inconsistencies. Among other features, the implemented controls check data structure and completeness, ontological data consistency, ID format and evolution, unexpected data quantification values, and consistency of data from single and multiple sources. We use the implemented controls to analyze the quality of data available from several different biological databases and integrated in the GFINDer data warehouse. By doing so, we identified in these data a variety of different types of errors and inconsistencies; this enables us to ensure good quality of the data in the GFINDer data warehouse. We reported all identified data errors and inconsistencies to the curators of the original databases from where the data were retrieved, who mainly corrected them in subsequent updating of the original database. This contributed to improve the quality of the data available, in the original databases, to the whole scientific community.

Automatic Approach to Ontology Evolution Based on Change Impact Comparisons

Ontology evolution is the timely adaptation of ontologies to changing requirements, which is becoming more and more important as ontologies become widely used in different fields. This paper shows how to address the problem of evolving ontologies with less manual case-based reasoning using an automatic selection mechanism. An automatic ontology evolution strategy selection framework is presented that automates the evolution. A minimal change impact algorithm is also developed for the framework. The method is shown to be effective in a case study.

Eagle-i: An Ontology-Driven Framework For Biomedical Resource Curation And Discovery

The eagle-i Consortium ("http://www.eagle-i.org/home":www.eagle-i.org/home) comprises nine geographically and ethnically diverse universities across America working to build a federated network of research resources. Biomedical research generates many resources that are rarely shared or published, including: reagents, protocols, instruments, expertise, organisms, training opportunities, software, human studies, and biological specimens. The goal of eagle-i is to improve biomedical research by helping researchers more easily find scientific resources that are difficult to discover, reducing time-consuming and expensive duplication of resources. Now in early development, the system will ultimately expand to include research resources at other universities following the end of the two-year pilot phase. An application ontology is being developed to enable representation of core facility and research lab resources in the eagle-i repository, leading to more effective searches and better linkage between data types. The eagle-i ontology will guide users to valid queries via auto-suggestion, ontology browsing, concept-based search, and synonym expansion. The ontology development effort is being guided by active discussions within the ontology community and brings together relevant preexisting ontologies in a logical framework. Components of the data entry and search interfaces are generated directly from the ontology, which allows rapid change in response to user needs and ontology evolution. Each eagle-i institution will populate and manage a local repository using data collection and curation tools. To enhance the quantity and quality of data, the data tools will take advantage of the ontology to support semi-automated annotation of resources. NIH/NCRR ARRA award U24RR029825.

Eagle-i: An Ontology-Driven Framework For Biomedical Resource Curation And Discovery

AbstractThe eagle-i Consortium ("http://www.eagle-i.org/home":www.eagle-i.org/home) comprises nine geographically and ethnically diverse universities across America working to build a federated network of research resources. Biomedical research generates many resources that are rarely shared or published, including: reagents, protocols, instruments, expertise, organisms, training opportunities, software, human studies, and biological specimens. The goal of eagle-i is to improve biomedical research by helping researchers more easily find scientific resources that are difficult to discover, reducing time-consuming and expensive duplication of resources. Now in early development, the system will ultimately expand to include research resources at other universities following the end of the two-year pilot phase. An application ontology is being developed to enable representation of core facility and research lab resources in the eagle-i repository, leading to more effective searches and better linkage between data types. The eagle-i ontology will guide users to valid queries via auto-suggestion, ontology browsing, concept-based search, and synonym expansion. The ontology development effort is being guided by active discussions within the ontology community and brings together relevant preexisting ontologies in a logical framework. Components of the data entry and search interfaces are generated directly from the ontology, which allows rapid change in response to user needs and ontology evolution. Each eagle-i institution will populate and manage a local repository using data collection and curation tools. To enhance the quantity and quality of data, the data tools will take advantage of the ontology to support semi-automated annotation of resources. NIH/NCRR ARRA award U24RR029825.

Heterogeneous Deep Web Data Extraction Using Ontology Evolution

This paper proposed a complex ontology evolution based method of extracting data, and also completely designed an extraction system, which consists of four important components: Resolver, Extractor, Consolidator and the ontology construction components. The system gives priority to the construction of mini-ontology. When the user submits query keywords to the deep web query interface, the returned result will pass through the prior three components; after that, the final execution result will be returned to user in a unified form. This paper adopted an extraction method that is different from the general ontology extraction. More specifically, the ontology used in extraction here is dynamic evolution, which can adapt various data source better. Experimental results proved that this method could effectively extract the data in the query result pages.

Ontological realism: A methodology for coordinated evolution of scientific ontologies

Since 2002 we have been testing and refining a methodology for ontology development that is now being used by multiple groups of researchers in different life science domains. Gary Merrill, in a re...

A design methodology of ontology based database applications

Recently, ontologies emerge in various domains and different communities. For instance, in the database area, they are used to facilitate data exchange and integration, in the Semantic Web field, to describe terms, retrieve information and interconnect Web services. Several ontology languages and formalisms are given (OWL, PLIB, etc.). Due to the ontology evolution, various applications manipulate a huge amount of ontological data (data referencing ontologies). Consequently, they cannot fit in the main memory. To offer efficient storage and querying mechanisms of these data, academicians and industrials proposed database solutions storing in the same repository data and the ontology describing their senses. Such database is called ontology-based databases (OBDB). Despite this trend, no design methodology adapted to OBDB exists contrary to traditional databases, where two main methodologies inspired from: Codd-Armstrong and Chen research efforts are proposed. Developing a design methodology for OBDB constitutes a crucial challenge for the success of OBDB applications. Similarities between conceptual models and ontologies recently identified motivate us to propose an OBDB design methodology inspired from the existing ones. To do so, we first describe in details these similarities and differences. Secondly, we propose an ontology-driven methodology to derive a normalized logical schema of a database from domain ontology whose each class contains a set of FDs. Finally, a prototype implementing our methodology is presented.

Personalized ontology-based semantic annotation and retrieval of image model

In order to solve the problem that it is difficult to implement semantic retrieval of image,a novel ontology-cored semantic annotation and retrieval model of image was proposed,which constructed a personalized ontology to describe image,extracted the image semantic features based on the concept set and used inter-relationship "Is-A" of ontology to measure the similarity between the images and query.The difficulty lies in the personalized evolution of the top ontology,as well as the semantic similarity measure based on concept set and inter-relationship " Is-A ".This model was implemented and some related experiments were carried out.The experimental results validate the retrieval accuracy as high as 88.6%,significantly higher than the traditional keyword-based and generic ontology-based image retrieval models,which realized intelligent retrieval of image.

Fuzzy Multi-Attribute Decision Making-Based Algorithm for Semantic Web Service Composition

Choosing the global optimal execution plan is an important process in the semantic Web service composition. The plan selection based on QoS is still challenging because the heterogeneous QoS values make data aggregation and decision making hard. This paper presents a novel Fuzzy Multi-attribute decision making-based semantic Web service Composition algorithm (FuMuCom) to solve the above difficulties for the first time. FuMuCom takes all possible QoS expression types (real number, interval and linguistic expression) into consideration. It includes three main steps: defuzzifying linguistic data, normalizing the decision matrix and evaluating alternatives synthetically. Other contributions of the paper include an extensible QoS ontology to express the heterogeneous QoS values, an ontology evolution strategy for aggregating QoS and a set of experiments that demonstrate the benefits and effectiveness of our approach.

A Human-Centered Semantic Service Platform for the Digital Ecosystems Environment

Digital Ecosystems (DEST) have emerged with the purpose of enhancing communications among small and medium enterprises (SMEs) within the worldwide Business Ecosystem. However, because of the diversity and heterogeneity of the services in the DEST environment, existing commercial products or research outputs cannot be directly applied to this field so as to fulfill the requirements of SMEs. Human-centered computing has been applied to many areas, such as social classification, community-based ontology evolution, and more importantly, human-centered systems. In this paper, we propose a framework for a human-centered semantic service platform, in order to address the issue in the DEST environment. This framework incorporates the features of human-centered metadata publishing, maintenance and clustering, community-based ontology revolution and human-centered service retrieval, evaluation and ranking. To thoroughly validate the framework, we implement a prototype in the transport service domain, and conduct a series of evaluation experiments on the basis of this prototype.

Learning subsumption hierarchies of ontology concepts from texts

Abstract. This paper proposes a method for learning ontologies given a corpus of text documents. The method identifies conceptsin documents and organizes them into a subsumption hierarchy, without presupposing the existence of a seed ontology. Themethod uncovers latent topics for generating document text. The discovered topics form the concepts of the new ontology.Concept discovery is done in a language neutral way, using probabilistic space reduction techniques over the original term spaceof the corpus. Furthermore, the proposed method constructs a subsumption hierarchy of the concepts by performing conditionalindependence tests among pairs of latent topics, given a third one. The paper provides experimental results on the Genia and theLonely Planet corpora from the domains of molecular biology and tourism respectively.Keywords: Ontology Learning, Concept Discovery, Subsumption Hierarchy Construction, Latent Dirichlet Allocation, ConditionalIndependence 1. IntroductionOntologies have been proposed as the key ele-ment to shape, manage and further process knowledge.However, the engineering of ontologies is a costly,time-consuming and error-prone task when done man-ually. Furthermore, in quickly evolving domains ofknowledge, or in cases where information is constantlybeing updated, possibly making prior knowledge obso-lete, the continuous maintenance and evolution of on-tologies are tasks that require significant human effort.Thus, there is a strong need to automate the ontologydevelopment/maintenance tasks in order to minimizethe cost of ontology creation and evolution.For this reason, ontology learning has emerged as afield of research, aiming to help knowledge engineersto build and further extend ontologies with the help ofautomated or semi-automated machine learning tech-niques, exploiting several sources of information. On-tology learning is commonly viewed ([1], [10], [30],[35]) as the task of extending or enriching an exist-ing ontology with new ontology elements mined fromtext corpora. Depending on the ontology elements be-ing discovered, existing approaches deal with the iden-tification of concepts, subsumption relations amongconcepts, instances of concepts, or concept proper-ties/relations. Linguistic, statistical, or machine learn-ing techniques are used for these tasks.The seed ontology used in ontology enrichmentis usually a hierarchical backbone of concepts, re-lated via subsumption relations, or a generic ontologythat formalizes some of the concepts in a documentcollection. Linguistic approaches additionally sufferfrom language dependence, as they rely on language-specific lexico-syntactic patterns.In contrast to the majority of the existing work, thispaper proposes an automated approach to ontologylearning, without presupposing the existence of a seedontology, or any other type of external resource, exceptthe corpus of training text documents. The proposed

Use semantic decision tables to improve meaning evolution support systems

Meaning evolution support systems (MESSs) have been recently introduced as a real time, scalable, community-based cooperative systems to support the ontology evolution. In this paper, we intend to address the problems of accuracy and effectiveness by using Semantic Decision Tables (SDTs). A SDT separates general decision rules from the processes, bootstraps policies and template dependencies in the whole system. Recently, DOGMA-MESS ('developing ontology-grounded methodology and applications' framework based 'MESS') are developed at VUB STARLab as a collection of MESS. We embed SDTs in DOGMA-MESS to illustrate our approach. SDTs play the roles in both top-down and bottom-up processes of the meaning evolution cycle. The decision rules that consist of templates dependency rules are mainly responsible for the top-down process execution. The bottom-up process execution relies on the ones that contain the concept lifting algorithms.

Approach and tool to evolve ontology and maintain its coherence

The concept of ontology is more and more used to provide a shared understanding of a domain of interest and to enhance communication among humans, computers and software. However, ontologies are often used in changing environments and, therefore, must be adapted to evolution requirements. This paper proposes an approach to manage the ontology evolution and to maintain its coherence after changing. This approach anticipates incoherencies that can be generated and proposes additional operations to correct them. To assist users in expressing and applying evolution requirements, an ontology evolution tool has been developed and applied to develop the Tunisian Education system.