Showing papers on "Upper ontology published in 2007"

Ontology Matching

Abstract: Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology visualization methods—a survey

Abstract: Ontologies, as sets of concepts and their interrelations in a specific domain, have proven to be a useful tool in the areas of digital libraries, the semantic web, and personalized information management. As a result, there is a growing need for effective ontology visualization for design, management and browsing. There exist several ontology visualization methods and also a number of techniques used in other contexts that could be adapted for ontology representation. The purpose of this article is to present these techniques and categorize their characteristics and features in order to assist method selection and promote future research in the area of ontology visualization.

On Ontology, ontologies, Conceptualizations, Modeling Languages, and (Meta)Models

Abstract: In philosophy, the term ontology has been used since the 17th century to refer both to a philosophical discipline (Ontology with a capital “O”), and as a domain-independent system of categories that can be used in the conceptualization of domain-specific scientific theories. In the past decades there has been a growing interest in the subject of ontology in computer and information sciences. In the last few years, this interest has expanded considerably in the context of the Semantic Web and MDA (Model-Driven Architecture) research efforts, and due to the role ontologies are perceived to play in these initiatives. In this paper, we explore the relations between Ontology and ontologies in the philosophical sense with domain ontologies in computer science. Moreover, we elaborate on formal characterizations for the notions of ontology, conceptualization and metamodel, as well as on the relations between these notions. Additionally, we discuss a set of criteria that a modeling language should meet in order to be considered a suitable language to model phenomena in a given domain, and present a systematic framework for language evaluation and design. Furthermore, we argue for the importance of ontology in both philosophical senses aforementioned for designing and evaluating a suitable general ontology representation language, and we address the question whether the so-called Ontology Web languages can be considered as suitable general ontology representation languages. Finally, we motivate the need for two complementary classes of modeling languages in Ontology Engineering addressing two separate sets of concerns.

An Ontology Framework for Semantic Business Process Management

Abstract: A core challenge in Business Process Management is the continuous, bi-directional translation between (1) a business requirements view on the process space of an enterprise and (2) the actual process space of this enterprise, constituted by the multiplicity of IT systems, resources, and human labor. Semantic Business Process Management (SBPM) [HeLD'05] 1 is a novel approach of increasing the level of automation in the translation between these two spheres, and is currently driven by major players from the ERP, BPM, and Semantic Web Services domain, namely SAP 2 . One core paradigm of SPBM is to represent the two spheres and their parts using ontology languages and to employ machine reasoning for the automated or semi-automated translation. In this paper, we (1) outline the representational requirements of SBPM, (2) propose a set of ontologies and formalisms, and (3) define the scope of these ontologies by giving competency questions, which is a common technique in the ontology engineering process.

COMM: designing a well-founded multimedia ontology for the web

Abstract: Semantic descriptions of non-textual media available on the web can be used to facilitate retrieval and presentation of media assets and documents containing them. While technologies for multimedia semantic descriptions already exist, there is as yet no formal description of a high quality multimedia ontology that is compatible with existing (semantic) web technologies. We explain the complexity of the problem using an annotation scenario. We then derive a number of requirements for specifying a formal multimedia ontology before we present the developed ontology, COMM, and evaluate it with respect to our requirements. We provide an API for generating multimedia annotations that conform to COMM.

Measuring semantic similarity between Gene Ontology terms

Abstract: Many bioinformatics applications would benefit from comparing proteins based on their biological role rather than their sequence. This paper adds two new contributions. First, a study of the correlation between Gene Ontology (GO) terms and family similarity demonstrates that protein families constitute an appropriate baseline for validating GO similarity. Secondly, we introduce GraSM, a novel method that uses all the information in the graph structure of the Gene Ontology, instead of considering it as a hierarchical tree. GraSM gives a consistently higher family similarity correlation on all aspects of GO than the original semantic similarity measures.

Ontology learning: state of the art and open issues

Abstract: Ontology is one of the fundamental cornerstones of the semantic Web The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning We propose a new learning-oriented model for ontology development and a framework for ontology learning Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort The paper offers a road map and a variety of insights about this fast-growing field

Methodologies for ontology development

Abstract: It is now widely recognised that constructing a domain model, or ontology, is an important step in the development of knowledge based systems. What is lacking, however, is a clear understanding of how to build ontologies. In this paper we survey the work which has been done so far in beginning to provide a methodology for building ontologies. This work is still formative, and relies heavily on particular experiences. We also provide some discussion of this work, and identify the key issues that must be addressed if we are to move on from ontology construction being an art and to make it an understood engineering process.

Conservative extensions in expressive description logics

Abstract: The notion of a conservative extension plays a central role in ontology design and integration: it can be used to formalize ontology refinements, safe mergings of two ontologies, and independent modules inside an ontology. Regarding reasoning support, the most basic task is to decide whether one ontology is a conservative extension of another. It has recently been proved that this problem is decidable and 2ExpTime-complete if ontologies are formulated in the basic description logic ALC. We consider more expressive description logics and begin to map out the boundary between logics for which conservativity is decidable and those for which it is not. We prove that conservative extensions are 2ExpTime-complete in ALCQI, but undecidable in ALCQIO. We also show that if conservative extensions are defined model-theoretically rather than in terms of the consequence relation, they are undecidable already in ALC.

Possible Ontologies: How Reality Constrains the Development of Relevant Ontologies

Abstract: For years, ontologies have been known in computer science as consensual models of domains of discourse, usually implemented as formal definitions of the relevant conceptual entities. Researchers have written much about the potential benefits of using them, and most of us regard ontologies as central building blocks of the semantic Web and other semantic systems. Unfortunately, the number and quality of actual, "non-toy" ontologies available on the Web today is remarkably low. This implies that the semantic Web community has yet to build practically useful ontologies for a lot of relevant domains in order to make the semantic Web a reality. Theoretically minded advocates often assume that the lack of ontologies is because the "stupid business people haven't realized ontologies' enormous benefits." As a liberal market economist, the author assumes that humans can generally figure out what's best for their well-being, at least in the long run, and that they act accordingly. In other words, the fact that people haven't yet created as many useful ontologies as the ontology research community would like might indicate either unresolved technical limitations or the existence of sound rationales for why individuals refrain from building them - or both. Indeed, several social and technical difficulties exist that put a brake on developing and eventually constrain the space of possible ontologies

Automated ontology construction for unstructured text documents

Abstract: Ontology is playing an increasingly important role in knowledge management and the Semantic Web. This study presents a novel episode-based ontology construction mechanism to extract domain ontology from unstructured text documents. Additionally, fuzzy numbers for conceptual similarity computing are presented for concept clustering and taxonomic relation definitions. Moreover, concept attributes and operations can be extracted from episodes to construct a domain ontology, while non-taxonomic relations can be generated from episodes. The fuzzy inference mechanism is also applied to obtain new instances for ontology learning. Experimental results show that the proposed approach can effectively construct a Chinese domain ontology from unstructured text documents.

A logical framework for modularity of ontologies

Abstract: Modularity is a key requirement for collaborative ontology engineering and for distributed ontology reuse on the Web. Modern ontology languages, such as OWL, are logic-based, and thus a useful notion of modularity needs to take the semantics of ontologies and their implications into account. We propose a logic-based notion of modularity that allows the modeler to specify the external signature of their ontology, whose symbols are assumed to be defined in some other ontology. We define two restrictions on the usage of the external signature, a syntactic and a slightly less restrictive, semantic one, each of which is decidable and guarantees a certain kind of "black-box" behavior, which enables the controlled merging of ontologies. Analysis of real-world ontologies suggests that these restrictions are not too onerous.

A function–behavior–structure ontology of processes

Abstract: This paper presents how the function–behavior–structure (FBS) ontology can be used to represent processes despite its original focus on representing objects. The FBS ontology provides a uniform framework for classifying processes, and includes higher level semantics in their representation. We show that this ontology supports a situated view of processes based on a model of three interacting worlds. The situated FBS framework is then used to describe the situated design of processes.

Repairing ontology mappings

Abstract: Automatically discovering semantic relations between ontologies is an important task with respect to overcoming semantic heterogeneity on the semantic web. Existing ontology matching systems, however, often produce erroneous mappings. In this paper, we address the problem of errors in mappings by proposing a completely automatic debugging method for ontology mappings. The method uses logical reasoning to discover and repair logical inconsistencies caused by erroneous mappings. We describe the debugging method and report experiments on mappings submitted to the ontology alignment evaluation challenge that show that the proposed method actually improves mappings created by different matching systems without any human intervention.

Ontology module extraction for ontology reuse: an ontology engineering perspective

Abstract: Problems resulting from the management of shared, distributed knowledge has led to ontologies being employed as a solution, in order to effectively integrate information across applications. This is dependent on having ways to share and reuse existing ontologies; with the increased availability of ontologies on the web, some of which include thousands of concepts, novel and more efficient methods for reuse are being devised. One possible way to achieve efficient ontology reuse is through the process of ontology module extraction. A novel approach to ontology module extraction is presented that aims to achieve more efficient reuse of very large ontologies; the motivation is drawn from an Ontology Engineering perspective. This paper provides a definition of ontology modules from the reuse perspective and an approach to module extraction based on such a definition. An abstract graph model for module extraction has been defined, along with a module extraction algorithm. The novel contribution of this paper is a module extraction algorithm that is independent of the language in which the ontology is expressed. This has been implemented in ModTool; a tool that produces ontology modules via extraction. Experiments were conducted to compare ModTool to other modularisation methods.

Ontology Maturing: a Collaborative Web 2.0 Approach to Ontology Engineering

Abstract: Most of the current methodologies for building ontologies rely on specialized knowledge engineers. This is in contrast to real-world settings, where the need for maintenance of domain specific ontologies emerges in the daily work of users. But in order to allow for participatory ontology engineering, we need to have a more realistic conceptual model of how ontologies develop in the real world. We introduce the ontology maturing processes which is based on the insight that ontology engineering is a collaborative informal learning process and for which we analyze characteristic evolution steps and triggers that have users engage in ontology engineering within their everyday work processes. This model integrates tagging and folksonomies with formal ontologies and shows maturing pathways between them. As implementations of this model, we present two case studies and the corresponding tools. The first is about image-based ontology engineering (introducing so-called imagenotions), the second about ontology-enabled social bookmarking (SOBOLEO). Both of them are inspired by lightweight Web 2.0 approaches and allow for realtime collaboration.

Foundations for a Core Ontology of Manufacturing

Abstract: An initial fragment of a core ontology for the manufacturing domain is presented and motivated. It consists of an ontological classification of ADACOR concepts according to the DOLCE foundational ontology. The ontology is conceptually transparent and semantically explicit thus suitable for information communication, sharing, and retrieval. The system here described considers entities performing the manufacturing scheduling and control operations only.

The double role of ontologies in information science research

Abstract: In philosophy, Ontology is the basic description of things in the world. In information science, an ontology refers to an engineering artifact, constituted by a specific vocabulary used to describe a certain reality. Ontologies have been proposed for validating both conceptual models and conceptual schemas; however, these roles are quite dissimilar. In this article, we show that ontologies can be better understood if we classify the different uses of the term as it appears in the literature. First, we explain Ontology (upper case O) as used in Philosophy. Then, we propose a differentiation between ontologies of information systems and ontologies for information systems. All three concepts have an important role in information science. We clarify the different meanings and uses of Ontology and ontologies through a comparison of research by Wand and Weber and by Guarino in ontology-driven information systems. The contributions of this article are twofold: (a) It provides a better understanding of what ontologies are, and (b) it explains the double role of ontologies in information science research.

Leveraging data and structure in ontology integration

Abstract: There is a great deal of research on ontology integration which makes use of rich logical constraints to reason about the structural and logical alignment of ontologies. There is also considerable work on matching data instances from heterogeneous schema or ontologies. However, little work exploits the fact that ontologies include both data and structure. We aim to close this gap by presenting a new algorithm (ILIADS) that tightly integrates both data matching and logical reasoning to achieve better matching of ontologies. We evaluate our algorithm on a set of 30 pairs of OWL Lite ontologies with the schema and data matchings found by human reviewers. We compare against two systems - the ontology matching tool FCA-merge [28] and the schema matching tool COMA++ [1]. ILIADS shows an average improvement of 25% in quality over FCA-merge and a 11% improvement in recall over COMA++.

A methodology for creating ontologies for engineering design

Abstract: This paper describes a six-stage methodology for developing ontologies for engineering design, together with the research methods and evaluation of each stage. The methodology focuses upon understanding a user's domain models through empirical research. A case study of an ontology for searching, indexing, and retrieving engineering knowledge is described. The root concepts of the ontology were elicited from engineering designers. Relationships between concepts are extracted as the ontology is populated. The contribution of this research is a methodology to allow researchers and industry to create ontologies for their particular purpose and a thesaurus for the terms within the ontology.

OSS: a semantic similarity function based on hierarchical ontologies

Abstract: Various approaches have been proposed to quantify the similarity between concepts in an ontology We present a novel approach that allows similarities to be asymmetric while still using only information contained in the structure of the ontology We show through experiments on the WordNet and GeneOntology that the new approach achieves better accuracy than existing techniques

OntoDB: an ontology-based database for data intensive applications

Abstract: Recently, several approaches and systems were proposed to store in the same database data and the ontologies describing their meanings. We call these databases, ontology-based databases (OBDBs). Ontology-based data denotes those data that represent ontology individuals (i.e., instance of ontology classes). To speed up query execution on the top of these OBDBs, efficient representations of ontology-based data become a new challenge. Two main representation schemes have been proposed for ontology-based data: vertical and binary representations with a variant called hybrid. In these schemes, each instance is split into a number of tuples. In this paper, we propose a new representation of ontology-based data, called table per class. It consists in associating a table to each ontology class, where all property values of a class instance are represented in a same row. Columns of this table represent those properties of the ontology class that are associated with a value for at least one instance of this class. We present the architecture of our ontology-based databases and a comparison of the effectiveness of our representation scheme with the existing ones used in Semantic Web applications. Our benchmark involves three categories of queries: (1) targeted class queries, where users know the classes they are querying, (2) no targeted class queries, where users do not know the class(es) they are querying, and (3) update queries.

A Universal Ontology for Sensor Networks Data

Abstract: In this paper, we present our work towards the development and evaluation of an ontology for searching distributed and heterogeneous sensor networks data. In particular, we propose a two layer prototype ontology that utilizes the IEEE Suggested Upper Merged Ontology (SUMO) as a root definition of general concepts and associations and two sub- ontologies: the sensor data sub-ontology and the sensor hierarchy sub-ontology. The proposed ontology was implemented using Protege 2000 and eventually evaluated using the RDQL language (RDF Data Query Language). The performance analysis demonstrated the ability of the ontology-based search to improve both the precision and recall rates and enhance the interoperability between different sensor networks domains through the use of the universal SUMO ontology.

OntoGen: semi-automatic ontology editor

Abstract: In this paper we present a semi-automatic ontology editor as implemented in a new version of OntoGen system. The system integrates machine learning and text mining algorithms into an efficient user interface lowering the entry barrier for users who are not professional ontology engineers. The main features of the systems include unsupervised and supervised methods for concept suggestion and concept naming, as well as ontology and concept visualization. The system was tested in extensive user trails and in several real-world scenarios with very positive results.

SOR: a practical system for ontology storage, reasoning and search

Abstract: Ontology, an explicit specification of shared conceptualization, has been increasingly used to define formal data semantics and improve data reusability and interoperability in enterprise information systems. In this paper, we present and demonstrate SOR (Scalable Ontology Repository), a practical system for ontology storage, reasoning, and search. SOR uses Relational DBMS to store ontologies, performs inference over them, and supports SPARQL language for query. Furthermore, a faceted search with relationship navigation is designed and implemented for ontology search. This demonstration shows how to efficiently solve three key problems in practical ontology management in RDBMS, namely storage, reasoning, and search. Moreover, we show how the SOR system is used for semantic master data management.