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Creating, integrating and maintaining local and global ontologies
Abstract
In this paper, we explore the issues related to creating, integrating and maintaining multiple local ontologies, and how and whether a global reference ontology can be of use. We note that a major problem in existing organizations is the lack of consistent terminology. The adoption of a global reference vocabulary or ontology seems an attractive solution in theory, but in practice, this approach seems untenable. We believe that a mixed approach which enables local groups to build maintain and use their own ontologies, in conjunction with a global reference ontology, and mapping between them is a more viable approach. We describe various issues and approaches that arise in attempting to do this.
... • The concept identifier. Each concept should have a natural language-independent, unique, and immutable identifier [13]. Among other uses, identifiers are used for defining facts. ...
... Terminology. Some authors point out that "different communities of practice use the same terms with quite different meanings" [13], which can be a problem for the references to the concepts of the UO. This is the well-known problem of homonymy and/or polysemy in natural languages. ...
... • "Although some may think the solution is to come up with a single context for the whole world… in reality this is extremely difficult for any complex organization" [44] • "… people will always disagree about what terms to use and how to define them, a global ontology will always be seen as flawed" [13] • "It is of course unrealistic to hope that there will be an agreement on one or even a small set of ontologies" [45] • "Enforcing one centralized global ontology… is impractical to develop an ontology with consent from the user community at large" [54] • "A single huge ontology of everything is difficult to accomplish, as the effort of getting consensus on it becomes unimaginable" [46]. ...
This paper puts forward a vision of a universal ontology (UO) aiming at solving, or at least greatly alleviating, the semantic integration problem in the field of conceptual modeling and the understandability problem in the field of the semantic web. So far it has been assumed that the UO is not feasible in practice, but we think that it is time to revisit that assumption in the light of the current state-of-the-art. This paper aims to be a step in this direction. We try to make an initial proposal of a feasible UO. We present the scope of the UO, the kinds of its concepts, and the elements that could comprise the specification of each concept. We propose a modular structure for the UO consisting of four levels. We argue that the UO needs a complete set of concept composition operators, and we sketch three of them. We also tackle a few issues related to the feasibility of the UO, which we think that they could be surmountable. Finally, we discuss the desirability of the UO, and we explain why we conjecture that there are already organizations that have the knowledge and resources needed to develop it, and that might have an interest in its development in the near future.
... The final choice is always left to the domain experts, but the system provides them with a set of possible choices including information concerning how and when the attribute changes. Uschold [23] pointed out that the global reference ontology would be the perfect candidate for ontology mapping of local ontologies. Different user communities could view the global reference ontology from their own preferred perspectives through mapping and projecting. ...
... Furthermore, ontology mapping could be complicated by many-to-one, one-tomany or many-to-many relations either within one domain or one that transcends different domains (ontology clustering). Ontology mapping could also be viewed as the projection of the general ontologies from different points of view, either according to the different application domains or various tasks or applications [23]. Much remains to be done in the area of semi-automatic or automatic ontology mapping. ...
... The main difference is that METHOD- OLOGY focuses on comprehensively addressing the maintenance stage of the life cycle of ontology, whereas TOVE utilizes more formal techniques to address a more limited number of maintenance issues. Uschold [23] also pointed out the importance of ontology maintenance between local and global ontologies, especially the importance of standardizing concepts and relations using unique identifier codes and names. ...
This is the second of a two-part paper to review ontology research and development, in particular, ontology mapping and evolving. Ontology is defined as a formal explicit specification of a shared conceptualization. Ontology itself is not a static model so that it must have the potential to capture changes of meanings and relations. As such, mapping and evolving ontologies is part of an essential task of ontology learning and development. Ontology mapping is concerned with reusing existing ontologies, expanding and combining them by some means and enabling a larger pool of information and knowledge in different domains to be integrated to support new communication and use. Ontology evolving, likewise, is concerned with maintaining existing ontologies and extending them as appropriate when new information or knowledge is acquired. It is apparent from the reviews that current research into semi-automatic or automatic ontology research in all the three aspects of generation, mapping and evolving have so far achieved limited success.
Expert human input is essential in almost all cases. Achievements have been made largely in the form of tools and aids to assist the human expert. Many research challenges remain in this field and many of such challenges need to be overcome if the next generation of the Semantic Web is to be realized.
... Further merging of ontologies of different domains will produce the " universal " ontology of the world. The issue of constructing all-inclusive ontology is a very controversial issue (see [26] for an extensive discussion). Besides the philosophical question of creating versal ontology, it will lead to a bottleneck, since further updates and extensions of the ontology will involve a central point of management, unacceptable in pure P2P networks. ...
... For example, consider a simple ontology for cars domain, where the object is described by a vector containing three slots [manufacturer | engine_volume | year_of_production]. Each slot has the following range of values [{Ford, Mercedes, Jaguar} | {1000-1500, 1500-2000, 2000. The semantic P2P system based on this ontology will consist of a hypercube with at most 27 vertices, each having up to 6 neighbors. ...
... For example, consider a simple ontology for cars domain, where the object is described by a vector containing three slots [manufacturer | engine_volume | year_of_production]. Each slot has the following range of values [{Ford, Mercedes, Jaguar} | {1000-1500, 1500-2000, 2000. The semantic P2P system based on this ontology will consist of a hypercube with at most 27 vertices, each having up to 6 neighbors. ...
In this work we consider a general-purpose application for E- Commerce transactions over Peer-to-Peer (P2P) network. Weakly organized structure of P2P systems may cause data management problems and high com- munication overhead. We resolve these problems by developing a novel seman- tic approach to efficiently create, search and organize demand and supply ad- vertisements in E-Commerce P2P applications. The approach is based on the notion of Unspecified Ontology (UNSO). Unlike many existing systems, using a global predefined ontology, UNSO approach assumes that the ontology is not fully defined, leaving some parts of it to be dynamically specified by the user. The ads, inserted to the system, organize a multi-layered hypercube graph to- pology, providing an infrastructure for semantic search and routing operations. The proposed method has the potential of becoming a practical infrastructure for P2P-based publish-locate applications.
... Ontologies provide a layer of abstraction on top data, taxonomy or database schema [1] and offer richer semantics through concept mapping [2]. Various literature [2][3][4][5][6][7][8][9][10] indicate the potential of ontology mapping for data interoperability between heterogeneous sources. ...
... Semantic Technique. Semantic mapping is the most challenging area and a key research area [8,9]. The key feature of semantic mapping is the use of model theoretic semantics to define well-formed-formula (wff) to express the meaning of anything without ambiguity. ...
This paper surveys existing ontology mapping techniques towards data interoperability. Existing matcher algorithms and strategies are discussed generally and the research gaps are highlighted. The study concludes that semantic mapping has the biggest share of unresolved problems. Bridging the gap in semantic mapping may help improving mapping of ostensibly different domain knowledge and disparate data sources.
... Apart from detection of built up environment, OOA has also been successful in other fields for example: reviving legacy maps , urban forest mapping (Walker et al., 2007), assessment of landslide susceptibility (Park et al., 2008), mapping of benthic marine habitats (Lucieer, 2008) and oil spill contamination mapping (Hese et al., 2008). The advancement in feature recognition and advanced image analysis techniques facilitates the extraction of thematic information, for policy making support and informed decisions, irrespective of particular application fields (Lang, 2008). ...
... Top level ontology describe very general concepts like space, time, matter, object, event, action, which are independent of a particular domain (Guarino et al., 1998).They are useful in supporting very broad concepts and act as reference ontology to other levels of ontology. They can either be used as neutral reference format or can be imported in all the local ontology while building the local ontology (Uschold, 2000) . The scale of the global ontology is very important because if it is large enough, it will contain all the concepts proper for a common vocabulary (Minghua et al., 2008) which would be reasonable for large communities of users (Guarino et al., 1998). ...
... This approach was followed by Doerr [8], using the term "core ontology" to refer to an integrative ontology similar to the enterprise model. Uschold [9] defined the global ontology as either an intersection of local ontologies --given that it encompasses concepts, properties and axioms shared by local ontologies --or as a union of elements from all local ontologies in the case of an intended application of the global ontology as one which would reference the entire space of terms. Calvanese [10] introduced a formal framework which facilitates the efficient querying of integrated data corpus in a centralized manner. ...
... The RÉPENER project follows the centralized approach of ontology-based data integration, adopting the terminology of Uschold [9]. Accordingly, ontologies which specify the data located in single sources are called local ontologies, while the central ontology serving as a target for the mapping of local ontologies, being defined as the union of their elements, is called global ontology. ...
... Given the indeterminacy of geographic features used for land cover classification and their increasing availability to the public, Comber and Fisher [10] argue that there is an urgent need for the semantics of data to be made explicit to users. An ontology for the geographic domain should reflect and capture multiple conceptualizations of geographic features [13] The challenge of handling local [14], i.e., domain specific, conceptualizations at a global level is not new and has been a core topic in Artificial Intelligence (AI) research for 30 years [15,16]. The key idea is to be consistent at the local level but allow contradicting conceptualizations within the global overall knowledge base. ...
... In this work we have discussed the importance of local conceptualizations of geographic space. Different communities have developed their own understanding and terminology for good reasons [14]. By introducing microtheories and structuring them by spatial or administrative containment, we have shown how local and potentially contradictory conceptualizations can be reconciled in a common knowledge base. ...
The categorization of our environment into feature types is an essential prerequisite for cartography, geographic information re- trieval, routing applications, spatial decision support systems, and data sharing in general. However, there is no a priori conceptualization of the world and the creation of features and types is an act of cognition. Hu- mans conceptualize their environment based on multiple criteria such as their cultural background, knowledge, motivation, and particularly by space and time. Sharing and making these conceptualizations explicit in a formal, unambiguous way is at the core of semantic interoperability. One way to cope with semantic heterogeneities is by standardization, i.e., by agreeing on a shared conceptualization. This bears the danger of losing local diversity. In contrast, this work proposes the use of microthe- ories for Spatial Data Infrastructures, such as INSPIRE, to account for the diversity of local conceptualizations while maintaining their semantic interoperability at a global level. We introduce a novel methodology to structure ontologies by spatial and temporal aspects, in our case adminis- trative boundaries, which reect variations in feature conceptualization. A local, bottom-up approach, based on non-standard inference, is used to compute global feature denitions which are neither too broad nor too specic. Using dierent conceptualizations of rivers and other geographic feature types, we demonstrate how the present approach can improve the INSPIRE data model and ease its adoption by European member states.
... changing concept relations), specification or representation Yildiz (2006). Concept change can be tracked by investigating obsolete concepts that have changed name, but maintained their identifiers and a history of changes that can later be examined Uschold (2001). ...
Semantic drift is an active field of research, aiming to identify and measure changes in ontologies across time and versions, closely
related to several fields such as ontology evolution and versioning. However, practical and widely adopted methods that may be
directly applied to Semantic Web constructs to measure change have yet to emerge, while most current practices are pertinent to
specific models and domains. This paper presents the SemaDrift Application Suite, a comprehensive set of novel tools and methods
to measure semantic drift in ontologies, in an objective, cross-domain manner. To begin with, a novel set of aspects, metrics and
approaches is established, accounting for both structural and text similarity measures, along with their implementation as an opensource software library to promote their objective usage in applications. With the aim of expanding the user base from semantic
web experts to non-experts, i.e. domain experts, the library is embedded in end-user applications with graphical user interfaces and
additional utilities. Different scenarios are supported. Initially, the SemaDrift plugin of the popular Protege platform for ontology
engineers allows them to simultaneously author the ontology and investigate drift in the same environment. Alternatively, the
SemaDriftFx application allows a wider audience of users to visually investigate drift either as numeric output or a visual chains,
across a series of ontology versions. Proof-of-concept scenarios for the tools are demonstrated to validated their applicability in
usefulness in ontologies of different domains, such as digital preservation, the dutch historical census and web services. Usability
of the applications is assessed via end-user evaluation using standard scales and collecting optimistic feedback.
... In this setting, semantic change, i.e. the structural difference of the same concept in two ontologies [1], relates to various lines of research. Such examples are concept and topic shift [2], concept change [3], semantic decay [4], ontology versioning [5] and evolution [6]. A brief disambiguation of these terms can be found in [7]. ...
Semantic drift is an active research field, which aims to identify and measure changes in ontologies across time and versions. Yet, only few practical methods have emerged that are directly applicable to Semantic Web constructs, while the lack of relevant applications and tools is even greater. This paper presents a novel software tool developed in the context of the PERICLES FP7 project that integrates currently investigated methods, such as text and structural similarity, into the popular ontology authoring platform, Protégé. The graphical user interface provides knowledge engineers and domain experts with access to methods and results without prior programming knowledge. Its applicability and usefulness are validated through two proof-of-concept scenarios in the domains of Web Services and Digital Preservation; especially the latter is a field where such long-term insights are crucial.
... Therefore it can have drastic consequences on the use of knowledge representation models in applications. Semantic change relates to various lines of research such as ontology change, evolution, management and versioning [24], but it also entails ambiguous terms of slightly different meanings, interchanging shifts with drifts and versioning, and applied to concepts, semantics and topics, always related to the thematic composition of collections [54,45,20]. A related term is semantic decay as a metric: it has been empirically shown that the more a concept is reused, the less semantically rich it becomes [27]. ...
In accessibility tests for digital preservation, over time we experience drifts of localized and labelled content in statistical models of evolving semantics represented as a vector field. This articulates the need to detect, measure, interpret and model outcomes of knowledge dynamics. To this end we employ a high-performance machine learning algorithm for the training of extremely large emergent self-organizing maps for exploratory data analysis. The working hypothesis we present here is that the dynamics of semantic drifts can be modeled on a relaxed version of Newtonian mechanics called social mechanics. By using term distances as a measure of semantic relatedness vs. their PageRank values indicating social importance and applied as variable `term mass', gravitation as a metaphor to express changes in the semantic content of a vector field lends a new perspective for experimentation. From `term gravitation' over time, one can compute its generating potential whose fluctuations manifest modifications in pairwise term similarity vs. social importance, thereby updating Osgood's semantic differential. The dataset examined is the public catalog metadata of Tate Galleries, London.
... By using semantic technologies, it is possible to collect and combine data from multiple heterogeneous sources using ontologies to provide a homogeneous view of the information to specific users [45]. The use of ontologies for data integration across different domains has been investigated by various authors over the recent decades [46,47,48,49]. Different methods and techniques, based on mapping and matching, have been used to deal with the problems of semantic heterogeneity that may arise in the process of data integration. ...
Existing BIM technologies do not provide links to product components that are needed to facilitate the participation of manufacturers in the design and building processes. A deeper integration between product component catalogues and BIM is necessary for this purpose. Through the Linked Data approach, information about building products can be obtained from multiple sources and linked to create semantic descriptions of components that can be retrieved from a BIM model via component catalogues. In this article, we present the case of an application of Semantic Web technologies to connect BIM models with a catalogue of structural precast concrete components, carried out within the research project BAUKOM. As a proof of concept we have implemented a service which accesses the information provided by the catalogue to assist the design team in the assembly and dimensioning of structural components during the project phase.
... With regards to the latter Gubric and Fan provide an analysis of six supply chain ontologies [6]. Boeing's Boeing Technical Libraries developed a technical thesaurus in the form of a semantic network incorporating 37,000 concepts with an additional 19,000 synonym concept names, and 100,000 links [7][8][9] to promote common understanding between various partners involved in the manufacturing and design process. ...
... In particular, ontologies can provide data integration acting as mediators between data sources schemas. Ontology-based data integration has been studied by different authors over the recent decades (Goh 1997, Uschold 2000, Shvaiko & Euzenat 2005 and different methods to facilitate mapping and matching operations have been proposed to address the problems of semantic heterogeneity that may arise in the process of data integration. ...
The AEC industry is increasingly requiring more integration in the different phases of the building life-cycle. The progressive adoption of BIM technology is contributing to this integration by facilitating the participation of the multiple actors involved. However, only those who have access to the BIM model can participate in this process leaving out other stakeholders such as manufacturers of building components and systems. To over-come these limitations, it is necessary to link BIM models with other information systems. In this way, BIM models would be part of a comprehensive ecosystem in which they can relate to other AEC databases. We have developed an architecture of an information system which facilitates the connection between building component catalogues, associated services, and BIM software. Semantic Web technologies have been used to integrate multiple data sources providing a unified access which facilitates endusers searching in a building product catalogue. In this paper, we present the work done and discuss the consequences of the application of ontologies to integrate building product catalogues and BIM models
... This is the most popular approach for ontology evolution management. It is proposed and adopted by a number of researchers for KAON ontology language [14] [15] [12] [16] [7] [23]. Change capture phase, change representation phase, semantics of change phase, change propagation phase, change implementation phase and change validation phase constitute this approach. ...
Because of the rapid improvements occurring in the dynamic environment of web applications, ontologies have to be modified to reflect the changes made to the applications. Management of the changes within ontologies is one of the most crucial tasks that needs to be resolved. Various approaches and frameworks have been devised by the researchers to handle it. Despite all the efforts made in this direction, the problem still requires to be researched. To address the problem, we have critically analyzed a number of existing ontology evolution approaches against a criterion we have defined in this paper. Having identified the limitations and weaknesses along with their strengths, we have proposed some requirements that must be incorporated in the design of ontology management approaches.
... With regards to the latter Gubric and Fan provide an analysis of six supply chain ontologies [6]. Boeing's Boeing Technical Libraries developed a technical thesaurus in the form of a semantic network incorporating 37,000 concepts with an additional 19,000 synonym concept names, and 100,000 links [7][8][9] to promote common understanding between various partners involved in the manufacturing and design process. ...
Ontologies have emerged as an important tool in the Enterprise architecture discipline to provide the theoretical founda-tions for designing and representing the enterprise as a whole or a specific area or domain, in a scientific fashion. This paper examines the domain of maintenance, repair, and overhaul (MRO) of the Sikorsky UH-60 helicopter involving multiple enterprises, and represents it through an ontology using the OWL Language and Protégé tool. The resulting ontology gives a formal and unambiguous model/representation of the MRO domain that can be used by multiple par-ties to arrive at a common shared conceptualization of the MRO domain. The ontology is designed to be conformant to ISO 13030 or the Product Life Cycle Support Standard (PLCS) standard, hence representing the state of being as per this standard especially at the interfaces between enterprises while incorporating existing reality to the greatest possible extent within the enterprises. As a result the ontology can be used to design Information Systems (IS) and their inter-faces in all enterprises engaged in MRO to alleviate some of the issues present in the MRO area and to support business intelligence efforts.
... While the patterns and application ontologies can be aligned with top-level ontologies, the proposed framework focuses on mappings between local ontologies. This follows an established paradigm in Artificial Intelligence research, namely developing a lattice of theories that is consistent at the local level while allowing for contradicting conceptualizations within the global knowledge (McCarthy, 1987;Uschold, 2000a;Wachsmuth, 2000). While researchers and ontology engineers move up the stack layer by layer to create and maintain ontologies, the stack can be queried downwards to access provenance information. ...
Big Data, Linked Data, Smart Dust, Digital Earth, and e-Science are just some of the names for research trends that surfaced over the last years. While all of them address different visions and needs, they share a common theme: How do we manage massive amounts of heterogeneous data, derive knowledge out of them instead of drowning in information, and how do we make our findings reproducible and reusable by others? In a network of knowledge, topics span across scientific disciplines and the idea of domain ontologies as common agreements seems like an illusion. In this work, we argue that these trends require a radical paradigm shift in ontology engineering away from a small number of authoritative, global ontologies developed top-down, to a high number of local ontologies that are driven by application needs and developed bottom-up out of observation data. Similarly as the early Web was replaced by a social Web in which volunteers produce data instead of purely consuming it, the next generation of knowledge infrastructures has to enable users to become knowledge engineers themselves. Surprisingly, existing ontology engineering frameworks are not well suited for this new perspective. Hence, we propose an observation-driven ontology engineering framework, show how its layers can be realized using specific methodologies, and relate the framework to existing work on geo-ontologies.
... As a result of this study of the data sources, an energy model is implemented as a global ontology which is the union of the sets of terms from all data sources. Uschold (2000) states that a global ontology can be the intersection of all local ontologies if it will be used as a shared component of them. On the other hand, a global ontology can be understood as a neutral reference which results from the union of all local ontologies. ...
In the IntUBE project, we proposed an energy-information integration platform to model energy information throughout the whole building life cycle. This "platform" approach, however, limited the energy information to the domains established by the data repositories and constrained the relations between data to their contents. In a later project, RÉPENER, we have overcome these limitations by adopting the Linked Open Data approach to create a semantically-based energy information system. In this paper we present the structure of the system and the results obtained in the first implementation phase.
... Recent research efforts, such as [7] and [9], focus on the issue of merging ontologies to generate a global ontology. However, this issue is a controversial one [14]. Using a global shared ontology may lead to an unacceptable situation where updates of the ontology involve a central point of management. ...
Ontologies and Web services are the emerging technologies for service interoperability in eGovernment applications. This work proposes to adapt the notion of "UNSpecified Ontology" (UNSO) to the realm of eGovernment Web services. UNSO allows different users to specify their own personal ontologies, thus eliminating the need for an all-inclusive shared ontology and enhance the users' privacy. In the context of eGoverment Web services, it allows users to describe their need in a relatively free form manner and supports smart semantic matching capabilities which simplify use by inexperienced users. In addition, we propose to use these capabilities for the purpose of composition and orchestration of eGoverment Web services to enable specific behaviors tailored to user needs.
... While human actors are able to exchange their arguments and find a common definition for things like maps, this is not trivial for software agents and services. Therefore local ontologies are needed to specify the world view of each agent, service or provider (Uschold 2000). These ontologies must contain the specification of all resources (DAML-S-Coalition 2003a) in a machine-interpretable way, for example using OWL. ...
E-business is emerging in the market for geographic information (GI). Mostly, offered products are data and some technical services, which do not match the demand of business customers. They require complex GI-solutions, which integrate human services, e.g., consulting, software adaptation, and training. We suggest electronic GI marketplaces for enabling GI service chains integrating technical and human services. The implementation of technical services in an internet business platform requires an ontology-based semantic description language. The most advanced language is OWL-S (formerly DAML-S). The implementation of human services in internet-based service chains also requires semantics. This paper will demonstrate by a typical business scenario, that i. OWL-S fulfills the requirements of a semantic enabling web description for human services and ii. Integration of human and technical GI services in an internet-based service chain can be implemented by the common language OWL-S and thus enhancing GI business by connecting customers and providers.
... Having above considerations in mind, it seems to be much more promising approach to build an ontology starting from individual level, promote their local concepts to higher levels of department and institute in some manner and integrate them to achieve the global picture of institute activities . Such an idea of heterogeneous ontologies in distributed environment has been discussed in [7]–[9]. We stated above that every individual and group hold their own ontology. ...
Though the issue of knowledge management is a hot subject of interest in nowadays market companies, inte-grated solutions fit to the specific needs of research institutes still require more attention. This paper documents a part of the research activities performed at National Institute of Telecommunications, related to development of research insti-tute knowledge management support system. The ideas lying in the background of the system come from the recent the-ories of knowledge creation and creativity support and from experience with everyday practice of knowledge management in market companies. Main focus is put here on the issue of creation of a research topics ontology that is meant to be a semantic backbone of the system. Three-stage approach is proposed, aiming at the construction of ontologies for different levels of organizational hierarchy, from individual researcher, through group or unit, up to the whole institute. Created on-tologies are linked to knowledge resources and support diverse activities performed at those levels. Keywords— creativity support, knowledge creation, ontological engineering, scientific knowledge management.
... The use of such shared terminologies enables a certain degree of inter-operation between these data sources. This, however, does not solve the integration problem completely, because it cannot be expected that all individuals and organizations on the Semantic Web will ever agree on using one common terminology or ontology (Visser & Cui 1998, Uschold 2000). It can be expected that many different ontologies will appear and, in order to enable inter-operation, differences between these ontologies have to be reconciled. ...
Ontology mediation is a broad field of research which is concerned with determining and overcoming differences between ontologies in order to allow the reuse of such ontologies, and the data annotated using these ontologies, throughout different heterogeneous applications. Ontology mediation can be subdivided into three areas: ontology map- ping, which is mostly concerned with the representation of correspon- dences between ontologies; ontology alignment, which is concerned with the (semi-)automatic discovery of correspondences between ontologies; and ontology merging, which is concerned with creating a single new on- tology, based on a number of source ontologies. This chapter reviews the work which has been done in the three men- tioned areas and proposes an integrated approach to ontology mediation in the area of knowledge management. A language is developed for the rep- resentation of correspondences between ontologies. An algorithm, which generalizes current state-of-the-art alignment algorithms, is developed for the (semi-)automated discovery of such mappings. A tool is presented for browsing and editing ontology mappings. An ontology mapping can be used for a variety of different tasks, such as transforming data between different representations and querying different heterogeneous knowledge bases.
... Knowledge resources created for day-to-day purposes in a company can often be adapted for decision support. Uschold noted that a company's glossary or thesaurus could be adapted to become a semantic net [11]. Any manufacturing company, such as General Motors, has numerous lists and lexicons related to the names associated with the design, engineering, manufacturing, and servicing of its products. ...
Domain-specific knowledge is often recorded by experts in the form of unstructured text. For example, in the medical domain,
clinical notes from electronic health records contain a wealth of information. Similar practices are found in other domains.
The challenge we discuss in this paper is how to identify and extract part names from technicians repair notes, a noisy unstructured
text data source from General Motors’ archives of solved vehicle repair problems, with the goal to develop a robust and dynamic
reasoning system to be used as a repair adviser by service technicians. In the present work, we discuss two approaches to
this problem. We present an algorithm for ontology-guided entity disambiguation that uses existing knowledge sources, such
as domain-specific taxonomies and other structured data. We illustrate its use in the automotive domain, using GM parts ontology
and the unit structure of repair manuals text to build context models, which are then used to disambiguate mentions of part-related
entities in the text. We also describe extraction of part names with a small amount of annotated data using hidden Markov
models (HMM) with shrinkage, achieving an f-score of approximately 80%. Next, we used linear-chain conditional random fields (CRF) in order to model observation dependencies
present in the repair notes. Using CRF did not lead to improved performance, but a slight improvement over the HMM results
was obtained by using a weighted combination of the HMM and CRF models.
KeywordsText analysis–Language models–Information extraction–Ontology-guided search
... Knowledge resources created for day-to-day purposes in a company can often be adapted for decision support. Uschold noted that a company's glossary or thesaurus could be adapted to become a semantic net [11]. Any manufacturing company, such as General Motors, has numerous lists and lexicons related to the names associated with the design, engineering, manufacturing, and servicing of its products. ...
Domain-specific knowledge is often recorded by experts in the form of unstructured text. For example, in the medical domain, clinical notes from electronic health records contain a wealth of information. Similar practices are found in other domains. The challenge we discuss in this paper is how to identify and extract part names from technicians repair notes, a noisy unstructured text data source from General Motors' archives of solved vehicle repair problems, with the goal to develop a robust and dynamic reasoning system to be used as a repair adviser by service technicians. In the present work, we discuss two approaches to this problem. We present an algorithm for ontology-guided entity disambiguation that uses existing knowledge sources such as domain-specific ontologies and other structured data. We illustrate its use in automotive domain, using GM parts ontology and the unit structure of repair manuals text to build context models, which are then used to disambiguate mentions of part-related entities in the text. We also describe extraction of part names with a small amount of annotated data using Hidden Markov Models (HMM) with shrinkage, achieving an f-score of approximately 80%. Next we used linear-chain Conditional Random Fields (CRF) in order to model observation dependencies present in the repair notes. Using CRF did not lead to improved performance, but a slight improvement over the HMM results was obtained by using a weighted combination of the HMM and CRF models.
... The starting point for creating an ontology could arise from different situations. An ontology can be created from scratch, from existing ontologies (whether global or local ontologies) only, from a corpus of information sources only; or a combination of the latter two approaches (Uschold, 2000). Various degrees of automation could be used to build ontologies, ranging from fully manual, semi-automated, to fully automated. ...
Ontology is an important emerging discipline that has the huge potential to improve information organization, management and understanding. It has a crucial role to play in enabling content-based access, interoperability, communications, and providing qualitatively new levels of services on the next wave of web transformation in the form of the Semantic Web. The issues pertaining to ontology generation, mapping and maintenance are critical key areas that need to be understood and addressed. This survey is presented in two parts. The first part reviews the state-of-the-art techniques and work done on semi-automatic and automatic ontology generation, as well as the problems facing such research. The second complementary survey is dedicated to ontology mapping and ontology 'evolving'. Through this survey, we have identified that shallow information extraction and natural language processing techniques are deployed to extract concepts or classes from free-text or semi-structured data. However, relation extraction is a very complex and difficult issue to resolve and it has turned out to be the main impediment to ontology learning and applicability. Further research is encouraged to find appropriate and efficient ways to detect or identify relations through semi-automatic and automatic means.
... To refine this generic structure, we assessed the knowledge present in pharmacokinetics 30 but they are viewed as two separate ways of constructing a model. 31 Our approach can be viewed as a middle-out approach 32 : the most important concepts lead to generalization and special- ization. Generic pharmacokinetics concepts were used to select domain-specific candidate terms (CTs) that were then used as reference points to navigate in the initial text and to discover other CTs that often co-occur with them. ...
Develop a detailed representation of pharmacokinetics (PK), derived from the information in Summaries of Product Characteristics (SPCs), for use in computerized systems to help practitioners in pharmaco-therapeutic reasoning.
Available knowledge about PK was studied to identify main PK concepts and organize them in a preliminary generic model. The information from 1950 PK SPC-texts in the French language was studied using a morpho-syntactic analyzer. It produced a list of candidate terms (CTs) from which those describing main PK concepts were selected. The contexts in which they occurred were explored to discover co-occurring CTs. The regrouping according to CT semantic types led to a detailed object-oriented model of PK. The model was evaluated. A random sample of 100 PK texts structured according to the model was judged for completeness and semantic accuracy by 8 experts who were blinded to other experts' responses.
The PK text file contained about 300000 words, and the morpho-syntactic analysis extracted 17520 different CTs. The context of 592 CTs was studied and used to deduce the PK model. It consists of four entities: the information about the real PK process, the experimental protocol, the mathematical modeling, and the influence of factors causing variation. Experts judged that the PK model represented the information in 100 sample PK texts completely in 89% of cases and nearly completely in the other 11%. There was no distortion of meaning in 98% of cases and little distortion in the remainder.
The PK model seems to be applicable to all SPCs and can be used to retranscribe legal information from PK sections of SPCs into structured databases.
CasAware is an Ambient Assisted Living platform, developed within an Italian research project, with the aim to improve the level of comfort and well-being of inhabitants of a house, while optimizing the energy consumption. A key feature, for successful realization of such a platform, is its capability to interoperate with other IoT platforms, which can augment CasAware with additional services. Indeed, this capability facilitates smooth communication between CasAware devices and external devices connected to other IoT platforms, thus allowing efficient exchange of messages among them. However, such integration is hindered by the heterogeneity of data models used in different platforms, which is also related to lack of common standards. In order to realize integration needed for CasAware, this paper presents an approach which exploits results of the INTER-IoT project. Specifically, the INTER-IoT methodology and a set of software tools for achieving IoT interoperability are applied. In the presented study, it is shown how the INTER-IoT based approach can facilitate interoperability between CasAware and two other platforms, which use completely different data models.
Ontology is used as knowledge representation of a particular domain that consists of the concepts and the two relations, namely taxonomic relation and non-taxonomic relation. In ontology, both relations are needed to give more knowledge about the domain texts, especially the non-taxonomic components that used to describe more about that domain. Most existing extraction methods extract the non-taxonomic relation component that exists in a same sentence with two concepts. However, there is a possibility of missing or unsure concept in a sentence, known as an incomplete sentence. It is difficult to identify the matching concepts in this situation. Therefore, this paper presents a method, namely similarity extraction method (SEM) to identify a missing concept in a non-taxonomic relation by using a rough set theory. The SEM will calculate the similarity precision and suggest as much as similar or relevant concepts to replace the missing or unclear value in an incomplete sentence. Data from the Tourism Corpus has been used for the experiment and the results were then evaluated by the domain experts. It is believed that this work is able to increase the pair extraction and thus enrich the domain texts.
We present DINGO (Data INtegration for Grants Ontology), an ontology that provides a machine readable extensible framework to model data relative to projects, funding, actors, and, notably, funding policies in the research landscape. DINGO is designed to yield high modeling power and elasticity to cope with the huge variety in funding, research and policy practices, which makes it applicable also to other areas besides research where funding is an important aspect. We discuss its main features, the principles followed for its development, its community uptake, its maintenance and evolution.
Ontology is a representation of knowledge with a pair of concepts and relationships within a particular domain. Most of extracting techniques for non-taxonomic relation only identifies concepts and relations in a complete sentence. However, this does not represent the domain completely since there are some sentences in a domain text that have a missing or an unsure term of concepts. To overcome this issue, we propose a new algorithm based on probability theory for ontology extraction. The probability theory will be used to handle the incomplete information system, where some of the attribute values in information system are unknown or missing. The new proposed method will calculate and suggest the relevant terms, such as subject or object, that are more likely to replace the unsure value. The proposed method has been tested and evaluated with a collection of domain texts that describe tourism. Precision and recall metrics have been used to evaluate the results of the experiments. The output of this proposed method will be significantly used in the conceptualization process of the ontology engineering process to assist ontology engineers and beneficial to obtain valuable information from a variety of sources of natural language text description such as journal, structured databases of any domain, and also enable to facilitate big data analysis.
Most of the existing methods focus on extracting concepts and identifying the hierarchy of concepts. However, in order to provide the whole view of the domain, the non-taxonomic relationships between concepts are also needed. Most of extracting techniques for non-taxonomic relation only identify concepts and relations in a complete sentence. However, the domain texts may not be properly presented as some sentences in domain text have missing or unsure term of concepts. This paper proposes a technique to overcome the issue of missing concepts in incomplete sentence. The proposed technique is based on the similarity precision for selecting missing concept in incomplete sentence. The approach has been tested with Science corpus. The experiment results were compared with the results that have been evaluated by the domain experts manually. The result shows that the proposed method has increased the relationships of domain texts thus providing better results compared to several existing method.
Semantic drift is an active research field, which aims to identify and measure changes in ontologies across time and versions. Yet, only few practical methods have emerged that are directly applicable to Semantic Web constructs, while the lack of relevant applications and tools is even greater. This paper presents the findings, current limitations and lessons learned throughout the development and the application of a novel software tool, developed in the context of the PERICLES FP7 project, which integrates currently investigated methods, such as text and structural similarity, into the popular ontology authoring platform, Protégé. The graphical user interface provides knowledge engineers and domain experts with access to methods and results without prior programming knowledge. Its applicability and usefulness are validated through two proof-of-concept scenarios in the domains of Web Services and Digital Preservation; especially the latter is a field where such long-term insights are crucial.
In diesem Artikel beschäftigen wir uns mit der Integrati-on von Ontologien. Der wichtige Punkt hierbei ist, zwischen den verschiedensten Ontologien Abbildungen zu finden, die gleiche ode ahnliche Konzepte beider Ontologien verbin-den. Dies ist insofern nicht allzu einfach zu fassen, da oft-mals verschiedene Bezeichnungen ur gleiche Sachverhalte verwendet werden oder aber gleiche Bezeichnungen ur ver-schiedene Sachverhalte. Es gibt sehr viele verschiedene An atze für die Ontolo-gieintegration. Zwei Methoden sollen hier aher erläutert werden. Dies ist zum einen der SMART-Algorithmus und zum anderen das GLUE-System. Letzteres zeichnet sich im Gegensatz zu den anderen An atzen dadurch aus, daß es eine Art von Lerntechnik nutzt. Es wird auf der gegebe-nen Datenbasis trainiert und ermittelt somit eine Abbildung zwischen gegebenen Ontologien automatisch. Der SMART-Algorithmus arbeitet hingegen semi-automatisch mit Benut-zerinteraktionen. Er bietet Vorschï age an und läßt diese va-lidieren bevor sie umgesetzt werden.
Purpose In this document, we give a high-level overview of selected Semantic (Web) technologies, methods, and other important considerations, that are relevant for the success of EarthCube. The goal of this initial document is to provide entry points and references for discussions between the Semantic Technologies experts and the domain experts within EarthCube. The selected topics are intended to ground the EarthCube roadmap in the state of the art in semantics research and ontology engineering. We anticipate that this document will evolve as EarthCube progresses. Indeed, all EarthCube parties are asked to provide topics of importance that should be treated in future versions of this document.
The deliverable D1.2.3 analyses and evaluates existing methods for ontology content evaluation according requirements from an industrial point of view.
Conceptual analysis and knowledge representation often require to develop an ontological support. The activity of developing a domain ontology is therefore one of the fundamental steps to be carried out when developing a shared model of the knowl-edge possessed by an organization, and consequently, one of the pilasters of knowledge management. It is clear, however, that this activity, as well as all other engineering activities, is constrained to be methodologically coherent in its phases, because the control over correctness of the development method is the only possible way to keep events of such an activity on its own track. Though it would seem attractive to deploy software applications which provide automation to the development of such models, the results of automatic ontology tools in the real practice of knowledge management are definitely disappointing. We thus would prefer to look at formalized methodologies, and in particular, we are interested in methodologies which work from scratch, namely which aim at providing the ontological structure without any general and supposedly shared reference model in front. That choice is not the ideal one, since in general, scalability is a very desirable property, but since the two problems of developing from scratch and integrating existing parts are deeply different we have to commit to one focus.
The effective generation, sharing and re-use of innovation knowledge play an increasingly crucial role in the competitiveness
of industrial enterprises allowing the companies to offer new solutions based on rapidly exploited innovations. As the innovation
knowledge is by its nature very unstructured and manifold in its definition, solutions for effective exchange and sharing
of such knowledge within collaboration networks of extended enterprises are still missing. This paper presents a set of new
methods and IT tools to support efficient and smooth exchange of innovation knowledge in extended and networked enterprises.
The methods and tools are based on so-called hybrid approach in definition of ontologies needed as a basis for knowledge sharing
within a network of companies. The developed tools enable efficient set-up, harmonisation and maintenance of generic, shared
ontologies. The solutions are tested within a large manufacturing enterprise involving several subsidiaries.
The World-Wide Web provides an ever-increasing source of diverse information. We focus on query agents, in particular the matching and negotiation agents that are responsible for pre-integration where the matching agent decomposes the query into sub-queries, and then searches metadata to find datasets that match the query fragments. In the case of heterogeneous data, the matching agent utilises a negotiation agent to find datasets that match the query fragments, provides mappings from the data to the query, and constructs the appropriate (sub-)query re-writing rules. Such matching is done by generalising the data and testing if the (sub) query is matchable to the generalised (meta) data: we call this g-matchable; if it is then we can construct an operator stack to transform the data to match the (sub) query. Such an approach provides a capability of automating the process of executing queries on heterogeneous statistical databases that are distributed over the Internet. The novelty lies in the provision of automated methods for statistical aggregates, where the heterogeneity essentially resides in the classification schemes of categorical data, including both heterogeneity of nomenclature and heterogeneity of granularity. In addition, our solution permits queries to be specified in a goal-driven query-by-example format. Rather than impose an a priori global standard, the user can query through a unified interface where integration is done at run-time.
The paper proposes the approach to cope with the maintenance of dy- namically changing resource ontologies of autonomously maintained, distributed, heterogeneous, wrapped information resources and their mappings to common me- diator IS ontology. The approach intends to do the work in economical way reduc- ing efforts to matching and aligning only modified ontology elements. Proposed is ontology model comprising both descriptive part and the set of modification primi- tives for each ontology structural element. The set of ontology modification invari- ants and the corresponding set of modification conflicts resolution rules are formu- lated for taxonomies. The way to provide IS services for ontology changes moni- toring, matching and alignment is outlined.
The rapid growth in the number of ontologies has not met with the wide adoption of ontology in practice. Ontology evaluation can promote ontology use by facilitating the selection of a good ontology. Despite that a host of ontology evaluation methodologies are available, many of them are fragmentary and strongly tied to ontology development methodologies. Based on a review of extant ontology evaluation methods, we propose a framework for ontology evaluation. The framework provides a holistic view of ontology evaluation, suggesting both fundamental ontology dimensions and concrete criteria.
Human categorization is neither a binary nor a context-free process. Rather, the criteria that govern the use and recognition of certain concepts may be satisfied to different degrees in different contexts. In light of this reality, the idealized, static structure of a lexical-ontology like WordNet appears both excessively rigid and unduly fragile when faced with real texts that draw upon different contexts to communicate different world-views. In this paper we describe a syntagmatic, corpus-based approach to redefining the concepts of a lexical-ontology like WordNet in a functional, gradable and context-sensitive fashion. We describe how the most diagnostic properties of concepts, on which these functional definitions are based, can be automatically acquired from the Web, and demonstrate how these properties are more predictive of how concepts are actually used and perceived than properties derived from other sources (such as WordNet itself).
an abstract entity dened,to refer to a physical region (extent) in space and categorized (typed) according to commonly,agreed upon characteristics. Place is a social concept of interest for a particular community during a certain time span. Its name is a symbol used for communication. Categorization is a central cognitive process. This paper focuses on two reasons for the categorization of places: communication and cognition. Categorizing into types improves communication about places with which at least one communication partner is unfamiliar, for example when giving directions such as \follow the path along the river up to the bridge, then turn right towards the market place". Moreover, typing is the key to prediction, reasoning and decision making which all require an abstraction from entity to type level. What humans experience as a place is, in fact, the set of perceivable characteristics of the region in
Computer-based systems that support health care require large controlled terminologies to manage names and meanings of data elements. These terminologies are not static, because change in health care is inevitable. To share data and applications in health care, we need standards not only for terminologies and concept representation, but also for representing change. To develop a principled approach to managing change, we analyze the requirements of controlled medical terminologies and consider features that frame knowledge-representation systems have to offer. Based on our analysis, we present a concept model, a set of change operations, and a change-documentation model that may be appropriate for controlled terminologies in health care. We are currently implementing our modeling approach within a computational architecture.
As researchers in the ontology-design field develop the content of a growing number of ontologies, the need for sharing and reusing this body of knowledge becomes increasingly critical. Aligning and merging existing ontologies, which is usually handled manually, often constitutes a large and tedious portion of the sharing process. We have developed SMART, an algorithm that provides a semi-automatic approach to ontology merging and alignment. SMART assists the ontology developer by performing certain tasks automatically and by guiding the developer to other tasks for which his intervention is required. SMART also determines possible inconsistencies in the state of the ontology that may result from the user's actions, and suggests ways to remedy these inconsistencies. We define the set of basic operations that are performed during merging and alignment of ontologies, and determine the effects that invocation of each of these operations has on the process. SMART is based on an extremely general knowledge model and, therefore, can be applied across various platforms.