Semantic Web Environment Research Articles

Requirements Engineering and Semantic Web*

The author discusses modern methods of requirements engineering oriented on human communication and proposes the way for their transformation into semantic annotation of these requirements, suitable for manipulation in the Semantic Web environment.

A Resourceful Framework Using Combined Agent Ontology Mapping for Semantic Web

The combined agent ontology mappings assure that more and more people will start using ontology. The expectations are also high when one thinks about the potential use of these applications. Ontology mapping plays a vital role in achieving heterogeneous data integration on the Semantic Web. This enables a large number of ontology present on the web need to be aligned before one can make use of them. But this ontology can differ in representation, quality, and size that pose different challenges to ontology mapping. The main purpose of implementing this multi agent ontology mapping framework was that operate effectively in the Semantic Webenvironment. The aim is to build a framework that solves the difficulty of evaluating ontologies with a large number of concepts. Here a number of domain experts are necessary to evaluate similar concepts in different ontologies. The experts combine their knowledge and experience to create a solution rather than relying on a single person perspective. In this the classes are represented as RDF individuals where the individual properties are defined as OWL data properties. Here both ontologies are valid separately, and no logical reasoned would find inconsistency in them individually. It is easy to see that, once we compare the two ontologies, a considerable amount of uncertainty arises over the classes and its properties and in a way they can be compared. This uncertainty can be contributed to the fact that, owing to the different representations, certain elements will be missing for the comparison.

An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storage

E-learning and online education have made great strides in the recent past. It has moved from a knowledge transfer model to a highly intellect, swift and interactive proposition capable of advanced decision-making abilities. Two challenges have been observed during the exploration of recent developments in e-learning. Firstly, to incorporate e-learning systems effectively in the evolving semantic web environment and secondly, to realize adaptive personalization according to the learner's changing behavior. An ontology-driven system has proposed to implement the Felder-Silverman learning style model in addition to the learning contents, to validate its integration with the semantic web environment. Software agents are employed to monitor the learner's actual learning style and modify them accordingly. The learner's learning style and their modifications are made within the proposed e-learning system. Cloud storage is used as the primary back-end in order to maintain the ontology, databases and other required server resources. To verify the system, comparisons are made between the information presented and adaptive learning styles of the learner along with actions of agents according to learners' behavior. Finally, various conclusions are drawn by exploring the learner's behavior in an adaptive environment for the proposed e-learning system.

Ontologies in engineering: the OntoDB/OntoQL platform

Ontologies have been increasingly used over the past few decades in a wide range of application domains spanning both academic and industrial communities. As ontologies are the cornerstone of the Semantic Web, the technologies developed in this context, including ontology languages, specialized databases and query languages, have become widely used. However, the expressiveness of the proposed ontology languages does not always cover the needs of specific domains. For instance, engineering is a domain for which the LIAS laboratory has proposed dedicated solutions with a worldwide recognition. The underlying assumptions made in the context of the Semantic Web, an open and distributed environment, do not apply to the controlled environments of our projects where the correctness and completeness of modeling can be guaranteed to a certain degree. As a consequence, we have developed over the last decades a specialized standard ontology language named PLIB associated with the OntoDB/OntoQL platform to manage ontological engineering data within a database. The goal of this paper is threefold: (1) to share our experience in manipulating ontologies in the engineering domain by describing their specificities and constraints; (2) to define a comprehensive classification of ontologies with respect to three main research communities: Artificial Intelligence, Databases and Natural Language Processing and (3) to present a persistent solution, called OntoDB, for managing extremely large semantic data sets associated with an ontological query language, called OntoQL. These objectives are illustrated by several examples that show the effectiveness and interest of our propositions in several industrial projects in different domains including vehicle manufacturing and CO $$_2$$ storage.

ISBD as Bibliographic Content Standard: Interweaving Threads, Contemplating a Future

This summary considers the final articles selected for the special issue, interweaving some common threads that bind them together in their consideration of the International Standard Bibliographic Description (ISBD), consolidated edition, 2011, as a bibliographic content standard. With discussions of (1) the historical and current evolution of the ISBD, (2) the concept of Universal Bibliographic Control relative to an emerging Semantic Web environment, (3) ISBD and national cataloging codes, and (4) the continuing challenges of “non-book” resources, as context, the article concludes by exploring the question, “Is there a future for the ISBD?”

의미웹 환경에서 조건부함축 제약 지식표현을 위한 SWCL의 확장

By the publications of RDF and OWL, the Semantic Web is confirm ed as a technology through which information in the Internet can be processed by machines. The focus of the Semantic Web study after then has moved to how to provide more useful information to users for their decision making beyond simple use of the structured data in ontologies. SWRL that makes logical inference possible by rules , and SWCL that formulates constraints under the Semantic Web environment are some of many efforts toward the ac hievement of that goal. Constraint represents a connection or a relationship between individual data in ontology. Based on SWCL, this paper tries to extend the lan-guage by adding one more type of constraint, implication consta int, in its repertoire. When users use binary variables to represent logical relationships in mathematical models, it r equires and knowledge on the solver to solve the models. The use of implication constraint ease this difficulty. Its nee d, definition and relevant technical description is presented by the use of the optimal common attribute selection problem in product design.

The Formal Semantics of a Domain-Specific Modeling Language for Semantic Web Enabled Multi-Agent Systems

Development of agent systems is without question a complex task when autonomous, reactive and proactive characteristics of agents are considered. Furthermore, internal agent behavior model and interaction within the agent organizations become even more complex and hard to implement when new requirements and interactions for new agent environments such as the Semantic Web are taken into account. We believe that the use of both domain specific modeling and a Domain-specific Modeling Language (DSML) may provide the required abstraction and support a more fruitful methodology for the development of Multi-agent Systems (MASs) especially when they are working on the Semantic Web environment. Although syntax definition based on a metamodel is an essential part of a modeling language, an additional and required part would be the determination and implementation of DSML constraints that constitute the (formal) semantics which cannot be defined solely with a metamodel. Hence, in this paper, formal semantics of a MAS DSML called Semantic Web enabled Multi-agent Systems (SEA_ML) is introduced. SEA_ML is a modeling language for agent systems that specifically takes into account the interactions of semantic web agents with semantic web services. What is more, SEA_ML also supports the modeling of semantic agents from their internals to MAS perspective. Based on the defined abstract and concrete syntax definitions, we first give the formal representation of SEA_ML's semantics and then discuss its use on MAS validation. In order to define and implement semantics of SEA_ML, we employ Alloy language which is declarative and has a strong description capability originating from both relational and first-order logic in order to easily define complex structures and behaviors of these systems. Differentiating from similar contributions of other researchers on formal semantics definition for MAS development languages, SEA_ML's semantics, presented in this paper, defines both static and dynamic aspects of the interaction between software agents and semantic web services, in addition to the definition of the semantics already required for agent internals and MAS communication. Implementation with Alloy makes definition of SEA_ML's semantics to include relations and sets with a simple notation for MAS model definitions. We discuss how the automatic analysis and hence checking of SEA_ML models can be realized with the defined semantics. Design of an agent-based electronic barter system is exemplified in order to give some flavor of the use of SEA_ML's formal semantics. Lessons learned during the development of such a MAS DSML semantics are also reported in this paper.

A semantic similarity measure based on information distance for ontology alignment

Ontology alignment is the key point to reach interoperability over ontologies. In semantic web environment, ontologies are usually distributed and heterogeneous and thus it is necessary to find the alignment between them before processing across them. Many efforts have been conducted to automate the alignment by discovering the correspondence between entities of ontologies. However, some problems are still obvious, and the most crucial one is that it is almost impossible to extract semantic meaning of a lexical label that denotes the entity by traditional methods. In this paper, ontology alignment is formalized as a problem of information distance metric. In this way, discovery of optimal alignment is cast as finding out the correspondences with minimal information distance. We demonstrate a novel measure named link weight that uses semantic characteristics of two entities and Google page count to calculate an information distance similarity between them. The experimental results show that our method is able to create alignments between different lexical entities that denotes the same ones. These results outperform the typical ontology alignment methods like PROMPT (Noy and Musen, 2000) [38], QOM (Ehrig and Staab, 2004) [12], and APFEL (Ehrig et al., 2005) [13] in terms of semantic precision and recall.

An automated tool for semantic accessing to formal software models

Many software engineers have found that it is difficult to understand, incorporate and use different formal models consistently in the process of software developments, especially for large and complex software systems. This is mainly due to the complex mathematical nature of the formal methods and the lack of tool support. It is highly desirable to have software models and their related software artefacts systematically connected and used collaboratively, rather than in isolation. The success of the Semantic Web, as the next generation of Web technology, can have profound impact on the environment for formal software development. It allows both the software engineers and machines to understand the content of formal models and supports more effective software design in terms of understanding, sharing and reusing in a distributed manner. To realise the full potential of the Semantic Web in formal software development, effectively creating proper semantic metadata for formal software models and their related software artefacts is crucial. This paper proposed a framework that allows users to interconnect the knowledge about formal software models and other related documents using the semantic technology. We first propose a methodology with tool support is proposed to automatically derive ontological metadata from formal software models and semantically describe them. We then develop a Semantic Web environment for representing and sharing formal Z/OZ models. A method with prototype tool is presented to enhance semantic query to software models and other artefacts.

Dynamic faceted navigation in decision making using Semantic Web technology

Categorization in the decision making classifies decision makers' experiences about the world and provides a guide to reach a goal. This implies that dynamically providing categories reflecting the given decision context gives a great enhancement in decision quality. This study discusses the dynamic category selection under the Semantic Web environment, focusing on an implementation of a decision support system, the dynamic facet navigation system working with an ontology. Predefined fixed categories are provided to refine search results to evade use of complex queries and tedious review of search results, but they often output insensible information because of never reflecting the difference in search results. This paper proposes a dynamic category selection mechanism by using the total gain ratio under a given ontology, and a reordering scheme for resulted categories. It proves the validity of the proposed approach with a statistical analysis lastly.

Event extraction from corefering mentions to enrich semantic web metadata

Since an enormous amount of information is available on the web and much time is needed to find and read the desired information, users require precise information from the documents. More specifically, most users are interested in precise information about events occurring in different parts of the world, such as when and where the event has occurred and who was involved in the event. The task of finding such information is called event extraction. Event extraction from text documents is an important task in the field of natural language processing, as it helps improve information retrieval and summarisation, as well as enriching the semantic web with event-based metadata. This paper provides a method to extract event arguments from corefering sentences of a particular event instance using rule learning based on rough sets. It also provides a metadata adoption strategy to exploit the results in the semantic web environment as metadata.

Automation of the semantic annotation of web resources

The annotation of a web page allows us to associate a semantic to the content of this page. But with the great mass of pages managed through the world, and especially with the advent of the web, their manual annotation is not feasible. In this paper, we will focus on the semiautomatic annotation of the web pages. We will propose an approach and a component (entitled 'Querying Web') for semantic annotation of web pages. The proposal of this component aims at improving the interrogation process in the semantic web environment. Our solution is an enhancement of the first result of annotation done by the 'Semantic Radar' plug-in on the web resources, by new annotations using an enriched domain ontology. Finally, we will present in this paper an evaluation of the automation made by our component.

On the use of a domain-specific modeling language in the development of multiagent systems

The study of Multiagent Systems (MASs) focuses on those systems in which many intelligent agents interact with each other. The agents are considered to be autonomous entities which contain intelligence that serves for solving their selfish or common problems, and to achieve certain goals. However, the autonomous, responsive, and proactive natures of agents make the development of agent-based software systems more complex than other software systems. Furthermore, the design and implementation of a MAS may become even more complex and difficult to implement when considering new requirements and interactions for new agent environments like the Semantic Web. We believe that both domain-specific modeling and the use of a domain-specific modeling language (DSML) may provide the required abstraction, and hence support a more fruitful methodology for the development of MASs. In this paper, we first introduce a DSML for MASs called SEA_ML with both its syntax and semantics definitions and then show how the language and its graphical tools can be used during model-driven development of real MASs. In addition to the classical viewpoints of a MAS, the proposed DSML includes new viewpoints which specifically support the development of software agents working within the Semantic Web environment. The methodology proposed for the MAS development based on SEA_ML is also discussed including its example application on the development of an agent-based stock exchange system.

Automatic Ontology Generation for Musical Instruments Based on Audio Analysis

In this paper we present a novel hybrid system that involves a formal method of automatic ontology generation for web-based audio signal processing applications. An ontology is seen as a knowledge management structure that represents domain knowledge in a machine interpretable format. It describes concepts and relationships within a particular domain, in our case, the domain of musical instruments. However, the different tasks of ontology engineering including manual annotation, hierarchical structuring and organization of data can be laborious and challenging. For these reasons, we investigate how the process of creating ontologies can be made less dependent on human supervision by exploring concept analysis techniques in a Semantic Web environment. In this study, various musical instruments, from wind to string families, are classified using timbre features extracted from audio. To obtain models of the analysed instrument recordings, we use K-means clustering to determine an optimised codebook of Line Spectral Frequencies (LSFs), or Mel-frequency Cepstral Coefficients (MFCCs). Two classification techniques based on Multi-Layer Perceptron (MLP) neural network and Support Vector Machines (SVM) were tested. Then, Formal Concept Analysis (FCA) is used to automatically build the hierarchical structure of musical instrument ontologies. Finally, the generated ontologies are expressed using the Ontology Web Language (OWL). System performance was evaluated under natural recording conditions using databases of isolated notes and melodic phrases. Analysis of Variance (ANOVA) were conducted with the feature and classifier attributes as independent variables and the musical instrument recognition F-measure as dependent variable. Based on these statistical analyses, a detailed comparison between musical instrument recognition models is made to investigate their effects on the automatic ontology generation system. The proposed system is general and also applicable to other research fields that are related to ontologies and the Semantic Web.

Isosemantic rendering of clinical information using formal ontologies and RDF.

The generation of a semantic clinical infostructure requires linking ontologies, clinical models and terminologies [1]. Here we describe an approach that would permit data coming from different sources and represented in different standards to be queried in a homogeneous and integrated way. Our assumption is that data providers should be able to agree and share the meaning of the data they want to exchange and to exploit. We will describe how Clinical Element Model (CEM) and OpenEHR datasets can be jointly exploited in Semantic Web environments.

A Semantic Query Method for Deep Web

Based on the idea of "functionality-centric", this paper proposes a complete set of oriented semantic query methods for Deep Web, builds up the relevant software architecture, provides a new method for full use of Deep Web data resources in semantic web environment through describing the establishment of the semantic environment, re-writing the SPARQL-to-SQL query, semantic packaging of semantic query result, and the architecture of semantic query services.

ISBD adaptation to semantic web of bibliographic data in linked data

Linked data is the current paradigm. All works, projects and applications have it as main tool, because of its potentiality. In linking data, the selected information, authorized, validated, recorded and structured in their databases for long time by cultural institutions as libraries, necessarily is going to play an important role. The work carried out by IFLA ISBD Review Group has had this goal in mind, to make possible that the information in our databases will be in the cloud, that is, “enhance the portability of bibliographic data in the semantic web environment and the interoperability of the ISBD with other content standards” as said in the Purpose of Consolidated ISBD, 2010. Many voices have spoken about the obscure language for users that represent the library language. Linked data can help in making it understandable. To reach this big objective much more, work than converting information in linked data is necessary. Declarations in RDF, definitions, and translations are essential to make really multilingual understanding, not only English semantic web.

Dewey linked data: Making connections with old friends and new acquaintances

This paper explores the history, uses cases, and future plans associated with availability of the Dewey Decimal Classification (DDC) system as linked data. Parts of DDC system have been available as linked data since 2009. Initial efforts included the DDC Summaries in eleven languages exposed as linked data in dewey.info. In 2010, the content of dewey.info was further extended by the addition of assignable numbers and captions from the Abridged Edition 14 data files in English, Italian, and Vietnamese. During 2012, we will add assignable numbers and captions from the latest full edition database, DDC 23. In addition to the “old friends” of different Dewey language versions, institutions such as the British Library and Deutsche Nationalbibliothek have made use of Dewey linked data in bibliographic records and authority files, and AGROVOC has linked to our data at a general level. We expect to extend our linked data network shortly to “new acquaintances” such as GeoNames, ISO 639-3 language codes, and Mathematics Subject Classification. In particular, the paper examines the linking process to GeoNames as an example of cross-domain vocabulary alignment. In addition to linking plans, the paper reports on use cases that facilitate machine-assisted categorization and support discovery in the semantic web environment.

A DSL for the development of software agents working within a semantic web environment

Software agents became popular in the development of complex software systems, especially those requiring autonomous and proactive behavior. Agents interact with each other within a Multi-agent System (MAS), in order to perform certain defined tasks in a collaborative and/or selfish manner. However, the autonomous, proactive and interactive structure of MAS causes difficulties when developing such software systems. It is within this context, that the use of a Domain-specific Language (DSL) may support easier and quicker MAS development methodology. The impact of such DSL usage could be clearer when considering the development of MASs, especially those working on new challenging environments like the Semantic Web. Hence, this paper introduces a new DSL for Semantic Web enabled MASs. This new DSL is called Semantic web Enabled Agent Language (SEA_L). Both the SEA_L user-aspects and the way of implementing SEA_L are discussed in the paper. The practical use of SEA_L is also demonstrated using a case study which considers the modeling of a multi-agent based e-barter system. When considering the language implementation, we first discuss the syntax of SEA_L and we show how the specifications of SEA_L can be utilized during the code generation of real MAS implementations. The syntax of SEA_L is supported by textual modeling toolkits developed with Xtext. Code generation for the instance models are supplied with the Xpand tool.

연구 개발 트렌드 분석을 위한 기술 지식 온톨로지 구축

과학기술 분야 연구자들은 이전 연구와 개발 결과에 대한 조사 연구에 많은 시간을 소비한다. 또한, 연구자들은 유리한 입지를 성공적으로 차지하기 위해 일반적으로 학술 논문, 특허, 최근 연구 동향에 대한 웹 문서 등의 다양한 학술 자원을 분석하여 새롭게 등장하는 연구 주제를 선점하려고 한다. 하지만 키워드 기반의 정보 검색이나 참고문헌 정보에 근거한 연관 문서 추출 방법을 사용해서는 방대한 문헌에서 투자 가능한 연구 주제를 효율적으로 찾는 일이 쉽지 않다. 본 논문에서는 대규모 기술 문헌 자료에서 추출되는 기술, 제품, 연구 주체 간의 의미론적으로 연결된 정보를 효율적으로 생성, 저장하고 활용할 수 있는 방법을 제안한다. 세부적으로 텍스트 마이닝 기술을 활용하여 문헌에서 나타나는 주요 개체들과 연관 관계를 추출하여 시맨틱 웹 환경에 적용 가능한 기술 지식으로 생성하는데 적합한 온톨로지를 구축한다. 이렇게 구축된 온톨로지는 연관 관계를 가진 기술 지식 탐색을 지원하기에 연구 개발 트렌드 예측 및 분석 서비스인 InSciTe Adaptive에 사용되었다. Researchers and scientists spend huge amount of time in analyzing the previous studies and their results. In order to timely take the advantageous position, they usually analyze various resources such as paper, patents, and Web documents on recent research issues to preoccupy newly emerging technologies. However, it is difficult to select invest-worthy research fields out of huge corpus by using the traditional information search based on keywords and bibliographic information. In this paper, we propose a method for efficient creation, storage, and utilization of semantically relevant information among technologies, products and research agents extracted from 'big data' by using text mining. In order to implement the proposed method, we designed an ontology that creates technological knowledge for semantic web environment based on the relationships extracted by text mining techniques. The ontology was utilized for InSciTe Adaptive, a R&D trends analysis and forecast service which supports the search for the relevant technological knowledge.