Top PDF Some reflections on knowledge representation in the semantic web

Some reflections on knowledge representation in the semantic web

Some reflections on knowledge representation in the semantic web

For me a key insight of the PEN&PAD project was that a fundamental aspect of the design of a system involves developing a representation of data or knowledge that intuitively corresponds to the understanding of users. For PEN&PAD this meant the development of a system of knowledge representation for symptoms, signs and diseases necessary for clinical data entry by general medical practitioners. In my experience, user interface design, even if it is firmly focused on supporting user tasks, depends to a large extent on how well the underlying representation of knowledge or data fits with the understanding of users. So an approach to the representation of knowledge that in some way corresponds to the way users think seemed to be a significant step in the direction of real user centred system design. User centred design was not just a matter of paying attention to the surface appearance in the form of good interface design but also extended to the deep structures where knowledge representation and data in some way matched or was intuitive to the user.
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Semantic Web Domain Knowledge Representation Using Software Engineering Modeling Technique

Semantic Web Domain Knowledge Representation Using Software Engineering Modeling Technique

A markup language is a tool for adding information to the documents. Semantic markup is expected to have universal expressive power, syntactic and semant ic interoperability. The XML (eXtensible Markup Language) does not provide semantic interoperability. Using XML as a base , a number of new markup languages have been deve loped to meet semantic interoperability, these are RDF(Resource Description Framework), RDFS, SHOE, DAML+OIL and recently OWL (Daconta Michael, 2003). The work done earlier in domain knowledge representation using software engineering modeling techniques was concentrated towards developing the mapping of the software engineering models to the RDF, RDFS and other markup languages (Cranefield, 2001). At present the researchers are trying to map the domain models to the OWL representation (Djuric, Gasevic & Devedzic, 2004).
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Indian Music Instruments Semantic Knowledge Representation

Indian Music Instruments Semantic Knowledge Representation

Semantic Web is an intelligent incarnation and advancement in World Wide Web to store, annotate, index and retrieve information by providing knowledge representation, classification, and categorization, a common understanding among the group of people and structuring the information in machine processable format. To structure the information in machine processable semantic models Semantic Web have introduced the concept of ontology. Ontologies are most widely used in artificial intelligence, semantic web, software engineering, information retrieval, knowledge representation, knowledge sharing, knowledge integration, knowledge reuse, and so on. Music plays a vital role in human life. A vast number of musical instruments are used to produce rhythmic sounds. The rapid growth of technology enables on line music. The instruments used and the terms used to describe the concepts varies from individual groups. To support concept based knowledge representation and semantic retrieval music files music instruments knowledge should be identified and represented. This raises the need for construction of Indian music instruments ontology. This ropes semantic web with more intelligent, capable, relevant, responsive interaction and high performance retrieval system. The Indian music instrument ontology is been constructed and the paper concludes with conclusion and future work.
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Research on construction of natural language processing system based on semantic web ontology

Research on construction of natural language processing system based on semantic web ontology

Some people are committed to study to indicate the likelihood of RDF data. However, it is the final conclusion of these studies and to fully consider the scale problem. One of the study's proposed RDF data storage based on path, but this method based on path eventually in the relational database based on: it is the sub graph stored in different relational tables. Therefore, such systems cannot provide massive RDF data query scale. Others focus on the measure of semantic similarity network internal and estimation method is used to selectively to optimize the RDF data query; these methods based on memory map implementation, there are still limitations on the scope of it. As the language definition is currently a very active research field in network based knowledge representation, there are many proposals and new standards. The most important of which are RDF mode and DAML+OIL (recently re defined as OWL), the latter defined on the former one. In addition, there are XML model and Topic Maps is sometimes seen as a knowledge representation language.
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RKB: a Semantic Web knowledge base for RNA

RKB: a Semantic Web knowledge base for RNA

Increasingly sophisticated knowledge about RNA structure and function requires an inclusive knowledge representation that facilitates the integration of independently – generated information arising from such efforts as genome sequencing projects, microarray analyses, structure determination and RNA SELEX experiments. While RNAML, an XML-based representation, has been proposed as an exchange format for a select subset of information, it lacks domain-specific semantics that are essential for answering questions that require expert knowledge. Here, we describe an RNA knowledge base (RKB) for structure-based knowledge using RDF/OWL Semantic Web technologies. RKB extends a number of ontologies and contains basic terminology for nucleic acid composition along with context/model-specific structural features such as sugar conformations, base pairings and base stackings. RKB (available at http://semanticscience.org/projects/rkb) is populated with PDB entries and MC-Anno- tate structural annotation. We show queries to the RKB using description logic rea- soning, thus opening the door to question answering over independently-published RNA knowledge using Semantic Web technologies.
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ONTOLOGY BASED SEMANTIC KNOWLEDGE REPRESENTATION FOR SOFTWARE RISK MANAGEMENT

ONTOLOGY BASED SEMANTIC KNOWLEDGE REPRESENTATION FOR SOFTWARE RISK MANAGEMENT

Then the knowledge available in SRMONTO is represented using document Semantic descriptions [Toma´sˇ Vitvar et. al, 2010] must usually co-exist with other already existing descriptions. Semantics enrich existing descriptions with additional expressivity that systems can use for advanced content manipulation and provisioning. Semantic description usually refers to a description of a resource, e.g., service, message, data, and alike expressed in a semantic language, that is, in a language that allows formal definition of semantic information (e.g., classes of concepts, relations between classes, axioms, etc.), while at the same time some logical foundations for the language exist. For example, every description in RDFS[Beckett], OWL[Michael K et. al, 2004], RIF,WSML is the semantic description. On the other hand, non-semantic description is a description of a resource, e.g., service, message, data, and alike which is captured in a language that does not allow expression of semantic information. In this respect, any description in XML, XML Schema, or any other proprietary format is the non-semantic description. Please note, that in the IT world, there might be different views on what semantics are about. People might call semantics a description of data in XML Schema with attributes’ types, restriction on values, etc. However, XML Schema does not comply with our semantic description definition as it does not allow expression of classes, their properties, nor relationships between classes, while it does not have any logical foundation. In addition, the XML is often used as a serialization format of semantic descriptions, for example, a description captured in RDFS may be formatted in XML (such serialization is called RDF/XML). Thus, XML is usually understood as the language capturing the syntax. The semantic knowledge representation definition we use is based on the Semantic Web point of view.
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Ontology based Semantic Search Engine for Cancer

Ontology based Semantic Search Engine for Cancer

RDF is a metadata language that does not provide special vocabulary for describing the resources. It is often essential to be able to describe more of a subject than saying it is a resource. Some form of classification for these resources is often required to be able to be able to provide a more precise and correct mapping of the world. The basic idea behind Semantic Web is to provide meaning of resources, as defined in the Knowledge Representation domain, "knowledge is descriptive and can be expressed in a declarative form" [16]. The formalization of knowledge in declarative form begins with a conceptualization. This formalization includes the objects presumed or hypothesized to exist in the world. This is why RDF schema (RDFS) was introduced as a language that provides formal conceptualization of the world. RDF Schema semantically extends RDF to enable us to talk about classes of resources, and the properties that will be used with them. The RDF schema defines the terms that will be used in RDF statements and gives specific meanings to them. It provides mechanisms for describing groups of related resources and the relationships between these resources. Meaning in RDF is expressed through reference to the schema. RDFS consists of a collection of RDF resources that can be used to describe properties of other RDF resources this makes it a simple ontology language which allows more capture of semantics than just pure RDF. The most important resources described in RDFS are:
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Leveraging Web Semantic Knowledge in Word Representation Learning

Leveraging Web Semantic Knowledge in Word Representation Learning

Distributed word representations boost performance of many NLP applications mainly because they are capable of capturing semantic regularities from a collection of text se- quences. Much research work tries to enhance word repre- sentation learning (WRL) from the semantic perspective by leveraging semantic lexicons. Semantic lexicons can be con- sidered as a collection of lists, in which each list consists of semantically related words. Some existing work pulls the vectors of synonyms close by either a post-processing model (Faruqui et al. 2015) or a joint representation learn- ing model with the distances between synonyms as regular- izers (Kiela, Hill, and Clark 2015; Bollegala et al. 2016). More recently, many manually well-designed semantic rela- tions or linguist structures, such as synonyms and antonyms (Mrkˇsi´c et al. 2017; Glavaˇs and Vuli´c 2018), concept con- vergence and word divergence (Liu et al. 2018), have been used to enhance the semantics of words.
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Knowledge Representation and Reasoning on the Web of Data

Knowledge Representation and Reasoning on the Web of Data

Finding the right information at the right time and in the right format becomes increasingly difficult as more information from myriad producers is made available to increasingly diverse communities of information consumers. The domain of eGovernment is unique because of its enormous challenge to achieve interoperability, given the manifold semantic differences of interpretation. Setting up seamless e- government services requires information integration as well as process integration involving a variety of objects with specific semantics.
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Publishing Knowledge and Transformation of Conventional Web into Semantic Web using Metadata

Publishing Knowledge and Transformation of Conventional Web into Semantic Web using Metadata

Flexible information Access using Metadata in Novel Combinations (FLAMENCO) is a research supported by a CAREER grant awarded to Prof. Marti Hearst from the National Science Foundation, NSF IIS-9984741 [13]. Flamenco provides a search interface framework that has a primary design goal of user to move through large information spaces with maximum flexibility of navigation and control. The interface uses hierarchical faceted metadata with explicit exposure of category to guide the user toward possible choices and to organize the results of keyword searches. The interface uses metadata in a manner that allows users to both refine and expand the current query, while maintaining a consistent representation of the collection's structure. This use of metadata is integrated with free-text search, allowing the user to follow links, and then add search terms, then again follow more links without interrupting the interaction flow.
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Managing Semantic Metadata for Web Grid Services

Managing Semantic Metadata for Web Grid Services

The way that current service-oriented infrastructure handles and manages services’ metadata is not adequate and effective for metadata to help services discovery and knowledge sharing. First, there is no enough metadata about Web/Grid services. Services, in particular, legacy resources, are developed by service providers for their own use, without realising the role and importance of metadata this naturally leads to the lack of descriptive information for services. Second, metadata are unstructured. Web/Grid services are diverse; the types of metadata required for describing ser- vices in e-Science (Hey & Trefethen, 2003) vary greatly between individuals, organisations, and scientific communities. The use of different ter- minologies and the adoption of various metadata models such as using comments or annotations as metadata are inevitable. Unsurprisingly, this causes the problem of mutual understanding and service interoperability. Third, metadata lack semantics. XML- (www.w3.org/XML) based metadata modeling and representation as in WSDL and UDDI are incapable of capturing genuine semantics, relationships, or constraints. There are no problems for humans to understand XML-based metadata as described in the above photo example because we know the meaning of these English words. The question is: “can
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Web Page Prediction System Based on Web Logs and Web Domain Using Cluster Technique

Web Page Prediction System Based on Web Logs and Web Domain Using Cluster Technique

ABSTRACT: In intelligent web system web recommendation plays important role. In web mining, for web recommendation system the knowledge discovery and representation of information is an important and crucial task. Here in this paper new method is introduce to efficiently provide better Web-page recommendation generations through semantic-enhancement by integrating the domain and Web usage knowledge of a website. By the help of knowledge discovery user profile is created to block suspicious user that are harmful for websites or server. This model uses semantic web network to represent relations between domain, Web-pages & websites. Other model, the conceptual model, is proposed to auto generate a semantic web network of the semantic Web usage knowledge, which is the integrated with domain knowledge and Web usage knowledge.
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Semantic Web Representation in RDFSchema: A Practical Approach

Semantic Web Representation in RDFSchema: A Practical Approach

OWL has a rich vocabulary for capturing knowledge and also basis for describing metadata. In addtion it also includes the features torepresent cardinality, localised range and domain constraints and characteristic of properties such as transitive , inverse and symmetrical. OWL provides OWL Full, OWL DL and OWL Lite as its sublanguages. OWL Lite is less expressive but easier to implement and provides more terms for describing relationships. OWL DL and OWL Full have same vocabulary but OWL DL has some restrictions like type separtions and property type.

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 KNOWLEDGE EXTRACTION METHOD USING STOCHASTIC APPROACHES IN GOOGLE MAPS

 KNOWLEDGE EXTRACTION METHOD USING STOCHASTIC APPROACHES IN GOOGLE MAPS

time complexity [10]. A framework IRS-III (Internet Reasoning System) is used for creation and running of Semantic Web Services, which takes a semantic broker-based approach for the mediation between service requesters and service providers [11]. Web service composition system can help us to automate the business process, from specifying functionalities, to develop the executable workflows that capture non-functional requirements to deploy them on a runtime infrastructure [12]. Aviv Segev et al. [3] proposed a context-based semantic approach to the problem of matching and ranking of web services for possible service composition and provided a numeric estimation about the possible composition to the designer. An OWL-S service profile ontology based framework is used, for the retrieval of web services based on subsumption relation and structural case-based reasoning, which performs domain-dependant discovery [4]. Tamer Ahmed Farrag et al. [6] proposed a mapping algorithm that helps to facilitate the integration of the current conventional web services into the new environment of the Semantic Web. This has been achieved by extracting information from WSDL files and using it to create a new semantic description files using OWL-S. Hai Dong et al [20] proposed a conceptual framework for a semantic focused crawler, which combines the speciality of ontology-based metadata classification from the ontology, based focused crawlers and the speciality of metadata abstraction from the metadata abstraction crawlers, in order to achieve the goal of automatic service discovery, annotation, and classification in the Digital Ecosystems environment. Antonio Brogi [8], had emphasized the importance of behavioral information in service contracts, that inhibits the possibility of guaranteeing the service interactions and highlighted the limitations of currently available service registries which do not take into account the behavioral information. In the research to web services, a lot of initiatives have been conducted with the intention to provide platforms and languages for Web Service Composition (WSC) such as Business Process Execution Languages for Web Services (BPEL4WS) [1]. Presently some languages have the ability to support semantic representation of the Web services available on the internet such as the Web Ontology Language for Web Services OWL-S [2] and the Web Service Modeling Ontology WSMO [9].
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Affordances in Grounded Language Learning

Affordances in Grounded Language Learning

The crucial question is how we can effectively map between word-vectors, which we take to rep- resent a kind of encyclopaedic knowledge of the world, and the affordances which are proposed as at least a rough model of the way that words are encountered by an early language learner. In or- der to explore this issue further, we construct a rudimentary neural network, mapping the 200 el- ements of each of our word-vectors onto the sets of probability distributions corresponding to affor- dances by way of a single dense softmax layer. This operation is in effect quite similar to a multi- class logistic regression, except that here we are attempting to learn to approximate an actual prob- ability distribution rather than to simply reward the assignment of the highest score to a particu- lar class. Formally, we map from a word-vector − →
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A Paper on Multisite Framework for Web page Recommendation Using Incremental Mining

A Paper on Multisite Framework for Web page Recommendation Using Incremental Mining

Large amounts of information present on web due to that web user finds difficulty when surfing the web. Useful and Effective suggestions provided by Recommendation systems which help web users. User’s future interest evaluate by using the learning and prediction models which learn user’s behavior. Web page recommendation improves web user satisfaction and website usage. Semantic enhanced “Multisite framework for Web page Recommendation using Incremental Mining” improves web page recommendation. In which web page recommendation will be done by using multiple website in same domain. Will be give effective recommendation results given by Different sites. To identify and collect more appropriate web usage data Dynamic web page recommendations tool is generated. Related terms identified which help to generate more appropriate data. Incremental mining update web usage knowledge and updates the knowledge base.
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Indexing languages in information Management, a promising future or an obsolete resource

Indexing languages in information Management, a promising future or an obsolete resource

Indexing languages lack of popularity is due to the absence of the desired double articulation, towards users and back from users; thus losing connexion to reality (Gonzalo & Yebra, García, 2004). This explains why –as shown in Figure 2– the higher the complexity of the language is, the lower the proximity and usability from a user point of view. (Morato et al., 2008). Undoubtedly, lightly formalized Knowledge Organization Systems (KOS) (Light weight ontologies) improve the usability, management and understanding on behalf of the user, which is obvious if we observe the Web 2.0 tags popularity (also called folksonomies) (Gruber, 1993) for describing multimedia resources of the Invisible Web. The Web 2.0 was developed as a natural evolution of the Web 1.0. Social Web, or Web 2.0, is a more usable Web employing a minimum complex indexing language (see Figure 3).
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Applying the Semantic Web to Manage Knowledge on the Grid

Applying the Semantic Web to Manage Knowledge on the Grid

Domain ontology of EDSO Knowledge Toolbox Workflow Composition Environment (WCE) Domain Script Editor (DSE).. Knowledge Life cycle[r]

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Applying the Semantic Web to Manage Knowledge on the Grid

Applying the Semantic Web to Manage Knowledge on the Grid

We have provided a parsing capability to facilitate automatic information extraction. As primitive Matlab functions have conventions for interface specification, we are able to obtain important information about a resource such as input, output parameters and location details directly from the Matlab code. The extracted information will be listed on one tab panel on the right-hand panel, and can be used directly by the function provider in creating the annotations. Other types of semantic information can be expressed manually, either as a result of viewing the code, or via the utilization of knowledge that is not expressed explicitly in the code.
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Talking NPCs in a Virtual Game World

Talking NPCs in a Virtual Game World

ture ontology. During conversation, these entries will be specified with the values of the properties of the discussed objects. This frame-based ap- proach increases the flexibility of the dialog man- ager (McTear, 2002) and is particularly useful for a task-driven dialog system. As long as the negoti- ated object is not yet fully specified, the form rep- resents the underspecified object description ac- cording to the ontology concept. Every time the user states a new preference or request, the form is enriched with additional features until the set of objects is small enough to be presented to the user for final selection. Thus the actual flow of dia- log according to the task model does not have to be expressed by the graph but can be derived on demand from the knowledge and handled by the form which in turn activates the appropriate dia- log subgraphs. This combination of graph-based dialog models and form-based task modelling ef- fectively accounts for the interaction of sequential dialog strategies and the non-sequential nature of complex dialog goals.
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