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 basedknowledgerepresentation, 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 knowledgerepresentationlanguage.
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 knowledgerepresentation definition we use is based on the Semantic Web point of view.
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 KnowledgeRepresentation domain, "knowledge is descriptive and can be expressed in a declarative form" . 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:
time complexity . 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 . 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 . Aviv Segev et al.  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 . Tamer Ahmed Farrag et al.  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  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 , 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) . 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  and the Web Service Modeling Ontology WSMO .
The UML class diagrams represent the important concepts from the problem domain in the form of classes and their attributes and methods. The relationships among these concepts are represented by relationships among these classes. One can also express the cardinality and other types of constraints using the UML notation available (Booch, Rum Baugh & Jacobson, 2004). The ontologies define concepts from the problem domain and relationships among them. The XML Model Interchange Language defines a standard way to serialize the UML diagrams (Cranefield, 2001). So the knowledge expressed in the form of UML diagrams can be directly comprehended by human because of its standard graphical representation as well as by ontology editors. There are also a number of Java class libraries available to provide an interface to various applications accessing this information. The UML diagrams also can be accessible and processed by computers because of XMI and associated libraries or APIs defined by MOF (Baclawski, Kokar, Kogut, Hart, Smith, Holmes, Letkowski & Aronson, 2001). The XMI specifies how a model stored in a MOF- based model repository can be represented as an XML document. The UML class diagrams can be mapped to RDF schemas (Falkovych 2003). UML classes can also be mapped to sets of Java classes. These classes correspond to the classes in the class diagram.
One of the most effective instruments teachers can employ in designing technology-based class environments is the internet. Limitless accessibility to the source, providing opportunities for interpersonal interaction, its ability to offer service independent of time and place have all made the internet a preferable instrument. The rapid development of web technologies have made materials for distance education more flexible and thus led to changes to make such settings an alternative to or a support to education in traditional classrooms . Teachers can design classes supported by graphs, audio materials, animations, videos and texts in internet-based or interned-assisted classes . These possibilities provided by the internet require that teachers and prospective teachers have the knowledge of and skills in using the internet and internet technologies in instruction . Due to the fact that the properties of the internet/web were different from other technologies, that using them needed technology, and that technological pedagogical domain knowledge did not provide adequate knowledge; Lee and Tsai and Lee, Tsai and Chang  described the concept of Web Pedagogical Content Knowledge (WPCK).
Since that time, each generation of computer hardware has brought an increase in speed and capacity and a decrease in price. Performance doubled every 18 months or so until around 2005, when power dissipation problems led manufacturers to start multiplying the number of CPU cores rather than the clock speed. Current expectations are that future increases in power will come from massive parallelism—a curious convergence with the properties of the brain. Of course, there were calculating devices before the electronic computer. The earliest automated machines, dating from the 17th century, were discussed on page 6. The first pro- grammable machine was a loom, devised in 1805 by Joseph Marie Jacquard (1752–1834), that used punched cards to store instructions for the pattern to be woven. In the mid-19th century, Charles Babbage (1792–1871) designed two machines, neither of which he com- pleted. The Difference Engine was intended to compute mathematical tables for engineering and scientific projects. It was finally built and shown to work in 1991 at the Science Museum in London (Swade, 2000). Babbage’s Analytical Engine was far more ambitious: it included addressable memory, stored programs, and conditional jumps and was the first artifact capa- ble of universal computation. Babbage’s colleague Ada Lovelace, daughter of the poet Lord Byron, was perhaps the world’s first programmer. (The programming language Ada is named after her.) She wrote programs for the unfinished Analytical Engine and even speculated that the machine could play chess or compose music.
basic concepts that underlie human cognition; the other two are organization and causation. Informally, granula- tion involves decomposition of whole into parts, organi- zation involves integration of parts into whole and causa- tion involves association of causes with effects. Hence, how to characterize the process of granulation has been a crucial problem. In other words, the validity of distin- guishable ability, which is used to create the knowledge granules, should be examined because the knowledge granules in an information system are finite. Shannon , Beaubouef , Qian  and Liang [11,12] etc. used some useful methods to evaluate the uncertainty of information and L. A. Zadeh applied the notion of granularity to do this work, which presents a more visual and easily understandable description for a partition on the universe. Moreover, the relationships between sev- eral measures on knowledge in an information system were discussed in ref. . These measures include granulation measure, information entropy, rough entropy, and knowledge granulation. Especially, closely associ- ated with granularity, Xu  carefully discussed the properties of every granularity mentioned above. It is known that these measures have become effective mechanisms for evaluating uncertainty in rough set the- ory.
In both exercise types sentences are selected according to the proficiency level specified by the user. For that, a special Lärka-based sentence readability module, HitEx “Hit Examples”, currently available for ntermediate level [B1] and above (Pilán et al., 2013; Pilán, 2013). The module selects and ranks corpus hits either based on heuristic rules only or using a combination of rules and classification with machine learning. To assess the readability of sentences, a number of morpho-syntactic (e.g. average dependency length) and lexical-sematic features (e.g. CEFR level and frequency of words) are taken into consideration. The rules offer the possibility also to filter sentences containing certain linguistic elements including, among others, abbreviations, negative formulations and participles. Sentences are selected from three different corpora to cater for a combination of different genres, namely SUC3.0 (a balanced corpus with texts from various genres), GP2012 (newspaper texts) and ROM99 (novels). Sentences for training vocabulary coming from AO are selected from specialized corpora comprising academic texts in the areas of the humanities and social sciences (Sköldberg and Johansson Kokkinakis, 2012).
KNOWLEDGE REPRESENTATION METHOD BASED ON PREDICATE CALCULUS IN AN INTELLIGENT CAI SYSTEM cagLl~ 82, J Horecl~ (e~ ) North Holland Publishln~ Cor,~my ? Academia, 1982 KNOWLEDGE REPRESENTATION METHOD BA[.]
In our second experiment, the goal is to propose and to evaluate techniques for the combination of n-gram counts from heterogeneous sources. Therefore, we will use the insights about the vo- cabulary differences presented in the previous sec- tion. In this evaluation, we measure the impact of the suggested techniques in the identification of noun–noun compounds in corpora. Noun com- pounds are very frequent in general-purpose and specialised texts (e.g. bus stop, European Union and gene activation). We extract them automat- ically from ep and from genia using a standard method based on POS patterns and association measures (Evert and Krenn, 2005; Pecina, 2008; Ramisch et al., 2010).
The Task section of the Web-quest explains clearly and precisely what the learners will have to do as they work their way through the Web-quest. The task should obviously be highly motivating and interesting for the students, and strongly con- nected with a real-life situation.[6,16,17] This of- ten involves the learners in a particular amount of role-play within a given situation (e.g. they find the approximate fare of a ticket between two certain cities or towns; information about the types of res- taurants, a school social organizer has to organize a trip for his class to an English-speaking country; a travel agent needs to organize a conference in London; he needs to know real data that helps him to figure out how things are (companies, bookings, prices, attractions, entertainment, Internet brows- ing and search skills and more are presented) in real life; learners choose the appropriate class of the airplane among different ones according to the services provided on board, exchange the informa- tion concerning the onboard services on a train which ones are desired, such as «catering», «type of sleeper» or various other requests that can be found in the online booking form etc. ). Be- sides it should be in place here to state what the students will be required to do, to avoid surprises down the road, to detail what products will be ex- pected and the tools that are to be used to produce them. The point is the formal description of what the students will produce in the Web-Quest. The task should be meaningful and fun. Creating it is the most difficult and creative part of developing a Web-Quest .
It takes into consideration two things, first weight of tuple which is similar to web page rank and second thing is weight of edge which measures the relationship between two tuples. Ranking strategy of BANKS, DISCOVER and DBXplorer do not take into account state-of-the-art IR style ranking which is very successful. Efficiency is given in first step. Standards are planned in such an approach to keep away from superfluous tuple in answer tuple tree.
Thus the ideal KRF would be one that is appropriate to the whole range of types of knowledge that could conceivably be encountered. This chapter, which is based on Basden (1993) but takes it further, discusses whether it might be possible to find such an ideal KRF. Using a concrete example of knowledge we might wish to represent, we briefly review the characteristics of various types of knowledge, which are discussed in Basden (1993). We outline the problems that arise in representing these using inappropriate KRFs and note that philosophy is needed to address them. After outlining some portions of a pluralistic philosophy, we show how they might be applied to develop more appropriate KRFs that enable diverse knowledge to be represented.
Abstract— A balanced diet contains nutrient rich foods from all the food groups. People are becoming very conscious about their health. People who follow a well balanced diet feel better and are in better health. Calories are an important component of the diet. Calorie needs for an individual depend on gender, age, activity level and weight. It is important to supply right amount of calories to the body to function properly. People follow different diet plans. Imbalance in the diet results in illnesses and different diseases. For recommending diet instead of manually, different approaches from computer science are used. In this project diet recommendation approaches is based on ontology used for knowledgerepresentation method and is combined with content based filtering concept to recommend diet specific to the user preference.
Abstract— This research explores the development of multi-leg searching concept by adopting graph-basedknowledgerepresentation. The research is aimed at proposing a searching concept that is capable of providing advanced information through retrieving not only direct but continuous related information from a point. It applies maximal join concept to merge multiple information networks for supporting multi-leg searching process. Node and edge similarity concept are also applied to determine transit node and alternative edges of the same route. A working prototype of flight networks domain is developed to represent the overview of the research.
2.3 KNOWLEDGEREPRESENTATION USING FRAME: A frame is a data structure that includes all the knowledge about a particular object. This knowledge is organized in a special hierarchical structure that permits a diagnosis of knowledge independence. Frames are basically an application of object- oriented programming for artificial intelligence and Expert System. Frames provide a concise structural representation of knowledge in a natural manner. The knowledge in a frame is partitioned into slots. A slot can describe declarative knowledge (e.g., the color of a car) or procedural knowledge. A frame includes two basic elements: slots and facets. A slot is a set of attributes that describe the object represented by the frame. Each slot contains one or more facets. The facets (subslots) describe some knowledge or procedural information about the attribute in the slot. Most artificial intelligence systems use a collection of frames linked together in a certain manner to show their relationship. This is called a hierarchy of frames. The hierarchical arrangement of frames permits inheritance frames. 2.4 KNOWLEDGEREPRESENTATION USING SCRIPT A script is a term proposed by Schank, and it refers to a form of knowledgerepresentation . A script is a structured representation describing a stereotyped sequence of events in a particular context . For Example, when we go to a restaurant, we usually 'enter the restaurant', 'wait', 'sit down', 'get the menu and decide what to eat', 'order the dish’, ‘wait until the dish has come', and so on. This sequence can be said to be script knowledge in the situation of 'eating at a restaurant'.
The answers of the six teachers to the ﬁve questions were systematically analyzed by three diﬀerent educational researchers. Every researcher had the same perspective on the subject. There was agreement about deﬁnitions of the categories and the results of the analysis (stability). Afterwards, there an authorization of the analysis and the way of representation by each of the interviewed teachers. The ﬁrst two questions focused on perceptions regarding diﬃcult topics and possible causes. The answers to questions three and four demonstrated representations in general, but sometimes exhibited speciﬁcally represent regarding analogies. The last question was an addi- tional opinion about using analogies. The ﬁrst question provided supposed diﬃcult topics within the area of mother-tongue education; the ranking of topics by the teachers was not always clear. We recorded which topics were mentioned by the teachers and for how long they pursued the subject. The second question provided reasons for diﬃculty related to the level of diﬃ- culty. We were looking for reasons like abstraction, strategies in use, and misconceptions. The third question was marked by teaching methods; the last two questions were opinions. The qualitative content analysis took place by summarizing their remarks and conclusions, and illustrating them with well-chosen quotations. There was a correspondence of the categories to the conclusions (validity). In view of the small number of teachers interviewed, we did not collect quantitative data.
Permintaan kepada pengintegrasian data secara pantas menjadi semakin tinggi dengan semakin banyak sumber-sumber maklumat yang terdapat di dalam perusahaan moden. Extensible Mark-up Language (XML) telah menjadi satu piawaian baru bagi perwakilan dan pertukaran data dalam World Wide Web (WWW), contohnya di dalam aplikasi Business to Business (B2B) pada e-dagang. Ini memerlukan alatan analisis data untuk mengendalikan data XML di samping format data tradisional. Tujuan
basic actions consists of a combination of convo- lutional and LSTM layers within a neural network. Work of this nature highlights the state of the art in modelling technologies, and as an information engineering approach to meaningful tasks such as question answering and image labelling a signifi- cant contribution is made. This is arguably done, however, at the expense of presenting interpretable or indeed plausible models of the way that envi- ronmentally embedded agents use relatively scant exposure to a language speaking community in or- der to develop a lexicon that is rich and produc- tive. In this regard, the conventional computa- tional stance on grounded language learning em- braces a view of the relationship between language and the world as a symbol grounding problem, by which abstract symbols susceptible to formal op- erations are somehow associated with perceptions and propositions: the hard work is done by a com- plex and philosophically opaque process of trans- forming signals into symbols, with the sense that computation by way of deep nets in some sense stands in for an inscrutable mind-brain gestalt.