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.
A domain model is defined as an abstract representation of a small part of the world (special case of ontologies). The domain model components include concepts, relationships between these concepts, and properties of the concepts and relationships. The definition of a concept in the context of other concepts is done by relationships. The specification of the characteristics of a concept is realized by properties. By modeling a domain, the knowledge about it is captured and the assumptions on which the domain is built are made explicit . However, a domain model is adopted for a common understanding of the domain capturing with the aim to create a basis for unambiguous communication. Each individual in the world has a unique personal conceptual model. It is difficult to model a domain, because the individual conceptual models of
The e-COGNOS platform presents the first comprehensive ontology-based portal for knowledge management in the construction domain. The main features of the platform are an ontology (to encapsulate human knowledge) and a set of web services to support the management of ontology (creation, updates), user management (profiling) and handling knowledge management requirements (indexing, documentation, retrieval and dissemination). Implementation of e-COGNOS platform (at leading European construction organizations) by Lima et al. (2005) has proven the benefit of semantic systems as they provided adequate search and indexing capabilities, allowed for a systematic procedure for formally documenting and updating organizational knowledge (through the ontology) and enhanced the customization functions in a knowledge management systems (through user profiling). The functional architecture of e-COGNOS is shown in Figure 3.
Among the proposed approaches for this task, most of them ignore R and K , centering on re- forming compositionality function f (Baroni and Zamparelli, 2010; Grefenstette and Sadrzadeh, 2011; Socher et al., 2012, 2013b). Some try to integrate combination rule R into SC models (Bla- coe and Lapata, 2012; Zhao et al., 2015; Weir et al., 2016; Kober et al., 2016). Few works con- sider external knowledge K. Zhu et al. (2016) try to incorporate task-specific knowledge into an LSTM model for sentence-level SC. As far as we know, however, no previous work attempts to use general knowledge in modeling SC.
predicate logic (FOPL), including Attempo Controlled English (ACE) by Fuchs et al. [Fuchs et al. 2005; Kaljurand and Fuchs 2006] and Computer-Processable EN- Glish (PENG) by Rolf Schwitter [Schwitter 2004]. Peter Chen proposed eleven rules to manually extract entities, relations and attributes in ER models from English sentences [Chen 1983]. ER models require engineers to decide whether a “thing” is an entity or a relation between entities, an ambiguity we call the node-edge problem, which we discuss in Section 3.1. We extend those rules identified by Chen with new NL patterns in Section 4. Alternatively, PENG provides formal semantics in FOPL to compose compound sentences from coordinators (and, or) and subordinators (before, after, if-then) [Schwitter 2004]. ACE presents a case-based analysis of nat- ural language structure that is mapped to FOPL and resolves anaphoric references using definite articles [Fuchs et al. 2005]. Words with an anaphoric or cataphoric function, such as English pronouns and definite articles (e.g., this, that, the), refer the reader to a particular thing or individual in the prior or subsequent context of a description, respectively. Our approach requires engineers to formally distinguish all shared individuals using anaphoric and cataphoric references and to unambigu- ously map these individuals into assertions in DL. Moreover, our approach requires atomicity in mapping nouns, verbs and adjectives to unique concepts in an ontol- ogy. This degree of atomicity, combined with DL subsumption inference, enables a richer query environment than unrestricted FOPL. Whereas some have argued that ACE has been mapped to DL using “artificial” examples that lack coherence and relevance to a particular domain [Kaljurand and Fuchs 2006], their results are still preliminary, anecdotal and have not been empirically validated to show repeata- bility across a substantial body of domain descriptions. In Section 5, we present empirical validation of our approach in three case studies over two domains.
The authors ShailyG.Langhnoja, Mehul P. Barot and, Darshak B. Mehta have presented Web Usage Mining is application of data mining techniques to discover interesting usage patterns from Web data, in order to understand and better serve the needs of Web-based applications  . Analyzing data through web usage mining can help effective Web site management, creating adaptive Web sites, business and support services, personalization, and network traffic flow analysis and so on. Lots of research has been done in this field while this paper emphasizes on finding user pattern in accessing website using web log record. The aim of this paper is to find user access patterns based on help of user‟s session and behavior. Web usage mining includes three phases namely pre-processing, pattern discovery and pattern analysis. In this paper we studied combined effort of clustering and association rule mining is applied for pattern discovery in web usage mining process in our system. This approach helps in finding effective usage patterns.
While various technologies are available for SMD representation and storage, EDSO service characteristics and the usage of GEO- DISE SMD determine that OWL is the most suitable for representing GEODISE ontologies and functions’ SMD. EDSO services usually embody deep knowledge about their usage and performance. For example, there are dozens of different optimisation methods, each of which is geared to solving a speciﬁc type of engineering problem. Even with a single method, differ- ent conﬁgurations of control parameters may produce very different results. To model such complex knowledge requires OWL’s expressive capability. The subtlety of knowledge of EDSO services also means service discovery is depen- dent on detailed search criterion. Description- based search mechanism is the best approach to dealing with this requirement. Services can be discovered in terms of the descriptions; the more detailed descriptions, the more accurate service will be found.
The ontologies are represented in a machine- understandable language with formal semantics and reasoning capability, namely DAML+OIL. This language is based on Description Logics. Ontologies in this language can be elaborate and expressive, and the temptation is to over complicate the interface to them, rendering them daunting and incomprehensible to the user. Instead we adopted a simplified presentation interface that loses little of the expressivity of the language but hides it from the user. We call this OntoView – it provides a “domain expert- sympathetic” view over the ontology, configurable by the expert knowledge engineer in collaboration with the domain specialists. The view consists of a set of relatively simple “view entities” that map to more complex constructs in the underlying ontology. As these entities are manipulated in the view, corresponding modifications will be produced in the ontology. The manner in which the entities in a particular ontology view map to the constructs in the underlying ontology, is determined by a “view configuration” (Figure 2), specifically created for that ontology, and stored in an XML-based format.
In this, paper describes process of Web personalization viewed as an technique of data mining required to guide all the data mining cycle. The phases include collection of data then preprocessing after that pattern discovery and evaluation. Finally this discovered knowledge is used in real-time for user and web.Here semanticweb technology is positive for recommendation. Along with that web mining plays important role which is
1) Step 1: Collect The Domain Terms: To gather the terms, we will: (i) gather the Web log record from the Web server of the site for a time of time (no less than seven days), (ii) run a preprocessing unit to dissect the Web log document and produce a list of URLs of Web-pages that were gotten to by clients, (iii) run a product specialists to slither all the Web- pages in the URL rundown to concentrate the titles, and (iv) apply a calculation to concentrate terms from the recovered titles, i.e., single tokens are separated first by expelling prevent words from the titles, some single tokens are then consolidated into composite terms if these single terms regularly happen at the same time and there is never any token shows up between these tokens, and the staying single tokens will get to be single word terms. In view of the removed terms, we can sum them up to space con- cepts in Step 2.
Distributional data tells us that a man can swal- low candy, but not that a man can swallow a paintball, since this is never attested. How- ever both are physically plausible events. This paper introduces the task of semantic plau- sibility: recognizing plausible but possibly novel events. We present a new crowdsourced dataset of semantic plausibility judgments of single events such as man swallow paintball. Simple models based on distributional repre- sentations perform poorly on this task, despite doing well on selection preference, but inject- ing manually elicited knowledge about entity properties provides a substantial performance boost. Our error analysis shows that our new dataset is a great testbed for semantic plausi- bility models: more sophisticated knowledge representation and propagation could address many of the remaining errors.
Beyond substantive ideas, Porphyry (Plotinus’s disciple) has the merit of having arranged the first semantic network by distributing universal predicates as a tree 1 , showing graphically the relations between concepts regarding their genus, subtype and difference (Sowa, 2000; Moreiro, 2006). By these means, the existence of a hierarchic order between Aristotle categories is fixed, where genus is occupied by substance or composed by, descending in the scale in the order provided by Genus and Species. This order has arrived to us as a conceptual structure of taxonomies and thesauri, containing the source of the hierarchic disposition of their terms in its category relation. Each genus has as generic its immediate superior genus, for which it is species, at the same time that it acts as generic of inferior genus of immediate order. It
SIMILE  is a joint project conducted by the MIT Libraries and MIT Computer Science and Artificial Intelligence Laboratory. As the project objective described in , it seeks to enhance interoperability among digital assets, schemata/vocabularies/ontology, metadata and services. SIMILE will leverage and extend DSpace , enhancing its support for arbitrary schemata and metadata, primarily through the application of RDF  and semanticWeb techniques. The project also aims to implement a digital asset dissemination architecture based on Web standards. The dissemination architecture will provide a mechanism to add useful "views" to a particular digital artifact (i.e. asset, schema, or metadata instance), and bind those views to consuming services. A key challenge is that the collections which must inter-operate are often distributed across individual, community, and institutional stores. The objective is set to be able to provide end-user services by drawing upon the assets, schemata/vocabularies/ontology, and metadata held in such stores.
Irrespective of type of values, handling missing data is one among the most important data pre-processing steps. If not handled carefully, the entire model may be deteriorated with decreased prediction accuracy. The dependency of missing values can be categorized into Completely Missing At Random (CMAR) in which missing values will be independent of all the features, Missing At Random (MAR) in which values depend on other features and No Missing At Random (NMAR) in which missing values depend on other missing features. In the present dataset, missing values and their dependencies can be categorized into MAR type. The statistical imputation methods like Hot Deck, central tendency measures (mean, median, mode) etc. impute values irrespective of the neighboring attribute dependencies. But in reality, particularly in healthcare domain, the attribute values depend on the other features to certain extent as stated earlier. So, Machine learning methods and imputation techniques using algorithms like SVM, k-means, K – Nearest Neighbors (KNN), Ensemble methods, Gradient boosting etc. can be employed for missing values
The literature on the SemanticWeb and DLs emphasises the need for formal computer representation of “meaning”, “understanding” and “knowledge” all of which is, of course, implied in the word “semantic” itself. Using formal computer representation means that SemanticWeb technologies would be amenable to searching and manipulating data “in ways that are useful and meaningful to the human user” (Berners -Lee et al, 2001). Ultimately, however, the final arbiter of the adequacy of the meaning, understanding and knowledge represented has to be the human end-user. This would seem to imply that the essentially human knowledge represented in DLs should in some fairly fundamental way correspond to the way this knowledge is represented in the human mind.
Ribonucleic acids (RNAs) are essential cellular components with significant roles in protein synthesis and gene regulation. Increasingly sophisticated knowledge about RNA structure and function is being revealed as a result of innovative biochemical investigations such as genome sequencing projects, sequence alignments, microarray analyses, structure determination and RNA SELEX experiments. Yet, our capacity to capture this knowledge by existing systems is limited in several important respects. First, RNAML , an XML-based exchange format for a select subset of information about RNAs, does not provide explicit formalization of the domain either from a logi- cally or philosophical perspective. As an example, base stacking can be described with a natural language comment associated with the base-stack element, but we cannot specify a machine understandable type – what kind of thing is base stacking and what specializations of it exist (e.g. adjacent stacking or upward stacking). Second, XML Schema is primarily interested in the validation of the document structure, as opposed to the semantics of the domain terminology therein contained, thus language exten- sions cannot be properly validated. In contrast, RDF/OWL are formal (logic) languages which enable the explicit formalization of the domain, and as such can be used to infer new knowledge using some information system. Moreover, as languages of the SemanticWeb, researchers may also publish their knowledge so as to further enhance structural and functional annotation in a machine accessible, but de-centralized manner.
An efficient private searching keyword on frequently visited web-page which ensures user authentication and access control in a privacy preserving way using homomorphic encryption technique has been implemented. The features of websites are enhanced according to the user’s interest on web based applications in an optimised web search engine. The Web-page recommendation is developed to offer Web users the top-N most commonly visited Web-pages from the currently visited Webpage. The knowledge bases used in the system, includes the website domain and Web usage knowledge bases, are represented by ontological-style semantic networks which can be implemented consistently in a formal Web Ontology Language. The current system works with static Web-pages. With the advancement in Web technology, pages have been evolving into pages with dynamic structures. To offer more effective Web-page recommendations, it will be highly desirable to develop advanced tools to identify and collect more appropriate Web usage data than Web logs, such as click stream data. Websites have been evolving over time therefore the knowledge bases, i.e. domain and Web usage knowledge bases, need to be updated accordingly. Considering the traditional Web usage data source, which is the Web log file, the system can only take a limited segment of the log file to build the Web usage knowledge base due to the fact that the size of the log file can be huge. The future work can be focused on the discovered Web usage knowledge is up-to date, new methods need to be developed to dynamically update the knowledge bases.
• Selection of right ontology designing or reusing methodology. There are online and offline ontologies available for download. Leeds Quran ontology, for example, is a good resource for research available at http://corpus.quran.com/ontology.jsp. There are numerous other studied that develop Quran ontology for a particular chapter of Quran or specific types of verses. For example, Quran ontology for salat was developed by (Saad, Salim, Zainal, & Muda, 2011). OWL DL ontology was used by (Aliyu Rufai Yauri, Rabiah Abdul Kadir, 2013) to extract particular verses from Quran. There is a strong challenge here to reuse the existing ontology for knowledge extraction or build a complete Quran ontology with aim of extracting knowledge. • Use of large but optimal semantic basic
The Figure 5 shows the methodology that we have followed to build the domains ontologies for the e- government in the OWL (Web Ontology Language) language. The Methodology includes four phases: First, Ontology Extraction Phase, which extracts the concepts from knowledge sources. Second, Ontology Design and Integration Phase, which design the required models to build a unified and integrated ontology for domains mentioned in our e-government framework. Third, the Ontology Verification Phase, which verifies the ontology concepts by domain experts. Fourthly, the Ontology Implementation Phase, which represents the ontology using the OWL language. Details for each phase discusses in the following sub-sections.