Owl language pdf

Results Citations. Topics from this paper. Web Ontology Language Ontology information science. Resource Description Framework. Citation Type. Has PDF. Publication Type.

That is an important issue in knowledge using some formal knowledge representation language [7]. By this organization, a class may inherit the In the understanding system, the knowledge base is the main properties of any of its superclasses and it may pass module. It contains the English vocabulary agents and all properties to any one of its subclasses [1]. However, only linguistic information about this vocabulary. Good traditional object-oriented technique is not enough for a Knowledge representation is the basis of a good knowledge good knowledge representation or knowledge base.

To evaluate one knowledge representation, there are the following four criteria [7]: Representational Adequacy: the ability to represent all kinds 3. Inferential Ontology is about the exact description of things and their Adequacy: the ability to manipulate the representational relationships. It is an old study of philosophy from ancient structures to derive new structures corresponding to new Greece. As the study of artificial intelligence growing, the knowledge inferred from old.

Inferential Efficiency: the concept of ontology have been using more and more in the ability to incorporate into the knowledge structure additional formalization of knowledge in terms of classes, properties, information that can be used to focus the attention of the instances and the relations. So, for knowledge, ontology is inference mechanisms in the most promising directions. The simplest case involves direct different categories of the knowledge.

Moreover, for the insertion, by a person, of new knowledge into the database. The OWL Web Ontology Language is designed for use by The objective of knowledge representation is to organize the applications that need to process the content of information information necessary to the application such that it is easily instead of just presenting information to humans.

Because it accessed and manipulated. The knowledge content must be provides additional vocabulary along with a formal sufficient to solve problems in the domain and it must be semantics, OWL facilitates greater machine interpretability efficient [1]. In addition OWL adds a much richer set of its One of the effective approaches to solve the data exchange own primitives, such as transitivity, cardinality, disjunction among different computers via the computer networks is etc.

HTML is to richer typing of properties e. The enumerated classes [5]. As a result, OWL has more documents written in XML can describe what data is and be facilities for expressing meaning and semantics than XML shared and exchanged among different systems [2]. Thus OWL goes beyond these languages in its provides a syntax for structured documents, however it ability to represent machine interpretable content on the imposes no semantic constraints on the meaning of the Web.

RDF is a data model for objects and relations between them, providing a simple semantic for this 1- Formalize a domain by defining classes and properties data model. RDFS is considered to be an 3. As Santtu Toivonen concludes Indeed, these requirements have prompted W3C's Web in his research [9], RDFS is suitable for providing the Ontology Working Group to define OWL as three different means for an ontology that characterizes some environment, sublanguages [4], each of which is geared towards fulfilling no matter how abstract [5].

RDFS alone, however, suffers different aspects of this incompatible full set of from its dependence on domain-specific and case-specific requirements: details. It also allows characteristics of properties. Both of them are based on top of RDFS.

The disadvantage of of a larger ontology [3]. OWL Full is the language has become so powerful as to be undecidable, dashing any hope of complete let alone efficient reasoning support [3]. Roughly this amounts to Figure 3 - Simple Named Classes disallowing application of OWL's constructor's to each other, and thus ensuring that the language corresponds to well- The fundamental taxonomic constructor for classes is studied description logic.

The advantage of this is that it rdfs:subClassOf. These documents are, however, all rather technical and mainly aimed at OWL 2 implementers and tool developers.

Those looking for a more approachable guide to the features and usage of OWL 2 may prefer to consult one of the user documents, which include a Primer and a Quick Reference Guide. Please, refer to a separate page listing some of those, as maintained by the community. That list also includes references to conference proceedings and article collections that might be of general interest. Note that you can browse tools per tool categories or programming languages , too.

The use of open datasets, such as OpenStreetMap, allows a widespread and detailed coverage of Italian geographical places and provides a high precision in the detection of real places. In addition, the introduction of Semantic Web Rule Language SWRL rules [11] allows inferences on knowledge, implicitly contained in the novel ontologies which are more refined than other currently available ontologies on geographical places.

Moreover, thanks to suitable procedures of automated reasoning, it is possible to extract implicit informa- tion present in data, leveraging a deeper knowledge of the domain.

The domain is specified by means of expressions describing statements about web shared resources. Such expressions are given as triples of the form subject-predicate- object. The subject denotes the resource to describe, namely the actor of the statement, the object denotes the recipient or the result of the action, and the predicate denotes traits or aspects of the resource, i. Sharing data is an important feature of the semantic web. Because of the uniqueness of the URI of the resources and of the document, we can refer to them without ambiguity.

An important feature of the semantic web is the capability to extract implicit information from the described data. This is why the language RDFS has been introduced. Unlike the RDF language, RDFS is powerful enough to enable such a feature since it allows to express subclass and subproperty relationships.

Elements of the domain sharing common charac- teristics can be grouped in particular sets called classes. Classes, in their turn, can be organised in hierarchies. A hierarchy of classes is called a taxonomy. Prop- erties can be organised in hierarchies too. If an element of the domain is related to another element by a subproperty, then it is related to the superproperty as well. RDFS provides other interesting inference capabilities that, for space reasons, we do not report here.

However, RDFS is far away from allowing complex reasoning. Relationships of this type can be expressed in OWL using a multitude of properties. The task of writing OWL data requires the definition of an ontology. Infor- mally, in computer science an ontology defines a set of representational primitives classes and properties apt to model a domain of knowledge or of discourse [22]. When an OWL reasoner namely, a given software system able to extract in- formation from OWL files is executed on OWL data, we can perform the task of mining data from resources.

In fact, OWL allows one to specify far more about the properties and classes of an RDFS schema by means of a formal description of the data. Expressiveness of such ontologi- cal model depends on the OWL profile adopted. Some of these profiles include SWRL rules. Such rules have the form of an implication between an antecedent body and consequent head. The intended meaning can be read as: whenever the conditions specified in the antecedent hold, then the conditions specified in the consequent must hold as well.

The desirable fea- tures of the OWL language shortly outlined above strongly motivate our interest in using OWL ontologies and related reasoning tools for the location recognition task. More details about OWL reasoning capabilities, semantics, and profiles can be found in [4, 7, 11]. In particular, in [16], a reasoning system based on a frag- ment of set-theory is proposed particularly suitable for the ontologies presented in this paper.

Unlike machine learning approaches, both unsupervised and supervised, we proposed a rule- based approach built from simple grammar rules of the Italian language com- plemented by a dictionary, where each rule identifies a different pattern that characterises sentences at the end of which we usually find a location name. The devised rules are supported by a specifically compiled Italian lexicon, containing the classes of words Articles, Verbs, and Descriptors, and a list of Non-places words that are known false positives; in order to improve the overall accuracy of the extraction tool, the lexicon can also be extended by other, user-detected false positives [18].

As shown in [18], these rules provide a large coverage of the Italian grammar for what concerns statements about places. Pipe and filter workflow for location extraction. Figure 1 depicts the entire workflow used for the location extraction: in the first sentence splitting step, an input text T is separated into a list of sentences using occurrences of punctuation marks, i.

Any other non-letter symbol is ignored, e. Each sentence is further segmented into words using the space character as a separator. The tokens words are then fed to a finite state ma- chine implementing three different rules, i.