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            "note": "<p><strong>Extracted Annotations (Wed Dec 18 18:10:48 2013)</strong></p>\n<p>\"In order to improve scalability, many Semantic Web query answering systems give up completeness (i.e., they do not guarantee to return all query answers). It may be useful or even critical to the designers and users of such systems to understand how m\" (<a href=\"zotero://open-pdf/0_ZWR87F2V/1\">Stoilos et al 2010:1</a>)</p>\n<p>\"kind of information is (potentially) being lost. to return all query answers). It may be useful or even critical to the designers and users of such systems to understand how much and what\" (<a href=\"zotero://open-pdf/0_ZWR87F2V/1\">Stoilos et al 2010:1</a>)</p>\n<p>\"A major difficulty with incomplete CQ answering systems is determining “how incomplete” they are. Thus, application developers may find it difficult to assess whether a system meets their needs.\" (<a href=\"zotero://open-pdf/0_ZWR87F2V/1\">Stoilos et al 2010:1</a>)</p>\n<p>\"according to our framework, Sesame is incomplete for queries Q2, Q4 and Q5. This is not surprising, as these queries use inverse roles (e.g., memb erOf and memb er ), which Sesame does not handle. The resulting incompleteness, however, is rather severe (e.g. Sesame is only 20% complete for Q4). However, the LUBM generator does not create assertions involving the role memb er and hence Sesame is complete for these queries w.r.t. the LUBM data. For Q6 OWLim fails to entail T [ fGraduateStudent(a)g j= Student(a) since it is not complete for inferences involving existential quantification and GraduateStudent v Student follows from v 9takesCourse:GraduateCourse; GraduateStudent v Course, GraduateCourse ≡ 9takesCourse:Course Student Minerva and HAWK use a DL reasoner to classify the ontology and make explicit subsumptions between atomic These two systems fail to entail Q5(a) from concepts. T [ fheadOf (a; b)g, which follows from the entailment T j= 9headOf :&gt; v Person involving a complex concept. These examples suggest that the identified sources of incompleteness could easily occur in realistic data.\" (<a href=\"zotero://open-pdf/0_ZWR87F2V/6\">Stoilos et al 2010:6</a>)</p>",
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            "note": "<p><strong>Extracted Annotations (Wed Dec 18 18:10:54 2013)</strong></p>\n<p>\"mobile devices nowadays routinely contain a GPS receiver, compass, accelerometer, and gyroscop e, and they can typically connect to WiFi and Blueto oth networks. Using data obtained from these sensors, p ossibly in combination with information accessed via the Internet, a mobile device can identify the context that it is b eing used in.\" (<a href=\"zotero://open-pdf/0_6ASERVXA/1\">Motik et al 2012:63</a>)</p>\n<p>\"The goal of context-aware applications is to exploit the context information in an intelligent way for the purp ose of helping the device owner in organising his daily life.\" (<a href=\"zotero://open-pdf/0_6ASERVXA/1\">Motik et al 2012:63</a>)</p>\n<p>\"Intelligent Mobile Platform (IMP)\" (<a href=\"zotero://open-pdf/0_6ASERVXA/1\">Motik et al 2012:63</a>)</p>\n<p>\"To provide the required knowledge management and reasoning services, several nontrivial questions must b e answered. The rst question is how to represent the knowledge used for reasoning in an adequate way. The second question is how to e ciently implement reasoning for the selected knowledge representation language. The third question concerns adequate to ol supp ort. veloping the necessary infrastructure from scratch can b e very time consuming, so reusing existing software is likely to considerably reduce the development cycle.\" (<a href=\"zotero://open-pdf/0_6ASERVXA/2\">Motik et al 2012:64</a>)</p>\n<p>\"Since the sensor readings change very frequently, DeltaReasoner must continuously up date the conclusions that it can draw from its ontology. Recomputing all conclusions from scratch every couple of seconds would clearly imp ose signi cant load on the limited resources of mobile devices. To this end, Delta-Reasoner uses incremental reasoning [18, 8]: after each change, our reasoner just computes the difference b etween the old and the new set of consequences, which considerably conserves resources.\" (<a href=\"zotero://open-pdf/0_6ASERVXA/2\">Motik et al 2012:64</a>)</p>\n<p>\"A set of axioms describing a domain of interest is commonly called a TBox. Sets of triples are commonly called (RDF) graphs or ABoxes.\" (<a href=\"zotero://open-pdf/0_6ASERVXA/3\">Motik et al 2012:65</a>)</p>\n<p>\"SWRL allows users to state rules that check for complex conditions. For example, the following rule states that if the device lo cation is the same as the lo cation of another ob ject, then the device is lo cated near this ob ject:\" (<a href=\"zotero://open-pdf/0_6ASERVXA/3\">Motik et al 2012:65</a>)</p>\n<p>\"Existing OWL reasoners can b e broadly divided into two groups, according to the kind of implemented reasoning algorithm. In the rst group are reasoners such as Pellet [16], HermiT [15], FaCT++ [19], and RACER [9] that are based on provably correct (hyp er)tableau calculi. These calculi can typically handle all or most of OWL 2 DL, and they can solve a wide range of reasoning tasks, such as classifying an ontology and checking ontology entailment; hence, (hyp er)tableau calculi are particularly useful in applications that require extensive reasoning ab out the schema (i.e., reasoning ab out the de nitions of classes and prop erties). In the second group are reasoners such as Jena [3], Jena2 [23], Oracle [4], and Sesame [2] that are based on (deductive) RDF stores. These systems typically store their data as RDF graphs, and they implement reasoning via materialisation : all (relevant) consequences that follow from RDF data and the OWL ontology are precomputed and stored in a prepro cessing step; after that, user queries can b e simply evaluated in the precomputed data. The main b ene t of such an approach is that computation-intensive pro cessing is concentrated in the prepro cessing phase.\" (<a href=\"zotero://open-pdf/0_6ASERVXA/3\">Motik et al 2012:65</a>)</p>\n<p>\"Materialisation, however, has two main drawFirst, materialisation can signi cantly increase the size of the RDF data set, thus requiring e cient storage and management of even larger materialised data sets. Second, materialisation can b e applied only to OWL 2 RL ontologies. Thus, all reasoners in this group supp ort only OWL 2 RL or a fragment thereof, with some also supp orting (a fragment of ) SWRL. backs.\" (<a href=\"zotero://open-pdf/0_6ASERVXA/3\">Motik et al 2012:65</a>)</p>\n<p>\"sion by adding\" (<a href=\"zotero://open-pdf/0_6ASERVXA/4\">Motik et al 2012:66</a>)</p>\n<p>\"SPARQL cannot express a query that retrieves only the direct sup erclasses of a class. This is primarily b ecause Semantic Web languages are based on rst-order logic, which is monotonic : one cannot invalidate a conclusion triples and/or axioms. A query for direct sup erclasses of a class, however, is clearly nonmonotonic, and so it cannot b e expressed in OWL and SPARQL.\" (<a href=\"zotero://open-pdf/0_6ASERVXA/4\">Motik et al 2012:66</a>)</p>\n<p>\"Problems such as the one mentioned ab ove are often solved in practice by issuing several SPARQL queries and combining their results manually. Such solutions, however, are typically hard-co ded to handle only sp eci c typ es of problems, and thus lack exibility. For example, combining class hierarchy queries with instance queries then b ecomes much more involved, b oth from the query sp eci cation and the query evaluation p oints of view. Thus, the lack of adequate supp ort for hierarchy navigation seems to b e an imp ortant feature that has not b een adequately treated in the existing Semantic Web standards.\" (<a href=\"zotero://open-pdf/0_6ASERVXA/4\">Motik et al 2012:66</a>)</p>\n<p>\"There are two main b ene ts of using OWL 2 RL. First, the language can readily b e extended with SWRL rules without making the resulting formalism undecidable. This is particularly imp ortant b ecause most conditions for recognising the current context can b e easily expressed as SWRL rules. Second, reasoning with OWL 2 RL ontologies can b e implemented in p olynomial time using techniques known from deductive databases, which is particularly attractive given the constraints on the hardware resources of mobile devices.\" (<a href=\"zotero://open-pdf/0_6ASERVXA/5\">Motik et al 2012:67</a>)</p>\n<p>\"In order to facilitate e cient retrieval, Delta-Reasoner employs three hierarchical indexes: Ispo , Ipos , and Iosp .\" (<a href=\"zotero://open-pdf/0_6ASERVXA/6\">Motik et al 2012:68</a>)</p>",
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            "note": "<p><strong>Extracted Annotations (Wed Dec 18 18:10:53 2013)</strong></p>\n<p><em>This is an important summary article for us.<br /><br />A nice set of tables summarizing reasoners is included. (<a href=\"zotero://open-pdf/0_XC3NSCZS/1\">note on p.339</a>)</em></p>\n<p>&nbsp;</p>\n<p>\"Semantic web reasoners and languages enable the semantic web to function. Some of the latest reasoning models developed in the last few years are: DLP, FaCT, RACER, Pellet, MSPASS, CEL, Cerebra Engine, QuOnto, KAON2, HermiT and others.\" (<a href=\"zotero://open-pdf/0_XC3NSCZS/1\">Mishra and Kumar 2010:339</a>)</p>\n<p>\"The description logic is based upon the formal semantics. It has been designed to mainly represent the knowledge. The origin of description logic lies in the structured inheritance networks and frame-based representations. In this logic, the concepts are given set-theoretic interpretation and the descriptions form hierarchical knowledge using two disjoint alphabets i.e. concepts and roles. Concepts denote unary descriptions and are interpreted as a set of individuals, while roles denote binary descriptions and are interpreted as sets of pairs of individuals.1 The description logic provides the constructs like value restrictions for establishing relationships between concepts. It has mainly two basic inferences on the concept expressions: subsumption and satisfiability. If C and D are two concepts, then the subsump-\" (<a href=\"zotero://open-pdf/0_XC3NSCZS/2\">Mishra and Kumar 2010:340</a>)</p>\n<p>\"tion is denoted by C ⊆ D and is the problem of checking whether the concept denoted by subsumer, D, is more general than the concept denoted by subsume, C. Satisfiability is the problem of checking whether a concept expression does not necessarily denote the empty set.\" (<a href=\"zotero://open-pdf/0_XC3NSCZS/3\">Mishra and Kumar 2010:341</a>)</p>\n<p>\"The knowledge base in description logic typically consists of two components: TBox and ABox. The TBox contains intensional knowledge representing the general knowledge about the problem domain, while the ABox contains the extensional or assertional knowledge representing the knowledge specific to a particular problem or individual of the domain.\" (<a href=\"zotero://open-pdf/0_XC3NSCZS/3\">Mishra and Kumar 2010:341</a>)</p>\n<p>\"Due to the increasing complexity of underlying logic in semantic web languages, the reasoning is becoming intractable in them especially in the worst case. To handle this type of situation, Groot et al. (2005) have presented some approximation techniques for description logic. These can be used for simplifying the reasoning process on semantic web languages particularly OWL. The goal of these techniques is to replace the costly reasoning operations with the small number of cheaper approximation reasoning operations without affecting the results of computation. Various approaches of approximation are as follow: By weakening the logical language in which a theory is encoded, based on the trade-off between the expressiveness and reasoning complexity of logical language. It enables to maintain a trade-off between the completeness of reasoning and run-time. By weakening the notion of logical consequence. By pre-processing the ontology off-line such that online reasoning becomes faster. This can also be accomplished by adding the derived facts to the original theory as axioms and avoiding the need to deduce them again.\" (<a href=\"zotero://open-pdf/0_XC3NSCZS/4\">Mishra and Kumar 2010:342</a>)</p>\n<p>\"Unlike reasoning models in second generation, the reasoners of this generation provide sound and complete reasoning support.\" (<a href=\"zotero://open-pdf/0_XC3NSCZS/14\">Mishra and Kumar 2010:352</a>)</p>\n<p>\"Jena (see footnote 15) is a Java based application tool for building semantic web applications. In addition to providing a programmatic environment for RDF, RDF-S, and OWL, it also includes a built-in rule based reasoner. The built-in reasoner provides semantic entailments for ontologies using OWL-Lite and some constructs from OWL-DL and OWL-Full. However, this reasoner is computationally expensive for handling complex large ontologies. Also, it can not handle somoe modeling primitives provided by OWL-DL. This problem is solved by Jena's architecture by supporting the external reasoner.\" (<a href=\"zotero://open-pdf/0_XC3NSCZS/17\">Mishra and Kumar 2010:355</a>)</p>\n<p>\"DAMLJessKB is a description logic based tool for reasoning on the semantic web, particularly with the DAML. It differs from the other description logic based systems as it uses the production system in inferencing process. Just like RDF, DAMLJessKB represents models as a list of triples, no matter how complex its XML form is. In It is a memory-based system and has limited scalability in data loading. OWLJessKB is considered as complete or almost complete OWL Lite reasoning system (Guo et al. 2005).\" (<a href=\"zotero://open-pdf/0_XC3NSCZS/17\">Mishra and Kumar 2010:355</a>)</p>\n<p>\"OWL is the successor of DAML+OIL.\" (<a href=\"zotero://open-pdf/0_XC3NSCZS/18\">Mishra and Kumar 2010:356</a>)</p>\n<p>\"DAML-S, a successor of OWL It considers the web based system as consisting of different web services. The ontologies written in DAML-S are organized in three parts: profile, model, and grounding. It enables the automation of various web-services related tasks such as discovery, invocation, composition, interoperation, and monitoring. It allows defining and composing processes using various constructs such as sequence, split, unordered, choice, and condition (Martin 2002). The latest release of DAML-S is known as OWL-S. It provides more expressive power and can be used to describe the semantically rich characteristics of services on the web. The ontology described in OWL-S is organized into four parts: profile, process model, grounding, and services. Profile describes the capabilities and discriminating features of the web services. The Process model describes the way of asking for the service and the effect of carrying out the service. The grounding specifies the details of the process with which an agent can access a service by describing the service-specific details such as communication protocol, message formats, port numbers used in contacting the service etc. The services part in the ontology describes a mean of bundling together the instances of top-level profile, process, and grounding classes that are meant to be used together.\" (<a href=\"zotero://open-pdf/0_XC3NSCZS/19\">Mishra and Kumar 2010:357</a>)</p>\n<p>\"SWRL is based on the combination of OWL-DL and OWL-Lite sublanguages of OWL with unary/binary sub-languages of RuleML.20 In SWRL, instead of using arbitrary predicates, the arbitrary OWL-DL descriptions are used in both the head and the body of the rule. A unary predicate corresponds with an OWL class and a binary one corresponds with an OWL property. SWRL is considered to be an un-decidable language due to the reason that it allows existential quantification in the head of the rule (inherited from OWL), combined with chaining variables over predicates (inherited from horn logic). However, it does not allow predicates with higher arity, but n-ary predicates can be encoded in a description logic knowledge-base by “emulating” relational parameters through the introduction of a number of functional properties.\" (<a href=\"zotero://open-pdf/0_XC3NSCZS/19\">Mishra and Kumar 2010:357</a>)</p>",
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            "note": "<p><strong>Extracted Annotations (Wed Dec 18 18:10:43 2013)</strong></p>\n<p><em>Likely not of much use unless we decide to use Racer as a reasoning agent OR we want to get more technical about reasoners. (<a href=\"zotero://open-pdf/0_UJETASF4/1\">note on p.1</a>)</em></p>\n<p>&nbsp;</p>",
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