Your Name:______________________________ Date:______________UTD ID Semantic Web Spring 2014Final ExamTrue or False_T__ 1) OWL Lite is a variant of the OWL language that contains a subset of the OWL Full syntax_F__ 2) In OWL, it is impossible to explicitly declare that two individuals with different URIs are the same_T__ 3) Many Semantic Web frameworks like Jena are designed with modularity in mind, such that a user can conveniently switch between RDF store implementations or reasoner implementations depending upon the use case at hand_F__ 4) Forward chaining in knowledgebases is advantageous when you care more about insertionand deletion queries than you do about retrieval queries_F__ 5) The list of 3-grams of the word "semantic" are 'sem', 'ema', 'ant', 'nti', and 'tic'_F__ 6) The Stanford NER is a tool that can be used to extract "Person" and "Location" entities well, but it is not as effective at extracting "organization" entities_T__ 7) The relation "less than" is an example of a relation that is irreflexive_T__ 8) OWL EL is a variant of OWL that restricts the allowable syntax in the language to make reasoning faster, and it guarantees polynomial time computation to map individuals to classes and to check ontology consistency_T__ 9) To compute the semantic distance between two concepts, you need to solve the formula: 1 – (semantic similarity between two concepts)_T__ 10) When defining an ontology, you can use owl:imports to import other ontologies into the current ontology Semantic Web Vocabularies, Tools and Sites (3 points each)For each problem below, provide the one, best answer from the table (items in table may be used 0+ times)A – LUBM E – WordNetB – Cyc F – Stanford NERC – Pellet G – Basic Formal OntologyD – Sesame H – Fuseki/Joseki_B__ 11) Very large ontology representing human behavior_D__ 12) Semantic Web framework implemented in Java whose popularity rivals that of Jena_E__ 13) Connected graph of words used in semantic similarity calculations_F__ 14) Tool used to identify different entities in text strings G 15) Represents concepts as being either SPAN or SNAP, based upon their time duration H 16) Open source software allowing one to administer their own SPARQL endpoint1OWL Modelling (Answer the following Questions)(17: What does problem1:thisClass represent? Describe the membership of this class in words.problem1:thisClass rdfs:subClassOf problem1:Professor; rdfs:subClassOf [ rdf:type owl:Restriction; owl:onProperty problem1:teachesPartTime; owl:hasValue "true" ]. rdfs:subClassOf [ rdf:type owl:Restriction; owl:minCardinality 1; owl:onProperty problem1:teachesClass ].Answer: problem1:thisClass consists of individuals that are Professors that teach part time and teach at least 1 class (18: What does problem2:thisClass represent? Describe the membership of this class in words.Problem2:PetsOfJeff rdf:type owl:Class;owl:unionOf ( Problem2:SparklesTheDog Problem2:ShadowTheCat Problem2:GeorgeTheTurtle [ rdf:type owl:Class; owl:oneOf ( Problem2:StrayCat1 Problem2:StrayCat2 Problem2:StrayCat3 ) ] [ rdf:type owl:Restriction; owl:onProperty Problem2:friendsWith; owl:hasValue Problem2:AceTheWonderDog ]).Answer: problem2:PetsOfJeff describes individuals = {Problem2:SparklesTheDog, Problem2:ShadowTheCat, Problem2:GeorgeTheTurtle, one of Problem2:StrayCat1 Problem:StrayCat2Problem2:StrayCat3, and any individuals that are friends with AceTheWonderDog} 2(19: What values are described by the datatype defined below?[] rdf:type rdfs:Datatype; owl:unionOf ( [ rdf:type rdfs:Datatype; owl:onDatatype xsd:string; owl:withRestrictions ( [ xsd:pattern “[A-Za-z]*Semantic Web[A-Za-z]+”; ] ) ] [ rdf:type rdfs:Datatype; owl:onDatatype xsd:string; owl:withRestrictions ( [ xsd:pattern “[A-Za-z]*Data Science[A-Za-z]+”; ] ) ] )Answer: the datatype consists of all strings containing either the substring “Semantic Web” or the substring “Data Science” (20: You are given the following 2 ontologies describing soup and some notes about the ontology. Using this information, along with any semantic similarity formulas given to you, answer the questions below.Ontology O1>Soup >> Chicken >> Italian-Wedding-Style >> Cheese >> Vegetable >>> Onion >>> Pea >>> Potato >> Gumbo Notes:3Ontology O2>Soup >> Chicken-Noodle >> Italian-Wedding >> Cheese-Broccoli >> Veggie >>> Spinach >>> Bean >>> Split-Pea >> Gumbo- Each word above within an ontology represents a label for a concept. For example, ‘Cheese’ in O1 represents the concept with the label “Cheese”-In O1, any concept with a ‘>>’ to its left is a child of the concept labeled “Soup”. Also, “Spinach” and “Bean” are children of “Veggie”. >, >> and >>> have the same meaning among the concepts in O2. - Italian Wedding-Style and Italian Wedding are two different names for the same kind of soup(a: Looking at the two ontologies and their concept labels, which of the following matching techniques would be most suitable in calculating the semantic similarity between a given concept in O1and a given concept in O2? (a: Hamming Distance (b: Jaro similarity X (c: WordNet similarity measures (d: Stanford NER-based entity extraction(b: Calculate the Hamming distance between the concept “Chicken” in O1 and the concept “Chicken-Noodle” in O2. Please show your work.Hamming Distance = Hamming Distance = (0 + 7) / 14 = 0.5(c: Calculate the N-gram similarity between the combined labels of all child concepts of “Vegetable”