308 Y. Blanco-Fernandez et al. Knowledge- Based Systems 21(2008)305-320 gested. In order to guarantee the computational viability, The properties are the key elements of our semantic rea we use a controlled inference mechanism that explores soning methodology. Specifically, our approach explores the knowledge base by selecting instances significant to both these properties and the instances(and classes) joined the user and omitting those which are totally irrelevant. by them, with the goal of uncovering meaningful semantic Thanks to this mechanism, our methodology guarantees associations hidden in the knowledge base. To this aim, we that: (i)the discovered associations find programs appeal- adopt the notion of property sequence defined in the Sem- ing to the user, and (ii) the reasoning process adapts as Dis project his/her preferences evolve. This way, our content-based strategy achieves a balance between the diversification 3. 2. Property sequences and the personalization of the offered recommendations Before detailing the reasoning methodology applied in Let C and p be the respective sets of all classes and all AVATAR, the next section describes: (i) the TV ontology properties defined in our ontology. Given a property used in this system and (ii) the profiles that store the view- PE P, its domain( denoted by domain(P)) limits the enti ers'preferences, modeled from the knowledge represented ties of C to which P can be applied, and its range( denoted in the ontology by range(P)) indicates the entities of C that P may take its value 3. The reasoning framework In [1] a property sequence PS is defined as a finite set of 3.1. The Tv ontolog properties [PI,., PN] that join several classes defined in the ontology. This can be formally expressed as follows: Since AVATAR is a TV recommender system, methodology requires an ontology that formalizes the BS={P1,…,PMP∈PW1≤j≤N, cepts and relationships typical in the Tv domain. This range()=domain(Pa+1)VI<i<NI information has been extracted from TV-Anytime [41]. specification that provides detailed semantic descriptions Example 1. For instance, in Fig. I, it is possible to identify about generic audiovisual programs the property sequence PS=HASACTOR, ACTORIN, Our TV ontology has been implemented in OWL. Spe- HASTOPICIjoining the classes Adventure Movies, Starring cifically, it includes a set of classes(representing program Actors, Drama Movies, and War Topics enres, topics, credits, geographical and temporal informa tion,etc. )and properties that establish relationships among An instance of Ps(denoted by ps)is defined as the set of them. Besides, our ontology defines hierarchical relation properties that join specific instances of the classes chips among classes, and among properties. In fact, it con- contained in PS. We use Y to represent that tains several hierarchies defined from the Tv-Anytime y is an instance of Y, being Y a entity(class or property metadata: hierarchies of genres(action movies, nature doc in the ontology. According to this notation, given umentaries,sports,etc.),hierarchies of topics(war, travel, PS=[PI,., PNl, we define its instance ps as follows (countries, cities, etc. ) hierarchies of credits(actors, direc- Ps=(P1,.., Pwl/p-P,VI<J<NS (2) tors, hosts, etc. ), etc L In order to reason about specific TV programs and to Example 2. In Fig. I, it also is possible to identufy an er semantic associations among them, it is necessary to add specific instances of the classes and properties defined In this case, the instance ps =[HasActor, ActorIn, Has Top- in the OwL ontology. Specifically, the Tv programs are ic] links the nodes Cast Away, Tom Hanks, Saving Private represented as instances of the classes defined in the hierar- Rvan, and World War II. chy of genres, and each one is given a unique reference (henceforth, ID). The semantic attributes of these pro- O approac. [l] the following grams(topics, geographical and temporal information, definitions credits, etc )are also defined as instances belonging to the remaining hierarchies of classes mentioned before. These (1) The origin and the terminus of a sequence are the first characteristics are linked to each program by means of and the last nodes contained in it, respectively properties, as shown in the excerpt of the ontology repre (1. 1)As X E C can be the origin of several property sented in Fig. I sequences, we use Ps to identify a sequence originated in X Note that owl Object Property identifies a property between two nodes labeled with a name, whereas rdf type of represents the relationshi between a class and one of its instances. For simplicity, we omitted some 7 For simplicity, we use the term property sequence to refer both to the classes and rdf typeof links in Fig. I(e.g. links between some specific sequences and to their instances. The sequences actors and the class Starring Actors) uppercase letters(i.e. PS), and their instances by lowercase ones (ps)gested. In order to guarantee the computational viability, we use a controlled inference mechanism that explores the knowledge base by selecting instances significant to the user and omitting those which are totally irrelevant. Thanks to this mechanism, our methodology guarantees that: (i) the discovered associations find programs appeal￾ing to the user, and (ii) the reasoning process adapts as his/her preferences evolve. This way, our content-based strategy achieves a balance between the diversification and the personalization of the offered recommendations. Before detailing the reasoning methodology applied in AVATAR, the next section describes: (i) the TV ontology used in this system and (ii) the profiles that store the view￾ers’ preferences, modeled from the knowledge represented in the ontology. 3. The reasoning framework 3.1. The TV ontology Since AVATAR is a TV recommender system, our methodology requires an ontology that formalizes the con￾cepts and relationships typical in the TV domain. This information has been extracted from TV-Anytime [41], a specification that provides detailed semantic descriptions about generic audiovisual programs. Our TV ontology has been implemented in OWL. Spe￾cifically, it includes a set of classes (representing program genres, topics, credits, geographical and temporal informa￾tion, etc.) and properties that establish relationships among them. Besides, our ontology defines hierarchical relation￾ships among classes, and among properties. In fact, it con￾tains several hierarchies defined from the TV-Anytime metadata: hierarchies of genres (action movies, nature doc￾umentaries, sports, etc.), hierarchies of topics (war, travel, disasters, etc.), hierarchies of geographical information (countries, cities, etc.), hierarchies of credits (actors, direc￾tors, hosts, etc.), etc. In order to reason about specific TV programs and to infer semantic associations among them, it is necessary to add specific instances of the classes and properties defined in the OWL ontology. Specifically, the TV programs are represented as instances of the classes defined in the hierar￾chy of genres, and each one is given a unique reference (henceforth, ID). The semantic attributes of these pro￾grams (topics, geographical and temporal information, credits, etc.) are also defined as instances belonging to the remaining hierarchies of classes mentioned before. These characteristics are linked to each program by means of properties, as shown in the excerpt of the ontology repre￾sented in Fig. 1. 6 The properties are the key elements of our semantic rea￾soning methodology. Specifically, our approach explores both these properties and the instances (and classes) joined by them, with the goal of uncovering meaningful semantic associations hidden in the knowledge base. To this aim, we adopt the notion of property sequence defined in the Sem￾Dis project. 3.2. Property sequences Let C and P be the respective sets of all classes and all properties defined in our ontology. Given a property P 2 P, its domain (denoted by domain (P)) limits the enti￾ties of C to which P can be applied, and its range (denoted by range (P)) indicates the entities of C that P may take as its value. • In [1], a property sequence PS is defined as a finite set of properties [P1,...,PN] that join several classes defined in the ontology. This can be formally expressed as follows: PS ¼ f½P1; ... ; P N =Pj 2 P 8 1 6 j 6 N; range ðPiÞ ¼ domainðPiþ1Þ 8 1 6 i < Ng ð1Þ Example 1. For instance, in Fig. 1, it is possible to identify the property sequence PS = [HASACTOR, ACTORIN, HASTOPIC] joining the classes Adventure Movies, Starring Actors, Drama Movies, and War Topics. • An instance of PS (denoted by ps) is defined as the set of properties that join specific instances of the classes contained in PS. 7 We use y ! rdf :typeOf Y to represent that y is an instance of Y, being Y a entity (class or property) in the ontology. According to this notation, given PS = [P1,...,PN], we define its instance ps as follows: ps ¼ f½p1; ... ; pN =pj ! rdf :typeOf Pj 8 1 6 j 6 Ng ð2Þ Example 2. In Fig. 1, it also is possible to identify an instance of the property sequence PS used in Example 1. In this case, the instance ps = [HasActor, ActorIn, HasTop￾ic] links the nodes Cast Away, Tom Hanks, Saving Private Ryan, and World War II. Our approach also borrows from [1] the following definitions: (1) The origin and the terminus of a sequence are the first and the last nodes contained in it, respectively. (1.1) As X 2 C can be the origin of several property sequences, we use PSX to identify a sequence originated in X: 6 Note that owl:ObjectProperty identifies a property between two nodes labeled with a name, whereas rdf:typeOf represents the relationship between a class and one of its instances. For simplicity, we omitted some classes and rdf:typeof links in Fig. 1 (e.g. links between some specific actors and the class Starring Actors). 7 For simplicity, we use the term property sequence to refer both to the sequences and to their instances. The sequences are represented by uppercase letters (i.e. PS), and their instances by lowercase ones (ps). 308 Y. Blanco-Ferna´ndez et al. / Knowledge-Based Systems 21 (2008) 305–320