Ελληνικό Ανοικτό Πανεπιστήμιο-Ιόνιο Πανεπιστήμιο

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1 Ελληνικό Ανοικτό Πανεπιστήμιο-Ιόνιο Πανεπιστήμιο Διαπανεπιστημιακό Μεταπτυχιακό Πρόγραμμα Σπουδών «Βιοπληροφορική και Νευροπληροφορική» Διπλωματική Εργασία «Δημιουργία Προσωποποιημένων Συνόψεων Σημασιολογικών Βιολογικών Βάσεων Δεδομένων» Αλέξανδρος Γιακουμάκης Επιβλέπων καθηγητής: Χαρίδημος Κονδυλάκης Πάτρα, Ιούνιος 2021

2 H παρούσα εργασία αποτελεί πνευματική ιδιοκτησία του φοιτητή («συγγραφέας/δημιουργός») που την εκπόνησε. Στο πλαίσιο της πολιτικής ανοικτής πρόσβασης ο συγγραφέας/δημιουργός εκχωρεί στο ΕΑΠ, μη αποκλειστική άδεια χρήσης του δικαιώματος αναπαραγωγής, προσαρμογής, δημόσιου δανεισμού, παρουσίασης στο κοινό και ψηφιακής διάχυσής τους διεθνώς, σε ηλεκτρονική μορφή και σε οποιοδήποτε μέσο, για διδακτικούς και ερευνητικούς σκοπούς, άνευ ανταλλάγματος και για όλο το χρόνο διάρκειας των δικαιωμάτων πνευματικής ιδιοκτησίας. Η ανοικτή πρόσβαση στο πλήρες κείμενο για μελέτη και ανάγνωση δεν σημαίνει καθ οιονδήποτε τρόπο παραχώρηση δικαιωμάτων διανοητικής ιδιοκτησίας του συγγραφέα/δημιουργού ούτε επιτρέπει την αναπαραγωγή, αναδημοσίευση, αντιγραφή, αποθήκευση, πώληση, εμπορική χρήση, μετάδοση, διανομή, έκδοση, εκτέλεση, «μεταφόρτωση» (downloading), «ανάρτηση» (uploading), μετάφραση, τροποποίηση με οποιονδήποτε τρόπο, τμηματικά ή περιληπτικά της εργασίας, χωρίς τη ρητή προηγούμενη έγγραφη συναίνεση του συγγραφέα/δημιουργού. Ο συγγραφέας/δημιουργός διατηρεί το σύνολο των ηθικών και περιουσιακών του δικαιωμάτων.

3 «Δημιουργία Προσωποποιημένων Συνόψεων Σημασιολογικών Βιολογικών Βάσεων Δεδομένων» Αλέξανδρος Γιακουμάκης Επιτροπή Επίβλεψης Πτυχιακής / Διπλωματικής Εργασίας Επιβλέπων Καθηγητής: Χαρίδημος Κονδυλάκης Συνεργαζόμενος Ερευνητής, Ίδρυμα Τεχνολογίας και Έρευνας Συν-Επιβλέπων Καθηγητής: Φοίβος Μυλωνάς Αναπληρωτής Καθηγητής, Ιόνιο Πανεπιστήμιο. Πάτρα, Ιούνιος 2021

4 «Με την ολοκλήρωση της μεταπτυχιακής διπλωματικής μου εργασίας, θα ήθελα να εκφράσω τις θερμές μου ευχαριστίες καταρχάς στον επιβλέποντα καθηγητή μου, κ. Χαρίδημο Κονδυλάκη, για την εμπιστοσύνη που μου έδειξε εξ αρχής, αναθέτοντάς μου το συγκεκριμένο θέμα, την επιστημονική του καθοδήγηση, τις υποδείξεις του, τη ενεργή συμμετοχή του όταν χρειάστηκε, την απλότητα και αμεσότητα που έδειξε από την αρχή μέχρι το τέλος. Επίσης, ιδιαίτερες ευχαριστίες θα ήθελα να απευθύνω στον υποψήφιο διδάκτορα και συνάδελφο Πληροφορικό & Χημικό, κ. Γιάννη Βασιλείου για τη υποστήριξη και βοήθειά του, κατά τη διάρκεια της ερευνητικής διαδικασίας.» Διπλωματική Εργασία 4

5 Περίληψη Η ολοένα και αυξανόμενη πληθώρα βιολογικών πληροφοριών που συλλέγονται καθημερινά, καθιστά επιτακτική την διασύνδεση των πληροφοριών, και την δημιουργία μεθόδων για την αποτελεσματική τους εξερεύνηση. Παρά το γεγονός ότι τεχνολογίες του σημασιολογικού ιστού προσφέρουν ποικίλες υπηρεσίες για την διασύνδεση και τον διαμοιρασμό των δεδομένων ως ανοικτά διασυνδεδεμένα δεδομένα, έχουν συχνά εξαιρετικά περίπλοκα και μεγάλα σχήματα, τα οποία είναι δύσκολο να κατανοηθούν, περιορίζοντας το δυναμικό εκμετάλλευσης των πληροφοριών που περιέχουν. Ως αποτέλεσμα, υπάρχει μια αυξανόμενη ανάγκη για ανάπτυξη μεθόδων και εργαλείων που διευκολύνουν τη γρήγορη κατανόηση και εξερεύνηση αυτών των πηγών. Τα τελευταία χρόνια τεχνικές συνόψεων ολοένα και περισσότερο κερδίζουν έδαφος για την εύκολη και γρήγορη αποτύπωση της διαθέσιμης πληροφορίας σημασιολογικών βάσεων δεδομένων. Ωστόσο μέχρι τώρα δεν έχει ενδελεχώς μελετηθεί πως οι τεχνικές αυτές θα μπορέσουν να εφαρμοστούν αποτελεσματικά σε βιολογικές βάσεις δεδομένων. Επιπλέον οι χρήστες για τους οποίους δημιουργούνται οι συνόψεις δεν μπορούν να επιλέξουν το τμήμα του γράφου που τους ενδιαφέρει καθιστώντας τις παραγόμενες συνόψεις μη προσωποποιημένες. Στη συγκεκριμένη εργασία λοιπόν, μελετώνται οι υφιστάμενες τεχνικών συνόψεων για διασυνδεδεμένα δεδομένα, και παρουσιάζεται ένας νέος αλγόριθμος κατάλληλος για την σύνοψη βιολογικών βάσεων δεδομένων. Σε αντίθεση με άλλες αντίστοιχες εργασίες ο αλγόριθμος επιλέγει τριπλέτες από τον διαθέσιμο γράφο με βάση τις προτιμήσεις του χρήση και δημιουργεί συνόψεις υψηλού επιπέδου. Η πειραματική μελέτη επιβεβαιώνει την υψηλή ποιότητα των παραγόμενων συνόψεων δείχνοντας ότι επιτυγχάνουν να επιλέξουν τριπλέτες που οι χρήστες ρωτάνε συχνά με βάση τα ενδιαφέροντά τους. Λέξεις Κλειδιά Σημασιολογικός Ιστός, σύνοψη, βιολογικές βάσεις δεδομένων, διασυνδεδεμένα δεδομένα. Διπλωματική Εργασία 5

6 «Creation of Personalised Summaries in Semantic Biological Databases» Alexandros Giakoumakis Abstract The increasing abundance of biological information that is collected daily in medical and biological research, makes it imperative to interconnect the information and to create methods for their effective exploration. Although semantic web technologies offer a variety of services for interconnecting and sharing data as openly interconnected data, they often have extremely complex and large schemes that are difficult to understand, limiting the exploitation potential of the information they contain. As a result, there is a growing need to develop methods and tools that facilitate the rapid understanding and exploration of these resources. In recent years, summary techniques are increasingly gaining ground for the easy and fast mapping of available semantic database information. However, so far it has not been thoroughly studied how these techniques can be applied effectively in biological databases. In addition, in existing techniques, users cannot select the part of the graph that the users are mostly interested making the summaries nonpersonalized. The aim of this master's thesis is to investigate the available technical summaries for linked data, presenting a new algorithm for biological datasets. In contrast to other similar approaches the algorithm selects triples from the available graph based on user preferences, generating high quality summaries. Experimental evaluation verifies the quality of the generated summaries, showing that it achieves to select triples that users are frequently querying based on their interests. Keywords Sematic Web, Summary, Biological Databases, Interconnected Data. Διπλωματική Εργασία 6

7 Contents Περίληψη v Abstract vi Contents vii Pictures/Shapes Catalogue viii Tables Catalogue ix Abbreviation x 1 Introduction The problem Contribution 11 2 Preliminaries - RDF Labelled directed graphs: core concepts The resource description framework (RDF) Notations RDF schema (RDFS) RDF entailment Saturation Instance and schema graph Properties and attributes of an RDF graph BGP queries Notations Query evaluation Query answering 17 3 Related work 17 4 Approach High level diagram of algorithm The algorithm in detail Running example (Goslim Ontology) 22 5 Evaluation Metrics Datasets Results 25 6 Conclusions 27 7 Bibliography 28 8 Appendix Α: «Algorithm in Java Code» 31 9 Appendix Β: «Example of Results of Executing Code» Appendix C: «Software / System Requirements» 68 Διπλωματική Εργασία 7

8 Figures/Shapes Catalogue Figure 2.1 Sample edge-labeled directed graphs 12 Figure RDF graph and its implicit triples 14 Figure 3 A taxonomy of the works in the area from a survey 18 Figure Overall visualization of the Goslim Ontology. 22 Figure User s Inputs of the running example 23 Figure Visualization of the synopsis top10 output by running the algorithm 23 Shape High level diagram of algorithm 20 Shape Coverage of Summaries DBPEDIA 25 Shape Coverage of Summaries SWDF 26 Διπλωματική Εργασία 8

9 Tables Catalogue Table 2.2 RDF (top) and RDFS (bottom) statements 13 Table Description of algorithm s workflow 19 Διπλωματική Εργασία 9

10 Abbreviation : ΔΕ ΕΑΠ ΘΕ ΠΕ ΠΣ ΣΥΝ Διπλωματική Εργασία Ελληνικό Ανοικτό Πανεπιστήμιο Θεματική Ενότητα Πτυχιακή Εργασία Πρόγραμμα Σπουδών Συντονιστής Διπλωματική Εργασία 10

11 1. Introduction Knowledge bases now widely available on the web, have in many cases extremely complex and large schemas, which are difficult to comprehend, limiting the exploitation potential of the information they contain. One method for condensing and simplifying such datasets is through semantic summaries. According to a recent survey [1], a semantic summary is a compact information, extracted from the original RDF graph, intuitively; summarization is a way to extract meaning from data while reducing its size, and/or a graph, which some applications can exploit instead of the original graph to perform certain tasks more efficiently, e.g. query answering [2], view selection [3] and indexing. Structural summaries, a subcategory of semantic summaries, focus first and foremost on the graph structure, respectively the paths and sub-graphs one encounters in the RDF graph. They can be further classified to the quotient and non-quotient methods. Quotient methods allow characterizing some graph nodes as equivalent in a certain way, and then summarizing a graph by assigning a representative to each class. In this paper we focus on non-quotient summaries as we are interested on selecting the most important graph nodes, maximizing summary s utility for query answering. State of the art works in the area of non-quotient structural summarization, try to identify the most important nodes of the RDF/S graph, and then to optimally link those, producing a connected sub-graph. As such, the size of the presented graph is reduced to a minimum size, so that end-users are easier to understand the contents of the generated summary, while in parallel the most important nodes are selected and presented to the user. 1.1 The problem The problem with the state of the art, non-quotient structural semantic summaries is that in most of the cases are oblivious to the interests of the users. They try to identify the most important parts of the graph combining several important metrics [3, 4]. However, it is common for a dataset to contain an extensive amount of data, and users to focus only on a specific part of the dataset. For example, DBpedia v3.8 includes more than 400 classes, however, in a user log provided by the DBpedia endpoint with more than 56k queries only 177 classes are used. Although in the past, several approaches have pointed out the importance of user preferences in formulating these summaries [6, 7], so far incorporating user preferences is neither explored in detail, nor evaluated. Previous approaches, require either the explicit specification of weights by the users on graph edges to capture user preferences [6, 7], or try to indirectly identify them using proxies, such as the number of instances available for class nodes [8, 9]. 1.2 Contribution In this work, we present an approach for constructing high quality summaries based on the idea of selecting the most important triples of a knowledge graph based on user s selected nodes, in order to capture user preferences. To select the most important triples our algorithm finds the triples that include user selections and ranks them based on the triple s importance. Then the algorithm selects the top-k most important ones to be included in the summary. We experimentally evaluate our approach using real world datasets (DBPedia and SWDF) and show that the produced summaries enjoy a high quality, achieving to include triples that are frequently queried together with the selected nodes. To the best of our knowledge, we are the Διπλωματική Εργασία 11

12 first to select the most important triples based on user preferences for constructing structural, non-quotient semantic summaries. 2. Preliminaries RDF We recall here the core concepts and notations related to RDF graphs. At a first glance, these can be considered particular cases of labelled, oriented graphs, and indeed classical graph summarization techniques have been directly adapted to RDF; we recall them in Sect Then, we present RDF graphs in Sect. 2.2, where we introduce the terminology and specific constraints which make up the RDF standard, established by the W3C; we also introduce here ontologies, which play a central role in most RDF applications, with a focus on the simple RDF Schema ontology language. From a database perspective, the most common usage of RDF graphs is through queries; therefore, we recall the Basic Graph Pattern (BGP) dialect at the core of the SPARQL RDF query language in Sect Labelled directed graphs: core concepts Labelled directed graphs are the core concept allowing to model RDF datasets. Further, most (not all) proposals for summarizing an RDF graph also model the summary as a directed graph. Thus, without loss of generality, we will base our discussion on this model. Note that it can be easily generalized to more complex graphs, e.g., those with (multi-) labeled nodes. Given a set A of labels, we denote by G = (V, E) an A edge labeled directed graph whose vertices are V, and whose edges are E V A V. Figure 2.1 displays two such graphs; A edge labels are attached to edges. Node labels will be used/explained shortly in our discussion. In addition, the notions of graph homomorphism and graph isomorphism frequently appear in graph summary proposals: Definition 1 (Homomorphism and isomorphism) Let G = (V, E) and G = (V, be two A-edgelabeled directed graphs. A function φ: V V is a homomorphism from G to G if for every edge (v1,l,v2) E there is an edge (φ(v1),l,φ(v2) G. If, moreover, φ is a bijection, and its inverse φ 1 is also a homomorphism from G into G, then φ is an isomorphism. Fig. 2.1 Sample edge-labeled directed graphs Διπλωματική Εργασία 12

13 A homomorphism from G to G ensures that the graph structure present in G has an image into G. For our discussion, this is interesting in three different settings: 1. If G is a data graph and G is a summary graph representing G, a homomorphism from G to G ensures that every subgraph of G has an image in G. 2. Conversely, a homomorphism from a summary graph G into the data graph G means that all the graph structures present in the summary also appear in the data graph. 3. If Q is a graph query, e.g., expressed in SPARQL, and G is a data graph, e.g., an RDF graph, the answer to Q on G, denoted Q(G), is exactly defined through the set of homomorphisms which may be established from G to G. Together with the two items above, this leads to several interesting relationships between queries, data graphs and their summaries, in particular allowing to use the summary to gain some knowledge about Q(G) without actually evaluating it. Observe that while homomorphisms between a graph and its summary have useful properties, an isomorphism would defeat the purpose of summarization, as two isomorphic graphs would have the same size. In Fig. 2.1, the graph shown on the right is homomorphic to that on the left. Indeed, a homomorphism maps each node from the graph at left into the right graph node whose label contains its number. Throughout this document, unless otherwise specified, N denotes the number of nodes and M the number of edges of a directed graph input to some summarization approach. 2.2 The resource description framework (RDF) Our study of graph summarization techniques is centrally motivated by their interest when summarizing RDF graphs. RDF is the standard data model promoted by the W3C for Semantic Web applications. Assertion Triple Relational notation Class (s, rdf:type, o) o(s) Property (s,p,o) p(s,o) Constraint Triple OWA interpretation Subclass (s, sc, o) s o Subproperty (s, sp, o) s o Domain typing (p, d, o) Πdomain(s) o Range typing (p, r, o) Πrange(s) o Table 2.2 RDF (top) and RDFS (bottom) statements Διπλωματική Εργασία 13

14 An RDF graph (in short a graph) is a set of triples of the form (s, p, o). A triple states that a subject s has the property p, and the value of that property is the object o. We consider only well-formed triples, as per the RDF specification [10], belonging to (U B) U (U B L) where U is a set of Uniform Resource Identifiers (URIs), L a set of typed or untyped literals (constants), and B a set of blank nodes (unknown URIs or literals); U, B, L are pairwise disjoint. Blank nodes are essential features of RDF allowing to support unknown URI/literal tokens. These are conceptually similar to the labeled nulls or variables used in incomplete relational databases [11], as shown in [12]. As described above, it is easy to see that any RDF graph is a labeled graph as described in Sect However, as we explain below, RDF graphs may contain an ontology, that is, a set of graph edges to which standard ontology languages attach a special interpretation. The presence of ontologies raises specific challenges when summarizing RDF graphs, which do not occur when only plain data graphs are considered Notations We use s, p, and o as placeholders for subjects, properties and objects, respectively. Literals are shown as strings between quotes, e.g., string. Table 2.2 (top) shows how to use triples to describe resources, that is, to express class (unary relation) and property (binary relation) assertions. The RDF standard [13] has a set of built-in classes and properties, as part of the rdf: and rdfs: pre-defined namespaces. We use these namespaces exactly for these classes and properties, e.g., rdf:type specifies the class(es) to which a resource belongs. For brevity, we will sometimes use τ to denote rdf:type. Example 1 (RDF graph) For example, the following RDF graph G describes a book, identified by doi1, its author (a blank node _:b1 whose name is known), title and date of publication: {(doi 1,rdf:type,Book), (doi 1,writtenBy,_:b 1), (doi 1,hasTitle, LePortdesBrumes), G = (_:b 1,hasName, G. Simenon), (doi 1,publishedIn, Publication rdfs:domain hasauthor rdfs:subclassof rdfs:subpropertyof 1932 rdf:type Book rdfs:domain writtenby publishedin rdf:type rdfs:range doi 1 writtenby hasauthor : b 1 rdf:type Person hastitle hasname LePortdesBrumes G. Simenon Fig RDF graph and its implicit triples Διπλωματική Εργασία 14

15 2.2.2 RDF schema (RDFS) RDFS allows enhancing the assertions made in an RDF graph with the use of an ontology, i.e., by declaring semantic constraints between the classes and the properties they use. Table 2.2 (bottom) shows the four main kinds of RDFS constraints, and how to express them through triples hence particular graph edges. For concision, we denote the properties expressing subclass, subproperty, domain and range constraints by the symbols sc, sp, d and r, respectively. Here, domain denotes the first, and range the second attribute of every property. The RDFS constraints depicted in Table 2.2 are interpreted under the open-world assumption (OWA) [1], i.e., as deductive constrain Table 2.2 ts. For instance, if the triple (hasfriend, d,person) and the triple (Anne, hasfriend, Marie) hold in the graph, then so does the triple (Anne,τ,Person). The latter is due to the domain constraint in Table 2.2. Example 2 (RDF graph with an RDFS ontology) Assume that the RDF graph G in the preceding example is extended with the RDFS ontological constraints: (Book, sc, Publication), (writtenby, sp, hasauthor), (writtenby, d, Book) and (writtenby, r, Person). The resulting graph is depicted in Fig Its implicit triples are those represented by dashedline edges RDF entailment An important feature of RDF graphs are implicit triples. Crucially, these are considered part of the RDF graph even though they are not explicitly present in it, e.g., the dashed-line G edges in Fig , hence require attention for RDF graph summarization. W3C names RDF entailment the mechanism through which, based on a set of explicit triples and some entailment rules, implicit RDF triples are derived. We denote by i RDF immediate entailment, i.e., the process of deriving new triples through a single application of an entailment rule. More generally, a triple (s,p,o) is entailed by a graph G, denoted G RDF (s,p,o), if and only if there is a sequence of applications of immediate entailment rules that leads from G to (s,p,o) (where at each step, triples previously entailed are also taken into account) Saturation The immediate entailment rules allow defining the finite saturation (a.k.a. closure) of an RDF graph G, which is the RDF graph G defined as the fixed-point obtained by repeatedly applying i RDF rules on G. The saturation of an RDF graph is unique (up to blank node renaming), and does not contain implicit triples (they have all been made explicit by saturation). An obvious connection holds between the triples entailed by a graph G and its saturation: G RDF (s,p,o) if and only if (s,p,o) G. RDF entailment is part of the RDF standard itself; in particular, the answers to a query posed on G must take into account all triples in G [14], since in the presence of RDF Schema constraints, the semantics of an RDF graph is its saturation [15]. As a result, the summarization of an RDF graph should reflect its saturation, e.g., by summarizing the saturation of the graph instead of the graph itself. Διπλωματική Εργασία 15

16 Example 3 (RDF entailment and saturation) The saturation of the RDF graph comprising RDFS constraints G, displayed in Fig , is the graph G obtained by adding to G all its implicit triples that can be derived through RDF entailment, i.e., the graph G in which the implicit/dashed edges are made explicit/solid ones. We introduce below a few more notions we will need in order to describe existing RDF summarization proposals Instance and schema graph An RDF instance graph is made of assertions only (recall Table 2.2), while an RDF schema graph is made of constraints only (i.e., it is an ontology). Further, an RDF graph can be partitioned into its (disjoint) instance and schema subgraphs Properties and attributes of an RDF graph While this is not part of the W3C standard, some authors use attribute to denote a property (other than those built in the RDF and RDFS standards, such as, d etc.) of an RDF resource such that the property value is a literal. In these works, the term property is reserved for those RDF properties whose value is an URI. Example 4 (Instance, schema, properties and attributes of an RDF graph) The RDF graph G shown in Table 2.2 consists of the RDF schema graph comprising the blue triples, and of the RDF instance graph comprising the black triples. Further, within this G instance subgraph, the properties considered attributes are the following: publishedin, hastitle and hasname. 2.3 BGP queries SPARQL is the standard W3C query language used to query RDF graphs. We consider its popular conjunctive fragment consisting of Basic Graph Pattern (BGP) queries. Subject of several recent works [16,17,18,19,20], BGP queries are also the most widely used in real-world applications [21, 19]. A BGP is a generalization of an RDF graph in which variables may also appear as subject, property and object of triples Notations In the following we use the conjunctive query notation Q(x ):- t1,...,tα,where{t1,...,tα}is a BGP. The head of Q is Q(x ), and the body of Q is t1,...,tα. The query head variables x are called distinguished variables, and are a subset of the variables occurring in t1,...,tα; for boolean queries x is empty. We denote by VarBl(Q) the set of variables and blank nodes occurring in the query Q. In the sequel, we will use x, y, z, etc. to denote variables in queries. Διπλωματική Εργασία 16

17 2.3.2 Query evaluation Given a query Q(x ):- t1,...,tα and an RDF graph G, the evaluation of Q against G is: Q(G) = {Φ(x ) Φ : VarBl(Q) Val(G) is a Q to G homomorphism such that {Φ(t1),...,Φ(tα)} G} where we denote by Φ(t) (resp. Φ(x )) the result of replacing every occurrence of a variable or blank node e VarBl(Q) in the triple t (resp. the distinguished variables x ), by the value Φ(e) Val(G) Query answering The evaluation of Q against G uses only G s explicit triples, thus may lead to an incomplete answer set. The (complete) answer set of Q against G is obtained by the evaluation of Q against G, denoted by Q(G ). Example 5 (Query evaluation versus answering) The query below asks for the author s name of Le Port des Brumes : Q(x3):- (x1,hasauthor, x2), (x2,hasname, x3) (x1,hastitle, LePortdesBrumes) Its answer against the explicit and implicit triples of our sample graph is: Q(G ) = { G. Simenon}. Note that evaluating Q only against G leads to the empty answer, which is obviously incomplete. 3. Related work From a scientific viewpoint, existing summarization proposals are most meaningfully classified according to the main algorithmic idea behind the summarization method: 1. Structural methods. Structural methods are those which consider first and foremost the graph structure, respectively the paths and subgraphs one encounters in the RDF graph. Given the prominence of applications and graph uses, where structural conditions are paramount, graph structure is prominently used in summarization techniques. a. Quotient A particular natural concept when building summaries is that of quotient graphs (Definition 2). They allow characterizing some graph nodes as equivalent in a certain way, and then summarizing a graph by assigning a representative to each class of equivalence of the nodes in the original graph. A particular feature of structural quotient methods is that each graph node is represented by exactly one summary node, given that one node can only belong to one equivalence class. b. Non-quotient Other methods for structurally summarizing RDF graphs are based on other measures, such as centrality, to identify the most important nodes, and interconnect them in the summary. Such methods aim at building an overview of the graph, even if (unlike quotient summaries) some graph nodes may not be represented at all. Διπλωματική Εργασία 17

18 2. Pattern-mining methods. These methods employ mining techniques for discovering patterns in the data; the summary is then built out of the patterns identified by mining. 3. Statistical methods. These methods summarize the contents of a graph quantitatively. The focus is on counting occurrences, such as counting class instances or building value histograms per class, property and value type; other quantitative measures are frequency of usage of certain properties, vocabularies, average length of string literals etc. Statistical approaches may also explore (typically small) graph patterns, but always from a quantitative, frequency-based perspective. 4. Hybrid methods. To this category belong works that combine structural, statistical and pattern-mining techniques. Fig. 3 shows a taxonomy from for each of these works from a Survey [22]. Fig. 3 A taxonomy of the works in the area from a Survey [22] As our work is a structural non-quotient summarization method, in the sequel we shortly present an overview of the works in the area. For an overview of the overall domain, the interested reader is forwarded to our recent survey and the relevant tutorial [23, 24] presenting works in the area of semantic summaries. Peroni et al. [25] and Wu et al. [26] focused on non-quotient structural summarization. The former tries to automatically identify the key concepts in an ontology combining cognitive principles, lexical and topological measurements such as the density and the coverage, whereas in the latter the authors use similar algorithms to identify the most important concepts and relations in an iterative manner. However, both of these efforts focus only on returning the most important nodes ignoring user s input and preferences. Other approaches recognize the importance of incorporating user preferences, such as Zhang et al. [27] and Queiroz-Sousa et al. [28]. More specifically, Zhang et al. [27] uses measures such as the degree-centrality, the betweenness and the eigenvector centrality to identify not the most important nodes, but the most important RDF sentences. In this approach user preferences can be incorporated using weights on the edges of the graph. In Queiroz-Sousa et al. [31], on the other hand, the authors try to combine user preferences with the degree centrality and the closeness to calculate the importance of a node and then they use an algorithm to find paths that include the most important nodes in the final graph. However in both these approaches, incorporating user preferences is neither explored in detail, nor evaluated. Διπλωματική Εργασία 18

19 Finally, RDFDigest+ proposes the betweenness centrality for effectively constructing summaries, and experimentally shows that the generated summaries dominate other existing approaches in the area [29], [30]. RDFDigest+ tries to identify user preferences indirectly by combining centrality measures and the numbers of instances for adapting the importance of the schema nodes to be selected. However, it completely misses opportunities for incorporating user preferences. 4. Approach 4.1 High level diagram of algorithm The workflow presented in Shape is a high level diagram of the algorithm. It consists six main steps that are described in the Table below: Step Description 1: The user inputs a set of nodes, a set of edges, a desired number of triples to be included in the Summary. 2: All possible triples from the Union of SET_NODES*SET_EDGES*SET_NODES are produced and one pass is performed to find if each of them exists in the Ontology provided. Each triple that was found is added to summary. 3: There is a check if the desired number of triplets was found to move to the step 5. If not, all possible triples from the Union of SET_NODES*SET_EDGES are produced and second pass is performed to find if each of them exists in the Ontology provided. Each triple that was found is added to the existing summary of step 2. 4: There is a check if the desired number of triples was found to move to the step 5. If not all possible triples from the SET_NODES are produced and third pass is performed to find if each of them exists in the Ontology provided. Each triple that was found is added to the existing summary of step 3. 5: There is a check if the desired number of triples was found is less than or equal the desired number of triples to move to the step 6. If not, each triple importance is calculated (by finding the degree of each node and sum the degrees of each node in triples found). Then the found triples are sorted by importance and the desired number of the most important triples is stored to the summary. 6: The summary is returned. Table Description of algorithm s workflow An example in each stage is shown in Appendix Β: «Example of Results of Executing Code». Notice: The Ontology schema in RDF or OWL format is nested in the code, but it can be changed by user by placing the new Ontology schema in the workspace project s folder and change the line 68 model.read("name_of_schema.owl"); with the name of the new Ontology schema file. Διπλωματική Εργασία 19

20 Shape 4.1.1: High level diagram of algorithm Διπλωματική Εργασία 20

21 4.2 The algorithm in detail The algorithm gets as input an Ontology Schema (RDF/OWL file format) and multiple inputs from user such as: a set of nodes, a set of edges, a desired number of triples to be included in the Summary. In line 3-5 all triples from the Union of SET_NODES*SET_EDGES*SET_NODES are constructed and one pass is performed to find all the existing triples in the Ontology provided. In line 6-9 if the desired number of triplets was not found we move on to find all the existing triples in the Ontology provided from Union of SET_NODES*SET_EDGES. In line if the desired number of triplets was not found we move on to find all the existing triples in the Ontology provided from SET_NODES. In line if the desired number of triples was not found we return all found triples in Summary else In line each triple Importance is calculated (by finding the degree of each node and sum the degrees of each node in triples found), the found triples are sorted by importance and the desired number of the most important triples is returned. Algorithm : Input: A semantic graph G, a set of nodes, a set of edges, a desired number of triples to be included in the Summary. Output: A Summary with the desired number of triples. 1. Summary={} 3. For each triple in (SET_NODES*SET_EDGES*SET_NODES) do 4. If triple in (G) then 5. Add triple to Summary 6. If triples found < desired number of schema triples in Summary then 7. For each triple in (SET_NODES*SET_EDGES) do 8. if triple in (G) then 9. Add triplet to Summary 10. If triples found < desired number of schema triples in Summary then 11. For each triplet in (SET_NODES) do 12. if triplet in (GS) then 13. Add triplet to Summary 14. If triples found < desired number of schema triples in Summary then 15. Return all found triples in Summary 16. Else_if 17. Calculate triples Importance by finding the degree of each node and sum the degrees of each node in triples found. 18. Sort_found_triples(Importance) 19. Return the desired number of most Important triples In summary. The algorithm can be implemented appropriately, performing one pass to the available triples and as such its complexity is O(T) where T the number of triples in the graph. Διπλωματική Εργασία 21

22 4.3 Running example (Goslim Ontology) In the sequel we present an example of algorithm execution using GoSlim Ontology. Gene Ontology describes GO subsets (also known as GO slims) are cut-down versions of the GO containing a subset of the terms. They are specified by tags within the ontology that indicate if a given term is a member of a particular subset. GO subsets are particularly useful for providing an overview of the range of functions and processes found in a given clade or organism s genome. Given a coarse grained view of the Ontology content without the detail of the specific fine-grained terms, these slims can offer an overall sense of the key biological functions that are vital to an organism. For example, the limited number of opsin genes in bedbugs, or the abundance of kinins in ticks. The overall visualization of the Goslim Ontology provided to the algorithm by WebOwl online tool [32] and the complexity of interconnected nodes are presented in Figure In that Figure, the most important nodes of interest cannot be interpreted. Fig : Overall visualization of the Goslim Ontology. Διπλωματική Εργασία 22

23 Therefore, by running the algorithm with the above inputs (Figure 4.3.2) the result was the summary of the top10 triples of interest is visualized in Figure 4.3.3, where a more precise point of view of the most important nodes of interest is presented, resulting from the algorithm running. Fig : User s Inputs of the running example Fig : Visualization of the synopsis top10 output by running the algorithm. Διπλωματική Εργασία 23

24 5. Evaluation After a data scientist has chosen a target variable and completed the prerequisites of transforming data and building a model, one of the final steps is evaluating the model s performance. 5.1 Metrics Assuming a query log, we would like to maximize the fragments of queries that are answered by the generated summary. More specifically, having a summary, we can calculate for each query that can be partially answered by the summary, the percentage of the classes and properties that are included in the summary, i.e. the success classes and the success properties. The query coverage is the weighted sum of these percentages. As such the coverage is defined as follows. Definition 4. (Coverage). Assuming a graph G = (V, E), a query workload Q={q1,, qn}, and two weights for nodes and edges, i.e. wnodes and wprop, we define coverage as follows: Coverage(G, Q)= AVG n success_nodes(q q=i (w i,) success_properties(q nodes + w i,) nodes(q i ) prop ) properties(q i ) Note that as our summaries are based on user input we are interested only for the queries that that include nodes and edges provided as input by the users. 5.2 Datasets The data results were evaluated by calculating the coverage of 30 summaries for each of the two ontologies DBpedia and SWDF respectively. The DBpedia ontology is the heart of DBpedia. It evolved into a successful crowd-sourcing effort with the DBpedia community continuously contributing to the ontology schema. This large cross-domain ontology has 768 classes, 3000 properties and 5047 instances. Scholarly data dataset is a refactoring of the Semantic Web Dog Food (SWDF), in an effort to keep the dataset growing in good health. It uses a novel data model, the conference-ontology, which improves the Semantic Web Conference Ontology, adopting best ontology design practices. This small sized ontology has zero classes, 49 properties and 590 instances. All the current data can be accessed in different formats (i.e., HTML, RDF/XML, Turtle, N- TRIPLES, and JS ruon-ld) via URI dereferencing, queried via SPARQL or downloaded as single RDF dumps for research. Διπλωματική Εργασία 24

25 Coverage 5.3 Results By running the new algorithm that has been created in this study, with input of two random nodes and with demand of top 10, top 20, top 30 triples, we resulted summaries for each of one above ontologies. The application above was repeated for another 10 random pairs of nodes. Therefore, by analysing with appropriate queries, each summary for each pair of nodes, we estimated the coverage for all studied cases. The diagrams of the average values of the coverage for top 10, top 20 and top 30 triples vs random selected top 10, top 20 and top 30 triples in summaries for each ontology are presented in the figures below (Shapes ) Coverage of Summaries DBPEDIA top10 top20 top30 Random top10 top20 top30 Summaries Shape 5.3.1: Coverage of Summaries DBPEDIA Διπλωματική Εργασία 25

26 Coverage Coverage of Summaries SWDF top10 top20 top30 Summaries Shape 5.3.2: Coverage of Summaries SWDF top10 top20 top30 Random By analysing the data above, the size of the summary increases in proportion to the percentage of queries that are answered by this summary. This analysis was expected due to the fact the larger part of the graph is presented, more queries succeed their target. The more accurate the triples in the summary exist, they maximize the part of the graph that the queries answer. Therefore, the new triples that are added to the summary from top 10 to top 20 and top 30, increase the percentage of the queries that are answered with the input of the 2 specific nodes. As we see from the diagrams that the addition of new nodes increases the coverage, that means that the nodes are not accidental, but they have been selected correctly from the algorithm to be in the summary. Additionally, we conclude from the diagrams that the coverage in Ontology SWDF is higher in comparison to the Dbpedia, a fact that was expected due to the fact SWDF is smaller in size Ontology. In detail Top 10 of triples in Ontology SWDF covers bigger percentage of SWDF Graph then the percentage of Dbpedia Graph from Top 10 of triples in Ontology Dbpedia. Furthermore, we compare our approach with a random selection baseline, where for each case we randomly select the top-k number of triples. As shown in each case (Shapes ) the triples selected by our algorithm outperforms random selections. This is more obvious in the DBpedia dataset as it is a larger dataset with a larger amount of queries and as shown the coverage of our summary is by far superior by the coverage of the summaries constructed by random selection. This effect is minimized in the case of SWDF as the graph is smaller and even selecting randomly 30 triples the coverage is significantly improved. Nevertheless even in SWDF in all cases the summaries generated by our algorithm outperform random selection Διπλωματική Εργασία 26

27 6. Conclusions In this thesis we present a new and personalised algorithm, which can select the most important triples for generating a high quality summary. The proposed algorithm requires a single pass of the data to be executed and as such it is highly efficient. We experimentally evaluated our algorithm on the Dbpedia and SWDF datasets. Our experiments show the advantages of our approach, verifying that the more triples selected by our algorithm larger parts of user queries involving user preferences can be answered. That means that the nodes do not enter accidentally or randomly to the summaries. The top 30 triples summary coverage in Dbpedia Ontology and the top 30 triples summary coverage in SWDF Ontology were calculated 0,38 and 0,49 respectively. At this point, we would like to emphasize the fact that in this field of research, this is the first effort of development of a new algorithm for personalised summaries. The results will be published in a research paper in iswc As future directions we intend to explore machine learning methods for selecting the triples to be included in the summary and to include notions of diversity or fairness to the included summaries. Διπλωματική Εργασία 27

28 7. Bibliography [1] Sejla Cebiric, François Goasdoué, Haridimos Kondylakis, Dimitris Kotzinos, Ioana Manolescu, Georgia Troullinou, and Mussab Zneika Summarizing semantic graphs: a survey. VLDB J. 28, 3 (2019), [2] Giannis Agathangelos, Georgia Troullinou, Haridimos Kondylakis, Kostas Stefanidis, and Dimitris Plexousakis RDF Query Answering Using Apache Spark: Review and Assessment. In 34th IEEE International Conference on Data Engineering Workshops, ICDE Workshops 2018, Paris, France, April 16-20, IEEE Computer Society, [3] Georgia Troullinou, Haridimos Kondylakis, Matteo Lissandrini, and Davide Mottin SOFOS: Demonstrating the Challenges of Materialized View Selection on Knowledge Graphs. ACM SIGMOD. [4] Peroni Silvio, Motta Enrico, and d Aquin Mathieu Identifying key concepts in an ontology, through the integration of cognitive principles with statistical and topological measures. In ASWC [5] Gang Wu, Juanzi Li, Ling Feng, and Kehong Wang Identifying potentially important concepts and relations in an ontology. In ISWC [6] Kleber Xavier Sampaio de Souza, Adriana D. dos Santos, and Silvio R. M. Evangelista Visualization of ontologies through hypertrees. In CLIHC. [7] Xiang Zhang, Gong Cheng, and Yuzhong Qu Ontology summarization based on rdf sentence graph. In WWW [8] Alexandros Pappas, Georgia Troullinou, Giannis Roussakis, Haridimos Kondylakis, and Dimitris Plexousakis Exploring Importance Measures for Summarizing RDF/S KBs. In ESWC [9] Georgia Troullinou, Haridimos Kondylakis, Kostas Stefanidis, and Dimitris Plexousakis Exploring RDFS KBs Using Summaries. In ISWC [10] W3C: Resource description framework. RDF/ Διπλωματική Εργασία 28

29 [11] Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley, Reading (1995) [12] Goasdoué, F., Manolescu, I., Roatis, A.: Efficient query answering against dynamic RDF databases. In: Joint 2013 EDBT/ICDT Conferences, EDBT 13 Proceedings, Genoa, Italy, March 18 22, 2013, pp (2013) [13] W3C: Resource description framework. RDF [14] W3C: SPARQL 1.1 query language. sparql11-query/ (2013) [15] W3C: Resource description framework. RDF/ [16] Bursztyn, D., Goasdoué, F.,Manolescu, I.: Efficient query answering in DL-Lite through FOL reformulation (extended abstract). In: Proceedings of the 28th International Workshop on Description Logics, Athens, Greece, June 7 10, 2015 (2015) [17] Goasdoué, F., Karanasos, K., Leblay, J., Manolescu, I.: View selection in semantic web databases. PVLDB5(2), (2011) [18] Goasdoué, F., Manolescu, I., Roatis, A.: Efficient query answering against dynamic RDF databases. In: Joint 2013 EDBT/ICDT Conferences, EDBT 13 Proceedings, Genoa, Italy, March 18 22, 2013, pp (2013) [19] Picalausa, F., Luo, Y., Fletcher, G.H.L., Hidders, J., Vansummeren, S.: A structural approach to indexing triples. In: The Semantic Web: Research and Applications 9th Extended Semantic Web Conference, ESWC 2012, Heraklion, Crete, Greece, May 27 31, Proceedings (2012) [20] Stocker, M., Seaborne, A., Bernstein, A., Kiefer, C., Reynolds, D.: SPARQL basic graph pattern optimization using selectivity estimation. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, Beijing, China, April 21 25, 2008, pp (2008) [21] Lanti, D., Rezk, M., Xiao, G., Calvanese, D.: The NPD benchmark: Reality check for OBDA systems. In: Proceedings of the 18th International Conference on Extending Διπλωματική Εργασία 29

30 Database Technology, EDBT 2015, Brussels, Belgium, March 23 27, 2015, pp (2015) [22] Sejla Cebiric, François Goasdoué, Haridimos Kondylakis, Dimitris Kotzinos, Ioana Manolescu, Georgia Troullinou, Mussab Zneika: Summarizing semantic graphs: a survey. VLDB J. 28(3): (2019) [23] Sejla Cebiric, François Goasdoué, Haridimos Kondylakis, Dimitris Kotzinos, Ioana Manolescu, Georgia Troullinou, and Mussab Zneika Summarizing semantic graphs: a survey. VLDB J. 28, 3 (2019), [24] Haridimos Kondylakis, Dimitris Kotzinos, and Ioana Manolescu RDF graph summarization: principles, techniques and applications. In EDBT [25] Peroni Silvio, Motta Enrico, and d Aquin Mathieu Identifying key concepts in an ontology, through the integration of cognitive principles with statistical and topological measures. In ASWC [26] Gang Wu, Juanzi Li, Ling Feng, and Kehong Wang Identifying potentially important concepts and relations in an ontology. In ISWC [27] Xiang Zhang, Gong Cheng, and Yuzhong Qu Ontology summarization based on rdf sentence graph. In WWW. [28] Kleber Xavier Sampaio de Souza, Adriana D. dos Santos, and Silvio R. M. Evangelista Visualization of ontologies through hypertrees. In CLIHC [29] Georgia Troullinou, Haridimos Kondylakis, Evangelia Daskalaki, and Dimitris Plexousakis RDF Digest: Efficient Summarization of RDF/S KBs. In ESWC. [30] Alexandros Pappas, Georgia Troullinou, Giannis Roussakis, Haridimos Kondylakis, and Dimitris Plexousakis Exploring Importance Measures for Summarizing RDF/S KBs. In ESWC. [31] Queiroz-Sousa, P.O., Salgado, A.C., Pires, C.E.S.: A method for building personalized ontology summaries. JIDM 4(3), (2013) [32] WebVOWL is a web application for the interactive visualization of ontologies [33] Διπλωματική Εργασία 30

31 8. Appendix Α: «Algorithm in Java Code» 1 package apaxmsc; 2 3 import java.io.ioexception; 4 import java.io.printwriter; 5 import java.util.arraylist; 6 import java.util.hashset; 7 import java.util.linkedhashset; 8 import java.util.scanner; 9 import java.util.set; 10 import java.util.regex.matcher; 11 import java.util.regex.pattern; 12 import org.apache.jena.ontology.ontclass; 13 import org.apache.jena.ontology.ontmodel; 14 import org.apache.jena.ontology.ontmodelspec; 15 import org.apache.jena.rdf.model.model; 16 import org.apache.jena.rdf.model.modelfactory; 17 import org.apache.jena.rdf.model.property; 18 import org.apache.jena.rdf.model.rdfnode; 19 import org.apache.jena.rdf.model.resource; 20 import org.apache.jena.rdf.model.statement; 21 import org.apache.jena.rdf.model.stmtiterator; 22 import org.apache.jena.util.printutil; 23 import org.apache.jena.util.iterator.extendediterator; public class BNP_Msc { 26 public static void main(string[] args) { //ΟΡΙΣΜΟΣ ΕΙΣΟΔΩΝ ΑΠΟ ΧΡΗΣΤΗ 29 System.out.println(""); 30 System.out.println("Lets set the USER'S Input DATA:"); 31 System.out.println(""); //ΕΙΣΑΓΩΓΗ ΚΟΜΒΩΝ ΑΠΟ ΧΡΗΣΤΗ (ΠΡΟΣ ΑΝΑΖΗΤΗΣΗ ΣΤΗΝ ΟΝΤΟΛΟΓΙΑ) 34 ArrayList<String> Set_of_Nodes = new ArrayList<String>(); 35 System.out.println("Enter the Number of NODES in SetNodes:"); 36 Scanner Input1=new Scanner(System.in); 37 int NoNodes =Input1.nextInt(); 38 for (int i = 0; i < NoNodes; i++) { 39 System.out.println("Enter NODE Name for input to SetNodes:"); 40 Scanner input2=new Scanner(System.in); Διπλωματική Εργασία 31

32 41 String a =input2.nextline(); 42 Set_of_Nodes.add(a); 43 } 44 System.out.println(""); //ΕΙΣΑΓΩΓΗ ΙΔΙΟΤΗΤΩΝ ΑΠΟ ΧΡΗΣΤΗ (ΠΡΟΣ ΑΝΑΖΗΤΗΣΗ ΣΤΗΝ ΟΝΤΟΛΟΓΙΑ) 47 ArrayList<String> Set_of_Edges = new ArrayList<String>(); 48 System.out.println("Enter the Number of EDGES in SetEdges:"); 49 Scanner Input3=new Scanner(System.in); 50 int NoEdges =Input3.nextInt(); 51 for (int i = 0; i < NoEdges; i++) { 52 System.out.println("Enter EDGE Name for input to SetEdges:"); 53Scanner input4=new Scanner(System.in); 54 String a =input4.nextline(); 55 Set_of_Edges.add(a); 56 } 57 System.out.println(""); //ΕΙΣΑΓΩΓΗ ΕΠΙΘΥΜΗΤΟΥ ΑΡΙΘΜΟΥ ΤΡΙΠΛΕΤΩΝ ΑΠΟ ΧΡΗΣΤΗ (ΣΤΗΝ ΣΥΝΟΨΗ ΜΕΤΑ ΑΠΟ ΑΝΑΖΗΤΗΣΗ ΣΤΗΝ ΟΝΤΟΛΟΓΙΑ) 60 System.out.println("Enter the Desired Number of TRIPLES in Summary:"); 61 Scanner Input5=new Scanner(System.in); 62 int NoTriplesInSummary =Input5.nextInt(); 63 System.out.println(""); //ΔΗΜΙΟΥΡΓΙΑ ΜΟΝΤΕΛΟΥ ΑΠΟ ΑΡΧΕΙΟ ΟΝΤΟΛΟΓΙΑΣ (ΦΟΡΤΩΣΗ RDF/OWL FILE) 66 Model model = ModelFactory.createDefaultModel(); 67 model.read("second_swdf.owl"); 68 OntModel owlontology = ModelFactory.createOntologyModel( OntModelSpec.OWL_MEM, model ); //ΔΗΜΙΟΥΡΓΙΑ ΠΙΝΑΚΑ STATEMENTS ΣΧΗΜΑΤΟΣ ΟΝΤΟΛΟΓΙΑΣ 71 ExtendedIterator<Statement> statements = owlontology.liststatements(); 72 ArrayList<String> Table_of_Statements = new ArrayList<String>(); 73 ArrayList<String> Table_of_Predicates = new ArrayList<String>(); 74 int nostatements = 0; 75 while (statements.hasnext()) { 76 Statement thisstatement = (Statement) statements.next(); 77 nostatements++; 78 Table_of_Statements.add(thisStatement.toString()); 79 Resource Predicate = thisstatement.getpredicate(); 80 Table_of_Predicates.add(Predicate.toString()); 81 } Διπλωματική Εργασία 32