HELIOS: a General Framework for Ontology-based Knowledge Sharing and Evolution in P2P Systems

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1 HELIOS: a General Framework for Ontology-based Knowledge Sharing and Evolution in P2P Systems S. Castano, A. Ferrara, S. Montanelli, D. Zucchelli Università degli Studi di Milano DICO - Via Comelico, 39, Milano - Italy {castano,ferrara,montanelli,zucchelli}@dico.unimi.it Abstract The problem of knowledge sharing is eminent in the P2P area. In this paper, we propose a general framework, called HELIOS (Helios Evolving Interaction-based Ontology knowledge Sharing), conceived for supporting dynamic ontology-based knowledge sharing and evolution in P2P systems. The knowledge sharing and evolution processes in HELIOS are based on peer ontologies, describing the knowledge of each peer, and on interactions among peers, allowing information search and knowledge acquisition/extension, according to pre-defined query models and semantic techniques for ontology matching. 1. Introduction Peer-to-peer (P2P) networks are distributed systems whose nodes (peers) have equal roles and equal capabilities in exchanging information and services directly with each other. A widely accepted arrangement distinguishes between pure and hybrid systems. In pure systems such as Gnutella ( and Freenet ( all peers have equal roles and responsibilities in all aspects (e.g., query, download). In hybrid systems such as Napster ( search is performed over a centralized directory, while download occurs directly between the involved nodes. In Super-Peer Networks (SPN) [10] such as Morpheus ( peers are clustered, and for each cluster a Super-Peer (SP) node is designated, acting as a centralized server for queries in a cluster (as in the hybrid model). Systems for peer-based data management have recently appeared [7, 8]. These systems generally follow a hybrid approach, in that the P2P network contains special This paper has been partially funded by the WEB-MINDS FIRB Project and by the D2I COFIN Project, funded by the Italian Ministry of Education, University, and Research. nodes, called mediators or super-peers, having the responsibility of building and maintaining an integrated view of the knowledge of subsets of nodes in the network. An important requirement to be considered for knowledge sharing in P2P systems is related to the inherent dynamism of the context and to the role of Semantic Web techniques for semantic interoperability. Based on our experience in developing semantic information integration techniques in the ARTEMIS system [1, 3, 4], in this paper, we propose the HELIOS (Helios Evolving Interaction-based Ontology knowledge Sharing) framework. HELIOS is conceived for supporting dynamic ontology-based knowledge sharing and evolution in P2P systems. The knowledge sharing and evolution processes in HELIOS are based on peer ontologies, describing the knowledge of each peer (i.e., the knowledge a peer brings to the network and the knowledge the peer has of network), and on interactions among peers, allowing information search and knowledge acquisition/extension, according to pre-defined query models and ontology matching techniques. The paper is organized as follows. In Section 2, we describe the general architecture of HELIOS. In Section 3, we present the query models and the query processing in HELIOS. In Section 4, we describe the ontology matching techniques of HELIOS. Finally, the related work and future research issues are discussed in Section 5 and in Section 6, respectively. 2. Overview of the HELIOS framework HELIOS enables knowledge sharing and evolution considering a P2P system where nodes are equipotential in terms of functionalities and capabilities. Consequently, HE- LIOS can work either on a pure P2P system, to support knowledge sharing needs of a community of peers, or on a SPN system, to support knowledge sharing between superpeers.

2 2.1. HELIOS Architecture The HELIOS framework architecture is based on the following elements: Peer: the network node. It maintains its knowledge and interacts with other peers of the HELIOS network for knowledge sharing and evolution. Peer knowledge: the peer knowledge of the HELIOS network organized as a peer ontology. We conceptualize a peer ontology as a network of concepts, where each concept is characterized by a set of attributes and a set of relationships with other concepts. Moreover, a concept in the peer ontology has associated location attributes specifying the network locations of other peers storing concepts and/or data semantically related to the considered concept. A peer can augment its knowledge in the peer ontology by adding new concepts and/or by enriching existing concept descriptions in terms of new attributes and of new relationships acquired by other peers. Peer data: the data stored at the peer (e.g., relational data, XML documents, files, legacy datasets). The ontological description of peer data is provided by the peer ontology. Peer interaction: it is constituted by query requests and query answers between peers. As an example, in Figure 1 we have a HELIOS network with three peer nodes. The peer A maintains data on books and Library Journal does not store data, but has an ontology describing the concepts of Library, and Journal, with a relationship contains among them. The peer C stores book data with the corresponding concept description in its ontology HELIOS toolkit The Figure 2 shows the main components of the HELIOS toolkit with which a peer is equipped to submit queries and to process queries coming from other peers, by matching them against its ontology. The HELIOS toolkit is composed of the following components: Query processing manager: it performs the composition of queries according to the query models described in Section 3, the query processing and the answer composition. The query processing is described in Section 3. manager: it performs the comparison of a target concept (e.g., a concept in a query) against the peer ontology, in order to find the concepts matching the target concept (i.e., semantically related concepts), as described in Section 4. Ontology manager: it performs the definition and maintenance of the peer ontology. When a peer enters the HELIOS network, it receives the HE- LIOS toolkit and performs the initialization procedure. At the end of this procedure, the peer is able to interact with the other HELIOS peers, and has defined the peer ontology describing its data. Subsequently, the peer performs notification to acquire knowledge of the network, by starting the knowledge sharing and evolution processes. PEER B Legend Peer ontology PEER A Peer data Peer interaction PEER C Figure 2. The HELIOS toolkit Figure 1. HELIOS architecture metadata on books and publications (i.e., the peer ontology of A contains the concepts of and with an is-a relationship between them). The peer B 3. Query models and query processing in HE- LIOS In this section, we classify the different types of queries that each peer can send over the network and we discuss the

3 query processing techniques for matching a query against a peer ontology Query models Three different query models are supported in HELIOS, namely, the search model, the probe model, and the probe/search model. The search query model is used by a peer in order to find in the network data related to one or more concepts of interest (in the following called target concept). The aim is just data retrieval. Each peer storing data matching the target concept(s) of a search query can answer to the requesting peer. The probe query model is used by a peer interested in extending its knowledge of the network. Each peer having concepts matching the target concept(s) of a probe query can answer to the requesting peer. The answer to a probe query is constituted by a set of metadata, with which the requesting peer can extend its knowledge on target concepts, in terms of concept description, concept attributes, concept relationships, and concept location. The probe/search model allows a peer to find both data and metadata related to target concept(s). With this type of query, a peer can perform a search activity and contemporary increase its knowledge of the network. The answer to a probe/search query is constituted by data and metadata matching the target concept(s). The comparison between a target concept and a peer ontology is performed through appropriate matching techniques, which are described in Section 4. The matching process can be performed according to a matching strategy, and the number of matching concepts returned for each target concept depends on a matching threshold specified for the matching strategy. The matching strategy and the threshold value can be set either by the requesting peer (requestdriven approach) or by the answering peer (answer-driven approach). In the first approach, the matching strategy and the matching threshold are specified directly by the requesting peer in the query before submission. In the second approach, they are specified by the answering peer in the answer. Queries conform to the template shown in Table 1, which is composed of the following clauses: Find: list of the target concept(s) names. With: (optional) list of attributes of the target concept(s). Where: (optional) list of conditions to be verified by the attribute values, and/or (optional) list of relationships for the target concept(s). Query model: specification of the adopted query model (i.e., search, probe, probe/search). strategy: specification of the matching strategy (i.e., similarity or equivalence, as described in Section 4). This clause is present in the query-driven approach; otherwise the matching threshold is specified in the answer (see the answer template in Section 3.2). threshold: specification of the threshold value (i.e., a value in the range (0,1]) to be used for the selection of matching concepts according to the selected matching strategy. This clause is present in the query-driven approach; otherwise the matching threshold is specified in the answer (see the answer template in Section 3.2). Query template Find Target concept name [,...] [With Target concept attribute [,...]] [Where Condition, Relationship [,...]] Query model Probe Search Probe/Search [ strategy Similarity Equivalence] [ threshold t (0, 1]] 3.2. Query processing Table 1. Query template When a peer receives a query from another peer, the query is processed in order to extract the target concept(s) and the query model used. In particular, the query is transformed into an ontological description of the target concept(s) for comparison against the peer ontology. The information specified in a query for a target concept has an impact on the kind of matching that can be performed for the target concept. In general, a detailed description of a target concept will allow a more comprehensive matching, with a generally richer answer. As an example, suppose that the peer A is interested in extending its knowledge on, then it sends a probe query to the peer B. In Figure 3, we consider three different examples of probe queries (i.e., Query A, Query B, and Query C), which differ for the richness of the description of. When receiving the probe query, the peer B has to compare with the knowledge in its peer ontology. In Figure 3, we show the ontological descriptions of extracted from Query A, Query B, and Query C, respectively. In the Query A, only the concept name is specified; consequently the peer B can perform the matching process based only on the target concept name. In the Query B, the concept name and the concept attributes are specified; consequently the matching process can be based on the name and attributes. In the Query C, the concept name, the concept

4 attributes, and the concept relationships with other concepts are specified, allowing the peer B to perform the matching process based on the name, attributes, and relationships information. In Section 4, we describe how the matching process is performed. threshold: specification of the threshold value (i.e., a value in the range (0,1]) chosen for the matching. This clause is used just in the peer-driven approach; otherwise the matching threshold is specified in the query. Year Topic PEER A Query C Find With Where Query A Find Query B Find With,,.,.,.,.Year,.Topic is-a Query A transformation Query B transformation Query C transformation Year Topic Name-based Name-based Attribute-based Name-based Attribute-based Relationship-based Library Rooms s PEER B Journal Number Answer template {Concept Concept name [Attributes Attribute name [,...]] [Relationships Relationship [,...]] {<Target concept (matching value)> [,...]} Location Location link [,...] [ strategy Similarity Equivalence] [ threshold t (0, 1]]} Table 2. Answer template 4. Ontology matching in HELIOS REQUESTING PEER ANSWERING PEER Figure 3. Example of query processing Once concepts matching a target concept have been selected, they are returned in the query answer, which contains the list of concepts matching the target concept(s). Each concept is described by the following answer template (see Table 2): Concept: name of the matching concept. Attributes: (optional) list of attributes of the matching concept. Relationships: (optional) list of relationships between the matching concept and other concepts in the peer ontology. : set of pairs < target concept, matching value >, specifying the target concept with which the matching concept matches, together with the corresponding matching value. Location: list of location attributes known by the answering peer for the matching concept. strategy: specification of the matching strategy (i.e., similarity or equivalence, described in Section 4). This clause is used just in the peer-driven approach; otherwise the matching model is specified in the query. The general goal of ontology matching techniques is to find concepts that have a semantic relationship with a target concept. In our framework, we are interested in matching a target concept of a query against a peer ontology (knowledge sharing), or in assimilating new concepts returned by probe queries into a peer ontology (knowledge evolution) Affinity-based matching For ontology matching techniques in HELIOS, we rely on the schema matching techniques developed in the ARTEMIS data integration system [1], which are being extended in HELIOS to the problem of concept matching in distributed environments with ontological requirements from autonomous peers. To take into account different levels of detail in concept descriptions, concept matching can be performed considering the following matching features: names, attributes, and relationships. Affinity coefficients are introduced to assess the level of matching of two concepts with respect to each matching feature above. Name Affinity. Given two concepts, C i and C j, their Name Affinity, denoted by NA(C i,c j ), measures their level of matching considering their names, based on the number and kind of terminological relationships (e.g., synonymy, hypernymy) holding between them according to a reference thesaurus. For example, using ARTEMIS we can exploit a pre-defined thesaurus like WordNet or a domainspecific thesaurus. ARTEMIS properly strengths each kind of terminological relationships for name affinity evaluation (e.g., synonymy=1, hypernymy=0.8).

5 Structural Affinity. Given two concepts, C i and C j, their Structural Affinity, denoted by SA(C i,c j ), measures their level of matching based on their attributes, and is proportional to the number of common attributes. Two attributes of different concepts are considered common if their names have affinity and if their domains are compatible (e.g., ARTEMIS considers two domains compatible if they are equivalent, or if one is a specialization of the other). Contextual Affinity. Given two concepts, C i and C j, their Contextual Affinity, denoted by CA(C i,c j ), measures their level of matching based on their contexts, and is proportional to the number of their adjacents with affinity. The affinity between two adjacents is evaluated considering their name and structural affinity and the type of each semantic relationship in which they participate strategies To assess the level of matching of two concepts in a comprehensive way, a Global Affinity measure is defined as the linear combination of the previous affinity measures. In the Global Affinity, weights are introduced to assess the relevance of each kind of affinity in the computation of the Global Affinity measure, allowing flexible matching strategies depending on the information available in the ontological description of the concepts to be matched. Exploiting the global affinity values, the number of concepts matching a target concept depends on the level of closeness we want to impose on them. Given a target concept C and a peer ontology PO, we denote by M C the set of concepts of PO matching C, i.e., the concepts having a global affinity value GA 0with C. We define two matching strategies for the selection of matching concepts: Similarity. In the similarity-based strategy, the aim is to find the concepts which have a semantic correspondence with C. All concepts whose global affinity value is greater than or equal to a similarity threshold t S > 0 are retrieved, that is, MC S = {C PO GA(C, C ) t S }. Equivalence. In the equivalence strategy, the aim is to find only the concept(s) considered equivalent. All concepts whose global affinity value is greater than or equal to an equivalence threshold t E, with t E t S, are retrieved, that is, MC E = {C PO GA(C, C ) t E } with MC E M C S. This is a more restrictive strategy than the similarity strategy, because it returns a subset of the concepts retrieved by the previous strategy, those having the highest affinity with C. As an example of matching, we consider the Query B of Figure 3. We have to match the target concept against the peer ontology PO B. In order to find the concepts that match on the basis of the information available in the ontological description of extracted from Query B, is compared with each concept of PO B, by evaluating name and structural affinity coefficients. For concept selection, we set the equivalence threshold t E =0.9 and the similarity threshold t S =0.5. For name and structural affinity evaluation, we exploit ARTEMIS, with WordNet for determining terminological relationships. The results provided by ARTEMIS are summarized in Table 3 1. For instance, the Name affinity Structural affinity Global affinity NA(, ) =1.0 NA(, Library) =0.8 NA(, Journal) =0.64 SA(, ) =0.8 SA(, Journal) =0.33 SA(, Library) =0.0 GA(, ) =0.9 GA(, Journal) =0.52 GA(, Library) =0.4 Table 3. Affinity coefficients resulting from matching against PO B high affinity between and is due to fact that their names are synonyms and to the fact that they have two attributes in common. The low affinity between and Library is due to the absence of common attributes in their structure. In Figure 4, we show the query answers that peer B returns to peer A for Query B by adopting the similaritybased strategy, and the equivalence-based strategy, respectively. According to the first strategy, both and Journal are returned, while according to the second strategy, only is returned. 5. Related work In this section, we overview the main peer-based systems for knowledge sharing. Edutella [9] is an open source project that creates an infrastructure for sharing metadata in RDF format. It applies the P2P model using the JXTA protocol. The network is segmented into thematic clusters. In each cluster, a mediator semantically integrates source metadata. 1 Here we used the default settings of ARTEMIS for the terminological relationships synonym (SYN), hypernymy (BT), hyponymy (NT), and positive association (RT): W SY N =1, W BT/NT =0.8, W RT =0.5.

6 Similarity-based strategy strategy Similarity threshold 0.5 Concept Attributes, Relationships Library contains (0.9) Location peerb Concept Journal Attributes, Number, Relationships Library contains Journal (0.52) Location peerb Equivalence-based strategy strategy Equivalence threshold 0.9 Concept Attributes, Relationships Library contains (0.9) Location peerb Figure 4. Example of query answers for the target concept Edutella is an example of hybrid P2P architecture, in that each source sends queries to the mediator of its own cluster, and the mediator returns a list of nodes eligible to offer semantically related information. The effective data access holds in direct network connections among peers. The mediator handles a request either directly or indirectly: directly, by answering queries using its own integrated schema; indirectly, by querying other cluster mediators. The PDMS system [7] proposes a solution for the semantic integration of heterogeneous information sources in a distributed framework. Network nodes develop different functionalities according to their capabilities. In particular, nodes with high resource capabilities play the role of mediators in the network. The system implements a hybrid P2P solution: a mediator node receives a set of information sources schemas and executes the semantic integration step, to derive an ontology view of the acquired information. A set of mediators can be organized in a hierarchy, unifying their ontologies in a global view. When a mediator receives a query from any host, it consults its own ontology and returns a list of sources eligible to offer an answer to the query. A query can be received and analyzed by more mediators, without source clusterization. In Data Mapping [8], an approach to determine and handle mappings among heterogeneous data sources in a P2P framework is described. It is an example of pure P2P architecture: network nodes are really equipotential for functionalities and for capabilities and interact each others using a Gnutella-like protocol. Each node determines semantic mappings among instances of its entities, and takes care of mapping consistency interacting with domain experts. Relations are shared with other peers, that run a comparison and search algorithm to create new relations between received mappings and their own data schemas. Results will be distributed again to progressively increase the knowledge of each community member. The InfoQuilt[2] system developed at LSDIS Lab supports heterogeneous information sharing in a distributed framework. Each node describes its own information using an ontological language (DAML+OIL). This description is semantically mapped onto an inter-domain ontology (composed of DAML+OIL classes) which resides in a central directory node. InfoQuilt has a P2P hybrid architecture: each peer queries the directory node and the inter-domain ontology residing at the node to derive the location of the peers storing semantically related information in the system. Data are addressed with a direct connection to the identified peers. Original contribution of HELIOS. The original contribution of HELIOS with respect to these approaches is the use of semantic-based techniques for allowing dynamic knowledge sharing, by contemporary adopting a pure P2P system model. To show the contribution of HELIOS with respect to a typical P2P system, in Figure 5, we provide an example of operating scenario. We suppose that the peer C joins the Figure 5. Example of a peer-to-peer network scenario network with a peer ontology on the Detective novel concept. The neighbors of the peer C have no information on Detective novel concept and are not interested in it. On the opposite, the peer Y has an ontology describing the Mystery novel concept and is interested in other semantically related concepts such as Detective novel. The distance in terms of hops between the peer C and the peer Y is high. In a conventional P2P scenario without

7 semantic matching, the peer C would be basically isolated because queries on the Detective novel concept would not be sent. Furthermore, the peer C and the peer Y would have few possibilities to share their knowledge because of the high distance. In HELIOS, the peer C is integrated with the neighbors once it receives queries regarding or Novel concepts, since it answers to such queries with information on Detective novel by exploiting semantic matching techniques. We note that the peer C and the peer Y, inquiring about their own concepts, will retrieve information on semantically related concepts and Novel from peer B and and Novel from peer W, respectively. In HELIOS, through a chain of semantic concept relationships, the peer C and the peer Y have the possibility to reach each other and to share their knowledge in spite of their distance. 6. Conclusions and future research issues In this paper, we have presented the HELIOS framework architecture for dynamic ontology knowledge sharing and evolution in P2P systems. We want to stress that HELIOS is a generic framework whose features can be adopted in various domain contexts. HELIOS has been conceived to work on a pure P2P system, supporting semantic navigation and knowledge sharing needs of a community of peers. However, such interaction-based approach can be adopted as a model for knowledge sharing between super-peers, when adopting a hybrid P2P approach. In this paper, we have presented the framework architecture in general, which has been the first goal of our research activity within a national research project called WEB-MINDS (WidescalE, Broadband, MIddleware for Network Distributed Services). There are a number of ongoing research issues concerned with our system which will be the goal of our future activity in the WEB-MINDS project. Knowledge representation. For knowledge representation, we are working in the direction of choosing a Semantic Web compatible formalism for ontology knowledge representation and for supporting semantically rich queries. The representation of the ontology knowledge in HELIOS is an important aspect, since it will have an impact on the subsequent development of query processing and ontology consistency techniques. For this purpose, we will rely on ontology knowledge representation facilities of the ARTEMIS data integration system [5]. Knowledge distribution. A further issue to be studied in deep detail in HELIOS is related to strategies for the storage of new ontology concepts acquired from the network. A possible strategy is a storage approach: a peer stores all concept information it receives from the other peers, without filtering. On the opposite, in a link approach, a peer maintains a location link to the peer(s) storing ontological descriptions of a target concept. In HELIOS, we will work in the direction of adopting a mixed approach, and we are studying criteria for deciding which concepts to store and which concepts to link. System implementation. The choice of a pure system such as HELIOS, imposes us to carefully estimate the performance issues in our framework. To this end, we are working, in collaboration with a network group of the WEB- MINDS project, to develop a semantic routing protocol to build an overlay network that ensures an effective and focused query routing [6]. The mechanism efficiency will be evaluated with respect to both the probability of success in data retrieving and the amount of generated traffic. References [1] The ARTEMIS project web site. [2] M. Arumugam, A. Sheth, and I. B. Arpinar. Towards peer-to-peer semantic web: A distribuited environment for sharing semantic knowledge on the web. In Proc. of the WWW2002 Conference, Honolulu, Hawaii, USA, [3] S. Castano, V. D. Antonellis, and S. D. C. di Vimercati. Global viewing of heterogeneous data sources. IEEE Transactions on Data and Knowledge Engineering, 13(2): , [4] S. Castano, V. D. Antonellis, A. Ferrara, and G. K. Ottathycal. A disciplined approach for the integration of heterogeneous XML datasources. In Proc. of DEXA WEBS Workshop, Aix-en-Provence, France, Sept [5] S. Castano and A. Ferrara. Knowledge representation and transformation in ontology-based data integration. In Proc. of ECAI Workshop on Knowledge Transformation for the Semantic Web, Lyon, France, July [6] S. Castano, A. Ferrara, S. Montanelli, E. Pagani, and G. Rossi. Ontology-addressable contents in P2P networks. In Proc. of WWW 03 1st SemPGRID Workshop, Budapest, May stefan/sempgrid/proceedings/proceedings.pdf. [7] A. Halevy, Z. Ives, D. Suciu, and I. Tatarinov. Schema mediation in peer data management systems. In Proc. of ICDE 03, Bangalore, India, March [8] A. Kementsietsidis, M. Arenas, and R. J Miller. Mapping data in peer-to-peer systems: Semantics and algorithmic issues. In Proc. of ACM SIGMOD 03, San Diego, California, USA, June To Appear. [9] Nejdl et al. EDUTELLA: a P2P networking infrastructure based on RDF. In Proc. of the WWW2002 Conference, Honolulu, Hawaii, USA, May [10] B. Yang and H. Garcia-Molina. Designing a Super-Peer network. In Proc. of ICDE 03, Bangalore, India, March 2003.

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