FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W156904288> ?p ?o ?g. }

• W156904288 endingPage "116" @default.
• W156904288 startingPage "112" @default.
• W156904288 abstract "Recently, several research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which have been targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary for participants to provide brief descriptions of themselves, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peer a given query should be forwarded. In this talk, I propose the use of graph clustering techniques on knowledge bases for that purpose. After a brief round-trip over an ontology-based P2P knowledge management scenario, I will demonstrate the automatic generation of self-descriptions of peers’ knowledge bases through the use of graph clustering. Viewing the knowledge base of a peer as a graph consisting of concepts and instances, one can employ clustering techniques to partition it into clusters of similar entities. From each cluster, the centroid can then be selected as a representative. This yields a list of entities giving an aggregated self description of the peer. 1 Ontology-Based P2P Knowledge Management Recently, a lot of effort has been spent at integrating the upcoming research areas of peerto-peer systems and the semantic web vision [12, 3, 1, 8], based on a notion of peer-to-peer, personal knowledge management (P2PKM for short). In such a scenario, users will model their knowledge – e.g., metadata on research papers they store on their computers – in personal knowledge bases, which can then be shared with other users via a peer-to-peer network. One crucial point in such a P2P network is that in order to find relevant material to match a user’s query, query messages need to be routed to peers which will be able to answer the query without flooding the network with unnecessary traffic. Several proposals have been made recently as to how the network can self-organize into a topology beneficial for routing, and how messages can be routed in a P2PKM network based on the abovementioned scenario [10, 11, 6, 13]. All of these are based on the idea of routing indices as proposed in [2], adapted to the P2PKM scenario. 1.1 P2P Network Model Following [10], we thus make the following assumptions about peers in a P2PKM network: • Each peer stores a set of content items. On these content items, there exists a similarity function called sim. We assume sim(i, j) ∈ [0, 1] for all items i, j, and the corresponding distance function d := 1− sim shall be a metric. For the purpose of this paper, we assume content items to be entities from a knowledge base (cf. Section 2.1), and the metric to be defined in terms of the ontology as described in section 2.2. • Each peer provides a self-description of what it contains, in the following referred to as expertise. Expertises need to be much smaller than the knowledge bases they describe, as they are transmitted over the network and used in other peers’ routing indices. A method of obtaining this expertise is outlined in Section 3. • There is a relation knows on the set of peers. Each peer knows about a certain set of other peers, i. e., it knows their expertises and network address (IP, JXTA ID). This corresponds to the routing index as proposed in [2]. In order to account for the limited amount of memory and processing power, the size of the routing index at each peer is limited. • Peers query for content items on other peers by sending query messages to some or all of their neighbors; these queries are forwarded by peers according to some query routing strategy. Using the sim function mentioned above, queries can thus be compared to content items and to peers’ expertises." @default.
• W156904288 created "2016-06-24" @default.
• W156904288 creator A5077726948 @default.
• W156904288 date "2005-01-01" @default.
• W156904288 modified "2023-09-24" @default.
• W156904288 title "Towards Content Aggregation on Knowledge Bases through Graph Clustering." @default.
• W156904288 cites W1979734411 @default.
• W156904288 cites W2087739686 @default.
• W156904288 cites W21125717 @default.
• W156904288 cites W2127136283 @default.
• W156904288 cites W2133109597 @default.
• W156904288 cites W2133959199 @default.
• W156904288 cites W2135805607 @default.
• W156904288 cites W2141430513 @default.
• W156904288 cites W2145012178 @default.
• W156904288 cites W2149230623 @default.
• W156904288 cites W2165050266 @default.
• W156904288 cites W2169497679 @default.
• W156904288 cites W73036368 @default.
• W156904288 cites W52126514 @default.
• W156904288 hasPublicationYear "2005" @default.
• W156904288 type Work @default.
• W156904288 sameAs 156904288 @default.
• W156904288 citedByCount "1" @default.
• W156904288 crossrefType "journal-article" @default.
• W156904288 hasAuthorship W156904288A5077726948 @default.
• W156904288 hasConcept C111472728 @default.
• W156904288 hasConcept C132525143 @default.
• W156904288 hasConcept C136764020 @default.
• W156904288 hasConcept C138885662 @default.
• W156904288 hasConcept C154945302 @default.
• W156904288 hasConcept C23123220 @default.
• W156904288 hasConcept C2522767166 @default.
• W156904288 hasConcept C25810664 @default.
• W156904288 hasConcept C2987255567 @default.
• W156904288 hasConcept C41008148 @default.
• W156904288 hasConcept C4554734 @default.
• W156904288 hasConcept C534932454 @default.
• W156904288 hasConcept C73555534 @default.
• W156904288 hasConcept C80444323 @default.
• W156904288 hasConcept C93518851 @default.
• W156904288 hasConceptScore W156904288C111472728 @default.
• W156904288 hasConceptScore W156904288C132525143 @default.
• W156904288 hasConceptScore W156904288C136764020 @default.
• W156904288 hasConceptScore W156904288C138885662 @default.
• W156904288 hasConceptScore W156904288C154945302 @default.
• W156904288 hasConceptScore W156904288C23123220 @default.
• W156904288 hasConceptScore W156904288C2522767166 @default.
• W156904288 hasConceptScore W156904288C25810664 @default.
• W156904288 hasConceptScore W156904288C2987255567 @default.
• W156904288 hasConceptScore W156904288C41008148 @default.
• W156904288 hasConceptScore W156904288C4554734 @default.
• W156904288 hasConceptScore W156904288C534932454 @default.
• W156904288 hasConceptScore W156904288C73555534 @default.
• W156904288 hasConceptScore W156904288C80444323 @default.
• W156904288 hasConceptScore W156904288C93518851 @default.
• W156904288 hasLocation W1569042881 @default.
• W156904288 hasOpenAccess W156904288 @default.
• W156904288 hasPrimaryLocation W1569042881 @default.
• W156904288 hasRelatedWork W105648562 @default.
• W156904288 hasRelatedWork W1965577272 @default.
• W156904288 hasRelatedWork W1982416413 @default.
• W156904288 hasRelatedWork W2039783163 @default.
• W156904288 hasRelatedWork W2073029339 @default.
• W156904288 hasRelatedWork W2078722472 @default.
• W156904288 hasRelatedWork W2089433749 @default.
• W156904288 hasRelatedWork W2111433546 @default.
• W156904288 hasRelatedWork W2380347437 @default.
• W156904288 hasRelatedWork W2505650114 @default.
• W156904288 hasRelatedWork W2550029747 @default.
• W156904288 hasRelatedWork W2904730138 @default.
• W156904288 hasRelatedWork W2938694938 @default.
• W156904288 hasRelatedWork W2950192025 @default.
• W156904288 hasRelatedWork W2951743068 @default.
• W156904288 hasRelatedWork W3038092411 @default.
• W156904288 hasRelatedWork W3127613131 @default.
• W156904288 hasRelatedWork W3176948757 @default.
• W156904288 hasRelatedWork W3209214893 @default.
• W156904288 hasRelatedWork W2242645577 @default.
• W156904288 isParatext "false" @default.
• W156904288 isRetracted "false" @default.
• W156904288 magId "156904288" @default.
• W156904288 workType "article" @default.