KEx:a Peer-to-Peer solution for Distributed Knowledge
程控电压
衰减器Management
M.Bonifacio1,2,P.Bouquet1,2,G.Mameli2,and M.Nori2
1Dept.of Information and Communication Tech.–University of Trento(Italy)
2Istituto per la Ricerca Scientifica e Tecnologica,Trento(Italy) Abstract.Distributed Knowledge Management is an approach to Knowledge
Management based on the principle that the multiplicity(and heterogeneity)of
perspectives within complex organizations should not be viewed as an obstacle
to knowledge exploitation,but rather as an opportunity that can foster innovation
and creativity.Despite a wide agreement on this principle,most current KM sys-
循环氢压缩机
tems are based on the idea that all perspectival aspects of knowledge should be
eliminated in favor of an objective and general representation of knowledge.In
this paper we propose a peer-to-peer architecture(called KEx),which embodies
the principle above in a quite straightforward way:(i)each peer(called a K-peer)
provides all the services needed to create and organize“local”knowledge from
an individual’s or a group’s perspective,and(ii)social structures and protocols
of meaning negotiation are defined to achieve semantic coordination among au-
tonomous ,when searching documents from other K-peers).
1Introduction
Distributed Knowledge Management(DKM),as described in[6],is an approach to KM based on the principle that the multiplicity(and heterogeneity)of perspectives within complex organizations should not be viewed as an obstacle to knowledge exploitation, but rather as an opportunity that can foster innovation and creativity.
The fact that different individuals and communities may have very different per-spectives,and that th
ese perspectives affect their representation of the world(and there-fore of their work)is widely discussed–and generally accepted–in theoretical research on the nature of knowledge.Knowledge representation in artificial intelligence and cog-nitive science have produced many theoretical and experimental evidences of the fact that what people know is not a mere collection of facts;indeed,knowledge always pre-supposes some(typically implicit)interpretation schema,which provide an essential component in sense-making(see,for example,the notions of context[18,7,13],mental space[12],partitioned representation[10]);studies on the social nature of knowledge stress the social nature of interpretation schemas,viewed as the outcome of a special kind of“agreement”within a community of knowing(see,for example,the notions of scientific paradigm[16],frame[15]),thought world[11],perspective[3]).
Despite this large convergence,it can be observed that the high level architecture of most current KM systems in fact does not reflect this vision of knowledge(see[5,6,4] for a detailed discussion of this claim).The fact is that most KM systems embody the assumption that,to share and exploit knowledge,it is necessary to implement a process
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of knowledge-extraction-and-refinement,whose aim is to eliminate all subjective and contextual aspects of knowledge,and create an objective and general representation that can then be reused by other people in a variety of situations.Very often,this process isfinalized to build a central knowledge base,where knowledge can be accessed via a knowledge portal.This centralized approach–and its underlying objectivist episte-mology–is one of the reasons why so many KM systems are deserted by users,who perceive such systems either as irrelevant or oppressive[9].
In this paper we propose a peer-to-peer(P2P)architecture,called KEx,which is coherent with the vision of DKM.Indeed,P2P systems seem particularly suitable to implement the two core principles of DKM,namely the principle of autonomy(commu-nities of knowing should be granted the highest possible degree of semantic autonomy to manage their local knowledge),and the principle of coordination(the collaboration between autonomous communities must be achieved through a process of semantic co-ordination,rather than through a process of semantic homogenization)[6].In KEx,each community of knowing(or Knowledge Nodes(KN),as they are called in[4])is repre-sented by a peer,and the two principles above are implemented in a quite straightfor-ward way:(i)each peer provides all the services needed by a knowledge node to create and organize its own local knowledge(autonomy),and(ii)by defining social structures and protocols of meaning negotiation in order to achieve semantic , when searching documents from other peers).
The paper goes as follows.In section2,we describe the main features of KEx,and argue why they provide a useful support to DKM;in3,we describe its implementation in a peer-to-peer platform called JXTA;finally,we draw some conclusions and future work.
2KEx:a P2P architecture for DKM
圣诞工艺品KEx is a P2P system which allows a collection of KNs to search and provide documents on a semantic basis without presupposing a beforehand agreement on how documents should be categorized,or on a common language for representing semantic information within the system.In the following sections,we describe the high-level architecture of KEx,and explain what role each element plays in a DKM vision.
2.1K-peers
KEx is defined as a collection of peers,called knowledge peers(K-Peers),each of which represents a KN,namely an individual’s or a group’s perspective on a given body of knowledge.Each K-peer can play two main roles:provider and seeker.A K-peer acts as a provider when it“publishes”in the system a body of knowledge,together with an explicit perspective on it(called a a topic hierarchy used to categorized local documents[8]);a K-peer acts as a seeker when it searches for information
by making explicit part of its own perspective,and negotiates it with other K-peers.
Each K-peer has the structure shown in Figure1.Below we illustrate the main modules and functionalities.
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Fig.1.The KEx’s main components
Document Repository.A Document Repository is where each KN stores its own local knowledge.We can imagine a private space in which the KN maintains its document and data,possibly using a local semantic ,afile-system structure,or a database schema),or a document management system in order to organize and access them.
Context Repository.Following[2],we define a context as a partial and approximate representation of the world from an individual’s or a group’s perspective.The reason why we adopt this notion of context is that it provides a robust formal framework(called Local Models Semantics[13])for modeling both contexts and their relationships.
In order to use contexts in KEx,we adopted a web-oriented syntax for contexts, called CTXML.It provides an XML-Schema specification of context for document organization and classification3.
In KEx,each context plays the role of a category system for organizing and classi-fying documents,or any other kind of digital information identifiable by a URI,stored in a document repository.Each peer can use more than one context to classify local knowledge;a K-peer’s contexts are stored in a context repository.
From the standpoint of DKM,contexts are relevant in two distinct senses:
3Currently,contexts are trees,whose nodes are labelled with words defined in some name space.
Arcs are Is-A,Part-Of or generic relations between nodes.Details can be found in[8].
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–on the one hand,they have an important role within each KN,as they provide a dynamic and incremental explicitation of its semantic perspective.Once contexts are reified,they become cognitive artifacts that contribute to the process of perspec-tive making[3],namely the consolidation of a shared view in a KN,continuously subject to revision and internal negotiation among its members;
–on the other hand,contexts offer a simple and direct way for a KN to make public its perspective on the information that that KN can provide.Therefore,as we will see,contexts are an essential tool for semantic coordination among different KN.
It is important to observe that contexts provide only a common syntax for classifica-tion structures.Indeed,we could see them as a language for wrapping any classification ,like directory systems,databases schemas,web directories).This means that in principle people can conti
nue to work with their preferred document manage-ment system,provided it can be wrapped using CTXML.
Context management module.The context management module allows users to cre-ate,manipulate,and use contexts in KEx.The module has two main components:
–Context editor:provides users with a simple interface to create and edit contexts, and to classify information with respect to a context.This happens by allowing users to create links from a resource(identified by a URI)to a node in a con-text.Examples of resources are:documents in local directories,the address of a database access services,addresses of other K-peers that provide information that
a KN wants to explicitly classify in its own context.大容量锂离子
电池 –Context browser:is part of Seeker component(GUI User in Figure1)and allows users to navigate contexts in the context repository.The main reasons for navigat-ing a context in KEx are two.Thefirst is obviously tofind document in the local knowledge repository by navigating the semantic structure.The second,and more important reason,is to build queries.The intuitive idea is that users can make con-text dependent queries(namely,from their perspective)by selecting a category in one o
f the available contexts.Once a category is selected,the context browser builds
a focus4–namely a contextual interpretation of the user’s query–by automatically
extracting the relevant portion of the context to which the category belongs.The focus is then used as a basis for meaning coordination and negotiation with other K-peers during the search.
2.2Roles of K-peers in KEx
Each K-peer can play two main roles:seeker and provider.Their interactions are repre-sented in Figure2,and described in detail in the following two sections.
Seeker As a seeker,a K-peer allows users to search for documents(and other infor-mation)from other K-peers and federations(see Section2.3).The seeker supports the 4See[17]for a formal definition of focus.
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Fig.2.The KEx system:interaction between Seeker and Provider roles
user in the definition of context-dependent queries through the context browser.A query is composed by a query expression and a focus.A query expression is a list(possibly empty)of one or more keywords provided by a user;a focus is a portion of a context determined by the category that the user has selected.Moreover,the seeker provides the discovery mechanism,used tofind resources to which the query is to be sent.The user decides to send the query to some of the available K-peers and federations.When the user submits the query,the seeker activates a session associated to that query(there can be only one active session for each seeker).In a session,a seeker can receive several asynchronous replies from the providers which resolved the query(through the mean-ing negotiation protocol,see below)and called back the seeker.The results returned to the user are composed by the aggregation of all the results received from the providers; each result is made up of a list of document ,name of the document, short description,and so on).Each result is presented together with the part of context that the provider has matched against the current query.This relationship between con-texts can be used as an opportunity for learning relationships across contexts of different KNs that the seeker can store and reuse for future queries(see section2.3).Finally,if one or more interesting documents are found,the seeker can contact the K-peers that have the documents and,if possible,download them.
Provider.The provider is the second main role in the KEx system.It contains the func-tionalities required to take and resolve a query,and to identify the results that must to
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be returned to the seeker.When a K-peer receives a context-dependent query(keywords +focus),it instantiates a provider(which is configured to use a set of contexts and to provide documents in a given portion of the knowledge repository),and tries to resolve the query in two ways:
–Semantic resolution:using a context matching algorithm[17],the provider searches for relations between the locally available contexts and the query’s focus.More specifically,the matching algorithm searches categories whose associated contex-tual information in the providers contexts matches(in a sense defined in[17])with the querys focus.If a match is found,the URIs of the resources associated to the provider’s context are returned to the seeker,together with a short information on the reason why a semantic match was found.If the matched category contains also links to resources in other K-peers,the provider propagates the query to those K-peers.
–Lexical resolution:using a keyword-based indexer,the provider searches for the occurrence of specific keywords into the set of documents of the local repository.
If the query contains only keywords,the provider will use only the lexical search; if it contains only a focus,the provider will use only the semantic search;if both are available,the outcome will be the result of intersecting the semantic and lexical results.
2.3K-Services
KEx provides a collection of services which have an important role in supporting knowledge exchange(that’s why they are called K-services).The main K-services are described in the following sections.矩阵干扰
Context normalization and enrichment.This service allows to perform a linguistic ,deleting stop words,tokenizing,part-of-speech tagging,etc.)on user defined contexts,and to use knowledge from an external linguistic , WordNet)to add semantic information to the categories in a context.
Normalization uses pretty standard NLP techniques,so we do not discuss it here.As to enrichment,it is applied offline to a context defined by a user(see[17]for details). It takes a user-defined ,a context built with the context editor)as input and returns a semantically enriched context as output.In our current implementation,the result is that linguistic ,senses,sy
nonyms,hierarchical relations with other categories,and so on)is extracted from WordNet and is“attached”to each context node label.
It is important to say why enrichment is not equivalent to introduce a shared(“ob-jective”)semantics in KEx.Indeed,the intuition is that the meaning of a label in each context node has two components:
–thefirst is the linguistic component,which means that the words used as labels have a meaning(or,better,a set of meanings)in a“dictionary”.This is used to record that many words have different ,“apple”as a fruit,“apple”as
a tree,and“apple”as a computer brand),even if only one of them is likely to be
led
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the relevant one in a given context;
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