hyo-xtm: a set of hyper-graph operations on xml topic map toward knowledge management

Future Generation Computer Systems 20 (2004) 81–100

HyO-XTM: a set of hyper-graph operations on XMLTopic Map toward knowledge management

Ying Dong∗, Mingshu LiLaboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing 10080, PR China

Abstract

Knowledge management is a critical issue for the next-generation web application, because the next-generation web isbecoming a semantic web, a knowledge-intensive network. XML Topic Map (XTM), a new standard, is appearing in this fieldas one of the structures for the semantic web. It organizes information in a way that can be optimized for navigation. In thispaper, a new set of hyper-graph operations on XTM (HyO-XTM) is proposed to manage the distributed knowledge resources.HyO-XTM is based on the XTM hyper-graph model. It is well applied upon XTM to simplify the workload of knowledgemanagement. The application of the XTM hyper-graph operations is demonstrated by the knowledge management system ofa consulting firm. HyO-XTM shows the potential to lead the knowledge management to the next-generation web.© 2003 Elsevier B.V. All rights reserved.

Keywords:Knowledge management; Topic maps; XML Topic Map; Hyper-graph

1. Introduction

Knowledge management (KM) is a critical issuefor the next-generation web application, becausethe next-generation web is becoming a seman-tic web in Berners-Lee’s vision[1], which is aknowledge-intensive network. Interest in knowledgemanagement has seen an exponential growth over thelast 2–3 years, since it plays an important role in pro-moting innovation and productivity of organizations[2–4].

XML Topic Map (XTM) is a new standard[5],emerging as one of the structures for the semanticweb. It is built on the ISO 13250 Topic Maps (TMs)[6]. TMs are used to organize information in a waythat can be optimized for navigation[7]. Moreover, atopic map can be modeled as a hyper-graph[8]. Graph

∗ Corresponding author.E-mail address:[email protected] (Y. Dong).

is an effective way to model the distributed knowl-edge network. Based on the graph model, knowledgemanagement problems can be studied and eased in agraphical way. However, those problems are far fromsolved, because there is lack of a bridge between thegraph model and the knowledge network model.

The main contribution of this paper is that we pro-pose a new set of hyper-graph operations on XMLTopic Map (HyO-XTM). The hyper-graph operationsare well-applied upon XTM in order to simplify theworkload of knowledge management. It is a new wayto bridge the graph model and the distributed knowl-edge network, so that the knowledge managementproblems can be mapped to the graph problems. Thetechnical strengths of HyO-XTM include:

(1) It is a new way managing the knowledge by graphoperations;

(2) The hyper-graph algorithms and operations are de-liberately designed to match between the knowl-edge model and the graph model;

0167-739X/$ – see front matter © 2003 Elsevier B.V. All rights reserved.doi:10.1016/S0167-739X(03)00166-3

82 Y. Dong, M. Li / Future Generation Computer Systems 20 (2004) 81–100

(3) HyO-XTM is capable of solving the difficultknowledge problems by the (combination of)hyper-graph operations.

The structure of the paper is as follows: InSection 2, the related work is summarized.Section 3introduces XML Topic Map and its hyper-graphmodel. InSection 4, the set of hyper-graph operationsHyO-XTM is proposed with the formal definitionsand detailed explanations. An XTM-based knowledgemanagement system of a consulting firm is providedin Section 5, to show how to apply the operationsand the potential of the new method. Finally is theconclusion and future work.

2. Related work

Knowledge grid, as a global model to share andmanage knowledge resources, relates to XTM. Otherrelated knowledge resource organizing mechanismsare also provided. XTM recently is developing fast,as an effective way for knowledge management.Since XTM can be modeled as a hyper-graph, thehyper-graph-based semantic web research efforts aresummarized. Moreover, other main semantic webknowledge structures are discussed.

2.1. Knowledge grid

Knowledge grid model for global knowledge shar-ing was early proposed by Zhuge[2]. The model or-ganizes knowledge in a three-dimensional knowledgespace. A knowledge grid operation language KGOLwas provided. By KGOL, the Internet users can cre-ate their knowledge grids, edit knowledge, and get therequired knowledge from the knowledge grids. Themodel enables people to conveniently share knowl-edge with each other when they work on the Internet.

VEGA-KG is a knowledge grid platform[3].VEGA stands for Versatile resources, Enabling intel-ligence, Global uniformity and Autonomous control.It includes two major components: a resource spacemodel that organizes information, knowledge, andservice resources; and an operable knowledge browserthat enables users to conveniently locate and man-age resources. This platform enables geographicallydistributed participants to share business knowledge.

2.2. Knowledge resource organizing mechanisms

Soft-devices are promising next-generation webresources[9]. They are software mechanisms thatprovide services to each other and to other virtualroles according to the content of their resources andconfiguration information. Configuring a resource ina soft-device is similar to installing software in acomputer. Soft-devices combine the advantages of theactive and intelligent features of intelligent agents,the semantics-based features of the semantic web, theconfigurable feature of hard devices, and the notionof the grid.

Active document framework (ADF) is a self-repre-sentable, self-explainable, and self-executable docu-ment mechanism[10]. The content of the documentis reflected by granularity hierarchy, template hier-archy, background knowledge, and semantic linksbetween document fragments. ADF supports not onlythe browse and retrieval services, but also intelligentservices like complex question answering and onlineteaching.

2.3. XML Topic Map

Topic maps began with the Davenport group in1991 to create a standard SGML DTD for softwaredocumentation[7]. This group spun off an offshootcalled CApH (Conventions for the Application ofHyTime), whose task included designing an applica-tion for computerized back-of-book indexes. The ideabehind the application were what eventually becametopic maps. Topic Map definesa standardized no-tation for interchangeably representing informationabout the structure of information resources used todefine topics, and the relationships between topics[6]. It was accepted by ISO, later supported by an or-ganization TopicMaps.Org, which produced the XMLsyntax for topic maps. Research on topic maps is de-veloping fast, as presented on the XML-Europe-Seriesconference and KT-Series conference after 2000.

2.4. Hyper-graph related research efforts

Hyper-graphs were used in data mining. Hyper-graph-based clustering consists of two steps: Thefirst step constructs a hyper-graph where the relateddata are connected via hyper-edges; the second step

Y. Dong, M. Li / Future Generation Computer Systems 20 (2004) 81–100 83

uses the hyper-graph partitioning algorithms to findpartitions. A solution to the problem of k-way par-titioning of a hyper-graph is provided by HMETISalgorithm[11]. [12,13] proposed the methods of dataclustering on a hyper-graph model in a high dimen-sional space. The clustering or grouping algorithmsof association rules have been proposed in[14–17].However, in hyper-graph-based data mining, graphmodeling is the first step. So the key problem is thedetermination of the related items to be grouped ashyper-edges.

Other graph-based semantic structures include se-mantic network[18] and conceptual graphs[19]. Thesemantic network is a representation formalism usedin AI research, also consists of nodes and links[18].An example of a semantic network is WordNet[20],a semantic dictionary built as a network. Topic mapsare much more general than WordNet. Moreover, theeffective semantic network based applications andtools need much more than the raw network, becausethe simple associative networks lack in representativepower and are difficult to search effectively whenthey get large. The conceptual graphs (CGs) theorydeveloped by Sowa is a language for knowledge rep-resentation[19]. Concept graph applications on theweb include[21,22]. CGs and topic maps are bothknowledge maps. Topic maps fit in the web envi-ronment more nicely than CGs, because they aregrounded on web ideas like URI and XML.

2.5. Other semantic web knowledge structures

Other main semantic web efforts include W3Cresource description framework (RDF)[23] andDAML + OIL language[24]. RDF was developed bythe W3C (World Wide Web Consortium), and RDFdata consists of nodes and attached attribute/valuepairs[23]. In RDF, resources only have properties thathave values, while in topic maps, topics have char-acteristics of various kinds (names, occurrences androles); RDF relates one thing to another, while topicmaps can relate any number of things by associations.DAML + OIL is a semantic web marking-up lan-guage, providing a rich set of constructs with which tocreate ontologies and to markup information, to makethe information machine readable and understandable[24]. DAML + OIL uses the same data model asRDF, while topic maps, on the other hand, are quite

different [25]. It was recently proven that the RDFand topic maps could inter-operate at a fundamentallevel [26].

The current research on the semantic web tries toimprove the web performances by exploiting the newsemantic structures[27]. For example, semantic webcontent mining and structure mining apply backgroundknowledge in the form of ontologies and the semanticannotations in order to improve the process, such asthe focused crawler[28] and ontology-based focusedcrawling [29]. Web usage mining can for instance beperformed on log files which register the user behaviorin terms of an ontology, as the SEAL system by AIFB[30]. In these efforts, ontology is always provided asa necessary support. It thus separates the system intotwo parts: a mining part and an ontology part. While intopic maps, the knowledge management system workson the knowledge level as a whole and without theconcerning of ontologies.

3. XML Topic Map and its hyper-graphmodel

In 1999 an international standard was developedto describe a mechanism for representing informationabout the structure of information and organizingit into “ topics”. These topics have occurrences andassociations that represent and define relationshipsbetween the topics. Information about the topics canbe inferred by examining the associations and oc-currences linked to the topic. A collection of thesetopics and associations is called a topic map[6].TopicMaps.Org produced the XML syntax for topicmaps, which was a reformulation of topic maps inXML syntax based on XLink[5]. They can be utilizedas an enabling technology for the new semantic web,in which information is given well-defined meaning,making it possible for computers and people to co-operate more effectively than ever before. XTM termdefinitions are shown inFig. 1.

A topic map can be considered as a hyper-graph.Hyper-graph is a generalization of graph concept,where an edge is incident with an unspecified num-ber of vertices[31]. There are three distinct sets ofhyper-graph elements. An element is eithervertex,edge, or incidence. Every incidencelinks exactly onevertex and one edge. A vertex and an edge linked by


Fig. 1. Class hierarchy of XML Topic Map.

an incidence are calledincident to each other. In thegraph theory, a hyper-graph can be represented by agraph that:

(1) the set of vertices is the union of the vertices andthe hyper-edges’ sets of the hyper-graph;

Fig. 2. Hyper-graph of the XTM example in Eg1.

(2) the set of edges is defined by the relation of in-cidence between vertices and hyper-edges of thehyper-graph.

The representing graph of a hyper-graph,H =(VH, GH), is a graphG where the set of vertices


is the union of the set of vertices and the edges ofthe hyper-graph, and there exists an edge betweentwo vertices if and only if one of the vertices isan hyper-edge incident with the other vertex:G =((VH �EH), {{v, e}, e�EH, v�e}).

An important property of the representative graphof hyper-graph is that the vertices can be decomposedinto two sets, such that there are no edges between anycouple of vertices belonging to the same set[32]. Inother words, the representing graph of hyper-graph isa bipartite graph. The hyper-graph model matches themain topic maps objects, called topic nodes and asso-ciation nodes in the terminology of XTM ProcessingModel [33]. Hyper-graph vertices map to topic nodes,edges map to association nodes, and incidences mapto links between topic nodes and association nodes.

In XTM hyper-graph model, every other topic mapproperty, including role specification, scope, topictype, association template, topic occurrence, topicname, topic subject, implies in fact crossing board-ers of semantic layers—represented by connectedcomponents[32]. The topic map hyper-graph modeltherefore has to include a mechanism to jump betweenconnected components.

Appendix A shows an example of XTM (Eg1).There are five topics, two associations, and three in-cidences. Its hyper-graph model and representativegraph are shown inFig. 2.

XTM represents a powerful new tool for distributedknowledge management, while the distributed envi-ronment is a cooperative working environment[34].Because it not only originates from the interesting ideaof topic maps, but also finds an efficient support formthe formal hyper-graph model.

4. The hyper-graph operations on XML TopicMap (HyO-XTM)

HyO-XTM is a new set of hyper-graph opera-tions based on the XTM hyper-graph model. Thehyper-graph operations are well-applied upon XMLTopic Maps in order to simplify the workload ofknowledge management. The hyper-graph operationsare designed following the three principles:

(1) Expressive considering of the knowledge-level se-mantics;

(2) Atomic and can be flexibly combined with theothers;

(3) Easily implemented.

HyO-XTM knowledge operations are introducedfrom single topic nodes, then bypassing associationsand instances, and then crossing the components inhyper-graph. For each operation, it is graphically de-fined and explained by the process semantics. Theprocess semantics show how the knowledge operationsemantically relates to the hyper-graph model. Anexample, from Eg1, is also provided to explain theapplication of the operation.

4.1. Knowledge operation primitives fora single node

In XTM, the information at a knowledge node istagged by XML label. For example, topic has XML la-bels<instanceof>, <subjectIdentity>, <baseName>,<occurrence> representing the topic’s “super class”,“ reified subject”, “ the base form of the topic name”,and “related information”, respectively. To get the spe-cific information according to users’ request, a set ofprimitive operations for the single knowledge node’sinformation retrieval is defined.

Definition 1. The primitive knowledge operation fora single knowledge node:

Node(Label[ sub-Label]);

Process semantics. The operation gets the in-formation labeled by<Label> (and its sub-label<sub-Label>) of Node). In other words, it retrievesthe piece of information from the node, which lo-cates between the labels<Label> and</Label> (orits sub-label<sub-Label> and </sub-Label>). Theoperation retrieves the specific information that asingle knowledge map node contains. It can satisfythe users’ request as “What is the exact informationabout the aspect marked by<label> of this topic (orassociation)?”

For example in Eg1:

(1) Knowledge operationt1(baseNamebaseName-String) means to get the information—“What isthe title of topic t1?”, and the result is “t1”;


(2) Knowledge operationa1(memberroleSpec) is tofind the answer for “What roles do association a1contain?”, and the result are “r1 a1, r2 a1”.

4.2. Find the adjacent nodes

In XTM, the associations represent the relationshipamong topic nodes. In the hyper-graph, the adjacentnodes of a certain node represent its related informa-tion. If the user wants to obtain the related informa-tion of a topic, the answer can come from its adjacentnodes.

Definition 2. The knowledge operation to find the ad-jacent nodes:

Adjacent(Node[[ConnectionNode][AdjNodeType]] );

Process semantics: Nodecan be either topic, asso-ciation, or incidence. The connecting nodeConnec-tionNode is an incidence node, which is connectedwith Node. Because an incidence node is connectedboth with topic nodes and with association nodes, theadjacent node’ typeAdjNodeTypecan be either “topic”or “association”. The operation finds the adjacentnodes for a certain node (connected by an incidencenode). It can satisfy the users’ request as “What as-pects does it relate with?” (All the hyper-graph knowl-edge operations of this type are shown inTable 1.)

For example in Eg1, knowledge operationAdjacent(t1i3 topic) = Adjacent(i3topic)–“t1” = (“ t4”, “ t5”). Itfinds the adjacent topic nodes fort1 connected by the

Table 1XTM Hyper-graph knowledge operation: find the adjacent nodes

Knowledge operation Function Request

Adjacent(tincidence) Find the adjacent incidence nodes fortopic t

What relationship does topict have?

Adjacent(aincidence) Find the adjacent incidence nodes forassociationa

What relationship does associationa have?

Adjacent(itopic) Find the adjacent topic nodes forincidencei

What topics is incidencei related with?

Adjacent(iassociation) Find the adjacent association nodes forincidencei

What associations is incidencei related with?

Adjacent(ti topic) (=Adjacent(itopic)-“ t”) Find the adjacent topic nodesconnected by incidencei for topic t

What topics is topict related to by incidencei?

Adjacent(ti association)(=Adjacent(iassociation))

Find the adjacent association nodeconnected by incidencei for topic t

What associations is topict related toby incidencei?

Adjacent(ai topic) (=Adjacent(itopic)) Find the adjacent topic nodes connectedby incidencei for associationa

What topics is associationa related toby incidencei?

incidence nodei3. The result means: “In this fields,topic t1 is related to topic t4 and t5.”

4.3. Find the sub-graph containing certainnode(s)

In Section 4.2, the knowledge operation to “find theadjacent nodes” can provide the related informationfor a certain node. However, the related informationonly contains the node information, and does not con-tain the edge information. However in XTM, edgesrepresent the relationship among topics. In order to getricher related information, this operation is designedto find in the XTM the sub-graph containing the cer-tain nodes.

Definition 3. The knowledge operation to find thesub-graph containing the certain nodes:

Graph(Node1[, Node2, . . . ]);

Process semantics. Graph(Node) gets the sub-graphof XTM containing theNode1; and Graph(Node1,Node2) gets the sub-graph of XTM containing bothNode1andNode2. Sub-graph of a node (some nodes)in XTM means a part of the XTM, which containsthe incidence nodes connected with the certain node(or all the certain nodes), and all the nodes and edgesconnected with these incidence nodes. The operationcan find in the XTM the close connected part of acertain part. The certain part can be either a node, orsome nodes. It can satisfy the users’ request as “What


Table 2XTM knowledge operation: find the sub-graph containing the certain node(s)

Knowledgeoperation

Function Request

Graph(t) Find the sub-graph for topict What knowledge and relationship is topict related with?Graph(t1, t2) Find the sub-graph for topict1 and t2 What knowledge and relationship are both topict1 and t2 related with?Graph(t, a) Find the sub-graph for topict and

associationaWhat knowledge and relationship are both topict and associationarelated with?

Graph(t, i) Find the sub-graph for topict andincidencei

What knowledge and relationship are both topict and incidenceirelated with?

Graph(a) Find the sub-graph for associationa What knowledge and relationship is associationa related with?Graph(i) Find the sub-graph for incidencei What knowledge and relationship is incidencei related with?

knowledge and relationship is it related with?” (Allthe hyper-graph knowledge operations of this typeare shown inTable 2.)

For example in Eg1:

(1) Knowledge operationGraph(t1,a1) find thesub-graph in the XTM containing both topic nodet1 and association nodea1. The result is shownin Fig. 3(1). It means “Topic t1 and associationa1 connect with each other for 2 times, one is byincidence i1, and the other is by incidence i2”.

(2) Knowledge operationGraph(t3) find the sub-graphfor topic nodet3. The result is shown inFig. 3(2).It means “Topic t3 is related with topic t1 andassociation a1 by incidence i2”.

4.4. Find the path between nodes

In XTM, nodes can be connected with each otherby more than one edge, or there is apath betweenthem. They may either be close or be far away fromeach other. To find a path between two nodes means

Fig. 3. Results of knowledge operationGraph() on Eg1.

to find the relationship between them. Furthermore, apath between two nodes represents a possible solutionto solve the target problem (at the destination) from astart problem (at the start point).

Definition 4. The knowledge operation to find thepath between two nodes:

Path(Node1, Node2);

Process semantics. The operation finds the pathbetweenNode1and Node2. It can satisfy the users’request as “How to studyNode2providing the in-formation for Node1?” or “Please offer a possiblesolution to solve the target problem, starting from theorigin problem”. So it can find a possible solutionfrom one problem to the other one. (The hyper-graphknowledge operation of this type is shown inTable 3.)

For example in Eg1, knowledge operationPath(t2,t5) finds the path between topic nodet2 and t5. Theresult of Path(t2, t5) = {t2 → i1 → t1 → i3 →


Table 3XTM hyper-graph knowledge operation: find the path between nodes


Path (t1, t2) Find the path betweentopic t1 and t2

How to studytopic2 if topic1 is known? or Please offer a possible solutionto solve the target problem, starting from the original problem?

t5}. It means “Starting from topic t2, it needs to passincidence i1 in order to get to know topic t1, thenfrom t1 needs to pass incidence i3, finally topic t5 canbe found and located.” Furthermore, it can mean “Tostudy or solve the problem of t5, you need to studyor pass the nodes i1, t1, i3 and t5, from the startingproblem at t2”.

4.5. Travel from a node

The best way to study a topic is the way customizedby the user’s request, characters, and interests. In or-der to provide a study course at different levels, theoperation “Travel from a Node” is designed. It is ca-pable of controlling the method of the navigation andthe degree it can reach. The methods of the travel in-clude two: one is the width-first traversal, the otheris the depth-first traversal. The degree of the travel iscontrolled by the step of the journey. Combing thesetwo concerns, the navigation from a knowledge nodeis controlled both by direction and steps.

Definition 5. The knowledge operation to navigatefrom a node:

Navigation(Node[, Method, Step]);

Process semantics. Node is the point to startthe navigation;Method can be either width-first or

Table 4XTM hyper-graph knowledge operation: navigation from a node


Navigation(t) Navigate from topict Please offer the information related to topict?Navigation(t,width,step) By width-first method and the certain steps . . . with a scope of [width] and the degree of [step]Navigation(t,depth,step) By depth-first method and the certain steps . . . with a profundity of [depth] and the degree of [step]

Navigation(a) Navigate from associationa Please offer the information related to associationa?Navigation(a,width,step) By width-first method and the certain steps . . . with a scope of [width] and the degree of [step]Navigation(a,depth,step) By depth-first method and the certain steps . . . with a profundity of [depth] and the degree of [step]

Navigation(i) Navigate from incidencei Please offer the information related to incidencei?Navigation(i,width,step) By width-first method and the certain steps . . . with a scope of [width] and the degree of [step]Navigation(i,depth,step) By depth-first method and the certain steps . . . with a profundity of [depth] and the degree of [step]

depth-first; andStep sets the degree the travel canreach. The operation navigates from a knowledgenode, with the method and the degree a user specifies.If the user is interested in the related information ofa wider scope, the width-first method can be applied;while if he is interested in the related informationthrough a clue, the depth-first method can be applied.How rich the information will be, or how difficultthe study process can be, can also be controlled bythe user, by setting the number of the steps. Theknowledge operation can satisfy the users’ request as“Please provide the related information by[method]with the degree[step]”, or “ I would like to studythe topic by[method] with the degree[step]”. (Allthe knowledge operations of this type are shown inTable 4.)

For example in Eg1:

(1) Navigation(i1, width, 2) = {t1, t2}. It means “Bywidth-first method, (degree = 2)’s informationunder incidence i1 relates to topic t1 and t2”;

(2) Navigation(i1, width, MAX) = {t1, t2, a1}. Itmeans “By width-first method, the widest-spaninformation under incidence i1 relates to topict1, t2 and association a1”;

(3) Navigation(i1, depth, 1) = {t1, t2, a1}. It means“By depth-first method, (degree= 1)’s informa-tion under incidence i1 relates to topic t1, t2 andassociation a1”;


(4) Navigation(i1, depth, 2) = {i2, i3}. It means “Bydepth-first method, (degree = 2)’s informationunder incidencei1 relates to the other two inci-dences i3 and i4”.

4.6. Add new node

A knowledge base is a growing repository. Forthe XTM to become a growing-up knowledge base,it should be capable of absorbing new knowledge.Herein the operation is designed to support XTM’sgrowing-up process. It offers a way for the XTMsystem to study new knowledge.

Definition 6. The knowledge operation to add a node:

Add(Node);

Process semantics. Nodecan be either topic, asso-ciation, or incidence node. The operation adds newknowledge into XTM. The new knowledge is studiedeither by adding new knowledge nodes (when addingnew topic/association node), or by adding new knowl-edge edges (when adding new incidence node). Theoperation can support the system’s studying processas “Please let the system study the new knowledge(of a new topic, association, relationship)”. (All thehyper-graph knowledge operations of this type areshown inTable 5.)

For example in Eg1, through the operationAdd(i4),while i4 = {t2, t6, a3}, the new XTM in Eg1 turnsto beFig. 4. It means the system has studied the newrelationship knowledge between topict2, t6 and asso-ciationa3.

4.7. Knowledge operation among connectedcomponents of a map

Most of the knowledge operations above are de-fined on one connected graphs. However, an XTM

Table 5XTM hyper-graph knowledge operation: add new node


Add (t) Add new topic nodet Let the system study the new knowledge about a topic?Add (a) Add new association nodea Let the system study the new knowledge about an association?Add (i) Add new incidence nodeI Let the system study the new knowledge about an incidence?

Fig. 4. Results of the operationAdd(i4) on Eg1.

can be not a complete connected graph, but composedof some connected components instead. Each of theconnected components represents a cluster or a fieldof knowledge. Despite of the operations on a con-nected component, the knowledge operations betweenthe connected components are designed.

An XTM contains several connected componentsbecause:

(1) it is too complicated if weaving all these compo-nents together, so it is better to separate them toseveral parts; or

(2) there is no clear relationship between theseparts.

In case of (1), there may be the same node(s) in thedifferent connected components; while in case of (2),there may not be a same node in the different com-ponents. As for the separate and distributed knowl-edge, users may want to weave them together. In otherwords, users may need to gather the information about


the knowledge node in different fields. So two knowl-edge operations are designed here: one finds thoseconnected components that contain a same node; theother interweaves together the knowledge of the nodein the different connected components.

Definition 7. The knowledge operation to find theconnected components containing a certain node:

Same(Node);

Definition 8. The knowledge operation to inter-weave together the separate knowledge of a certainnode:

Interweave(Node);

Process semantics. Nodecan be either topic or as-sociation node, or even a certain attribute of a node;Same(Node) finds all the connected componentsthat contain the [Node] or the attribute; andInter-weave(Node) shows all the information of [Node] orthe attribute in different connected components. Theoperations can provide all the information of a samenode although it appears in the different connectedcomponents of XTM. In XTM, knowledge of differ-ent fields is clustered in separate connected compo-nents. So the operation gaps the different fields. It canbecome a way to provide a study course across thedifferent fields and scopes. The operation can satisfythe users’ request as “Please tell me which fields re-late to the certain knowledge?” or “Please provide theinformation of the certain knowledge in the differentfields?” (All the hyper-graph knowledge operationsof this type are shown inTable 6.)

Table 6XTM knowledge operation: knowledge operation among connected components


Same(t) Find all the connected components containing topict Which fields relate to topict?Same(a) Find all the connected components containing associationa Which fields relate to associationa?Same(att) Find all the connected components containing attribute “att” Which fields relate to attribute “att”?

Interweave(t) Show the information of topict in different fields Please provide all information about topict inthe different fields?

Interweave(a) Show the information of associationa in different fields Please provide all information aboutassociationa in the different fields?

Interweave(att) Show the information of attribute “att” in different fields Please provide all information about attribute“att” in the different fields?

5. Case study

In this section, a case is provided to show howto apply HyO-XTM for knowledge management.This case is a LLP law firm providing the consult-ing services. The firm helps the high-tech companiesconsult their legal problems. As the routines of theconsulting companies, it serves the clients by launch-ing case studies on the their products, services, legalenvironment, and the related problems. As a consult-ing firm, the case resources and the studying pro-cesses are the firm’s main assets. So the knowledgemanagement is a critical issue in such a consultingcompany.

Before applying HyO-XTM, the XML Topic Mapsare firstly designed for the knowledge organization.It models the distributed knowledge network as XMLTopic Maps. The HyO-XTM knowledge operationsdescribed inSection 4have been implemented asa toolkit. Then six scenarios are provided applyingHyO-XTM to manage the distributed knowledge forthe company.

5.1. The consulting company’s XTM knowledgestructure

In the high-tech law consulting firm, the XTM KMsystem manages knowledge including topics, associa-tions and incidences.

(1) As for topics, there are 10 types (Appendix B(1)):Market, Client, Competitor, Product, Service,Case, Person, Law, Document, and Web Infor-mation;


Fig. 5. XTM Component0for the high-tech law consulting firm XTM demonstration.

(2) As for associations, there are 22 types (AppendixB(2)): for example,a4 represents the associationof “case-serving-client”, and the two roles of it are“client” and “case”, respectively;a17 representsthe association of “service-related-webinfo”, andit has roles of “webinfo” and “service”;

(3) As for incidences: there is an example incidenceof associationa17 (Appendix B(3)). Its mem-ber “WebInfo1” (a piece of news for theSoft-ware Integration Services) has the association“service-related-webinfo” with the other member“Service0” (Software Integration Service).

The knowledge organization of the firm is composedof two parts: One part describes the firm’s own in-formation, including its business scope, clients, com-petitors, and the interested products and services (thispart of XTM is XTM Component0). The other partof knowledge come from the cases and organized bythe cases. For each of the cases, the knowledge in-cludes the client, consultants, products, services, laws,research report, and the related web information. Forexample, the firm is currently conducting two cases,

one serving a company callede-Tao, and the otheris for SAPCompany. The XTM part for each of thetwo cases are represented byXTM Component1andXTM Component2, respectively.

• XTM Component0(Fig. 5). It gathers the firm’sown information. The firm focuses on thehigh-techmarket (Market0). Now it has two clients, one ise-Tao Company(http://www.etao.net) (Client0) andthe other isSAP Company(http://www.sap.com)(Client1). In this market, the firm is now interestedin the productEnterprise Management Software(Product0) and the Software Integration Service(Service0). One competitor is the famous consult-ing companyMcKinsey(Competitor0).

• XTM Component1(Fig. 6): The firm has a cliente-Tao company (whose case isCase0). e-Tao doesbusiness in theEnterprise Management Software(Product0) and the Software Integration Service(Service0). Its legal problem is mainly on theCopy-right of software. The consulting firm sentCaro-line (Person0) andGrace(Person1) to conduct thecase. They produced areport (Document0) in the

http://www.etao.net

http://www.sap.com


Fig. 6. XTM Component1in the high-tech law consulting firm XTM, for the e-Tao company.

end, and found related web information (WebInfo0and WebInfo1) as the references and the learningby-products.

5.2. Knowledge paradise in the high-techconsulting firm

In this section, it shows how to apply HyO-XTMon the knowledge management in the consulting firm.All the services the firm can provide are based both onthe study process from the clients and on the experi-

ences the firm has had. So the firm is in fact a knowl-edge paradise, in that knowledge becomes the key is-sue in doing its businesses. By carefully designing,combining, and applying the knowledge operations,the firm does business based on its knowledge man-agement system. The knowledge-intensive businessesinclude:

(1) external knowledge services;(2) internal knowledge management; and(3) new businesses, coming from the new knowledge

mined from the available information.


Applying HyO-XTM knowledge operations, twoscenarios are shown for each of the three types of busi-ness.

5.2.1. External knowledge servicesThe firm provides the consulting services to clients.

At the different levels, the services include answeringthe frequently asked questions (FAQs), and others ac-cording to the clients’ requests.

Scenario 1. Answering FAQs: General ques-tions can be answered by showing the hyper-graphconnected component on the firm’s basic informa-tion (XTM Component0). Applying the single-nodehyper-graph operationMarket0(baseNamebaseName-String) on it, the question “What market does thefirm study?” can be answered; While applyingAdjacent(Market0a5 topic), the question “What arethe successful cases the firm has done?” is answered.Since the results of the two operations are “High-techMarket” and {“e-Tao”, “ SAP”}, respectively, it showsthat “the firm is professional in theHigh-tech Mar-ket; and the successful cases including serving thecompanye-Tao, SAP, etc.”

Scenario 2. Provide related information on theinterested product(s): Since e-Tao company is an im-portant client, the firm needs to provide the relatedinformation on the product(s) e-Tao is interested in.XTM Component1is the connected XTM componentfor e-Tao’s case. Applying the hyper-graph operationAdjacent(a6topic) on XTM Component1, it can findthe product(s) which e-Tao is interested in (sincea6is the association for “client-interestedin-product”);and then applyingGraph(a15, Product) on eachof the XTM connected component, it can findall the web information the firm has had relatedwith the product(s) (sincea15 is the associa-tion for “product-related-webinfo”). Let us studythese operations’ results: sinceAdjacent(a6topic) ={“Product0”}, the firm finds out that e-Tao company

is interested in theEnterprise Management Software;sinceGraph(a15, Product0) = {“WebInfo0”}, the firmcan provide e-Tao with the appropriate pieces of webinformation e-Tao is interested in.

5.2.2. Internal knowledge managementWhatever knowledge services the firm provides for

clients, it should build up an efficient knowledge man-agement system internally.

Scenario 3. Produce Case Profiles: On the con-nected component for each case, applying the hyper-graph operation Navigation(Case, depth, 2), itnavigates from theCasenode bydepth-first and2-stepdeep traversal. Since the operationNavigation(Case,depth, 1) covers all the incidence nodes connected totheCasenode, the operationNavigation(Case, depth,2) will cover all the topics and associations relatedwith theCasenode.

For example, to fulfill the request as “Pleaseprovide the profile for the SAP Company’s case”,the firm applies the hyper-graph operationNaviga-tion(Case1, depth, 2) on XTM Component2(SAP’scase is labeled by “Case1”, and its XTM componentis XTM Component2, which organization is similar toXTM Component1). Since Navigation(Case1, depth,2) = {{a4, Client1}, {a0, Person1}, {a0, Person2}, {a1,

Law1}, {a2, Document1}, {a3, WebInfo0}}, accordingto Appendix B, the SAP Company’s case profile canbe referred toTable 7.

Scenario 4. Different level knowledge publishing:Despite of the intelligent knowledge retrieval, theknowledge system should be capable of publishingknowledge intelligently and flexibly.

For example, the firm can apply the following op-erations for the abstract/index publishing:

(1) Integrating all the sub-graphs ofGraph(Case,a4) on each case’s connected component, it canproduce the cases’ information indexed by client(sincea4 is “case-serving-client” in the demo);


Table 7The case profile forSAP Company

Contentslot no.

Slot Content

1 Offering service for SAP Company2 Consultant Grace Leung, Harry Polo3 Governing laws Patent Law4 Case (research) report Research report of SAP5 Related web information News for software

management software

(2) Integrating all the sub-graphs ofGraph(Case, a0)on each case’s connected component, it can pro-duce the cases’ information indexed by consul-tant (sincea0 is “case-having-personnel” in thedemo);

Thus, the case publication by the index of “Client”and “Consultant” can be referred toTables 8 and 9,respectively.

Moreover, applyingNavigation()on a node withthe different traverse methods and steps, it canpublish the node’s information at different levels.The hyper-graph operationNavigation(Node, width,step) publishes the node’s information on a samelevel of different sections, with section number asNode.step (of the total numberNode.width); and

Table 8Cases indexed byClient

Index no. Client Case

1 e-Tao Company Case02 SAP Company Case1

Table 9Cases indexed byConsultant

Index no. Consultant Case

1 Caroline Yun e-Tao Company (Case0)2 Grace Leung e-Tao, and SAP

Company (Case0, Case1)3 Harry Polo SAP Company (Case1)

Navigation(Node, depth, step) publishes the node’sinformation on a different level of sections, withsection number asNode.d1.d2. . . step (of the to-tal number Node.d1.d2. . . depth). On e-Tao’s case(XTM Component1), sinceNavigation(i1, width, 2) ={a0, Person0}; Navigation(Case0, depth, 1) = {i0, i1,

i2, i3, i6, i7, i8}; and Navigation(Case0, depth, 2) ={{a4, Client0}, {a0, Person0}, {a0, Person1}, {a1, Law

0}, {a2, Document0}, {a3, WebInfo0}, {a3, WebInfo1}},the different-level publications on e-Tao company’scase are shown inTable 10.

5.2.3. New knowledge creation and new servicesMore than fulfilling the clients’ requests, the

consulting firm can create new services based on

the new knowledge mined from the available knowl-edge. The new knowledge services aim to providesuggestions for clients to improve their businessperformance based on the understanding of theirproblems, interests, or the current situations.

Scenario 5. Finding out the hot news: For eachpiece of news, applying the hyper-graph operationSame(News) on all the connected components ofthe XTM; Then the firm can choose the piece ofnews who has the max number of |Same(News)|,providing it as the hottest news to the interestedclients.

For example, sinceSame(WebInfo0) = {XTMComponent1, XTM Component2}, and Same(Web-Info0) = {XTM Component1}, by a comparison it getsthat Max{|Same(WebInfoi)|} = |Same(WebInfo0)| =2. So the firm finds out that “the most popular newsrecently in the market is theNews for software man-agement software.”

Scenario 6. Production Proposal: When a productbecomes popular, the amount of the web informa-tion relating to it will grow tremendously. The firmassumes that the amount of the news on a productreflects its popularity. When the product is becomingtoo hot, the firm can provide a proposal on reducing


Table 10Different-level information publishing on thee-Tao’s case

Section no. Content Sub-section no. Sub-section content

Information publishing on the e-Tao Company’s case (Section 2)2 i1 2.0 case-having-personnel (a0)

2.1 Caroline Yun (Person0)

Information publishing on the e-Tao Company’s case (Level 1)1 Incidence ofa0: case-having-personnel (i0)2 Incidence ofa1: case-ruledby-law (i1)3 Incidence ofa2: case-having-document (i2)4 Incidence ofa3: case-having-webinfo (i3)5 Incidence ofa6: client-interestedin-product (i6)6 Incidence ofa7: client-interestedin-service (i7)7 Incidence ofa8: competitor-interestedin-market (i8)

Information publishing on the e-Tao Company’s case (Level 2)1 1.0 Case-serving-client (a4)

1.1 e-Tao Company (Client0)

2 2.0 case-having-personnel (a0)2.1 Caroline Yun (Person0)

3 3.0 case-having-personnel (a0)3.1 Grace Leung (Person1)

4 4.0 Case-ruledby-law (a1)4.1 Copyright Law (Law0)

5 5.0 case-having-document (a2)5.1 Research report of e-Tao (Documnt0)

6 6.0 case-having-webinfo (a3)6.1 News for software management software (WebInfo0)

7 7.0 case-having-webinfo (a3)7.1 News for software integration services (WebInfo1)

the production; otherwise, the proposal can suggestexpanding the production.

The market capacity is thus measured by the webnews statistics. For example, [ProductExcessNum-ber, ProductShortageNumber] are, respectively,the upper and lower limit of the web news amountto trigger the proposal. Sincea15 represents

Table 11Production proposal

Reason and situation Suggestion

Hyper-graph operation Situation Hyper-graph operation For clients

(The number of topic nodes of� Graph(a15,Product∗)) >ProductExcessNumber

The Product∗ isvery popular

Proposal(ReduceProduct,Product∗)

Reduce the productionof the Product∗

(The number of topic nodes of� Graph(a15,Product∗)) <

ProductShortageNumberThe Product∗ isnot popular

Proposal(ProduceProduct,Product∗)

Expand the productionof the Product∗

“product-related-webinfo”, the hyper-graph opera-tion Graph(a15,Product∗) is applied on Product∗on all the connected components of the XTM. If{(the number of topic nodes of�Graph(a15, Product∗))> ProductExcessNumber}, then the firm can providethe Proposal(ReduceProduct, Product∗); otherwise,if {(thenumberoftopicnodesof�Graph(a15, Product∗))

< ProductShortageNumber}, thenProposal(ProduceProduct, Product∗) is provided. These operations areshown inTable 11.

From the scenarios above, the potential of the setof knowledge operations HyO-XTM has been shown.A combination of them by careful design can fulfillthe users’ request, including intelligent knowledge re-trieval, providing services such as solving problems,providing possible solutions, making statistics, etc.The knowledge management system adopting XTMhyper-graph knowledge structures and HyO-XTMcan manage the organization’s knowledge easily,efficiently and flexibly.

6. Conclusion and future work

In this paper, a new set of hyper-graph operationson XML Topic Map, named HyO-XTM, was pro-posed towards knowledge management. It connectsthe hyper-graph model with the knowledge network.Applying HyO-XTM on the XTM hyper-graph model,the workload of knowledge management can be sim-plified. It fits well in the knowledge-intensive business,such as consulting firms, law firms, and professionalservice-providing companies. According to the avail-able methods, HyO-XTM has three main advantages:

(1) Graphically manage the knowledge. All the oper-ations are based on the XTM hyper-graph.

Appendix A

An example of XTM (Eg1)

<topic id=“ ti”>

<baseName><baseNameString>ti</baseNameString></baseName></topic>

<association>

<instanceOf><topicRef xlink:href=“#a1”/></instanceOf><member>

<roleSpec><topicRef xlink:href=“#r1 a1”/></roleSpec><topicRef xlink:href=“#t1”/>

</member><member>

The graphic operations can deal the complicatedknowledge structures with ease.

(2) At high-level manage the knowledge. HyO-XTMworks on the XML Topic Map. XTM modelsthe knowledge network by topics, associationsand instances, working at a high expressivelevel.

(3) Distributedly manage the knowledge. The knowl-edge in the different fields is organized byconnected components in the hyper-graph. Thecomponents reflect the distributed features of theknowledge structure, while HyO-XTM is capableof applying the operations across the hyper-graphcomponents.

HyO-XTM can become a new way to managedistributed knowledge. The future work includes theresearch on the methodology to set up XML TopicMap knowledge networks, applying HyO-XTM on alarge-scale knowledge network, and on the relation-ship between XTM and other knowledge engineeringproblems.

Acknowledgements

This work was supported by China National HighTechnology Research and Development Program (863Program) (No. 2001AA113180) and National NaturalScience Foundation (No. 60273026).

Appendix A (Continued)


</member></association><association>



</member><member>


</member></association><association>



</member><member>

<roleSpec><topicRef xlink:href=“#r2 a2”/></roleSpec><topicRef xlink:href=“#t1”/><topicRef xlink:href=“#t4”/>

</member></association>

Appendix B. The topics, associations, and incidences of the XML Topic Map in the case study

(1) Topics

Topic type Subject Topic examples Label

Market Business scope High-tech Market Market0

Client Offering service for e-Tao Company Client0SAP Company Client1

Competitor Other consulting companies McKinsey Competitor0

Product Products in this field Enterprise Management Software Product0

Service Services in this field Software Integration Service Service0


Appendix B (Continued)

Topic type Subject Topic examples Label

Case Case The Case for e-Tao Case0The Case for SAP Case1

Person Consultant Caroline Yun Person0Grace Leung Person1Harry Polo Person2

Law Laws and regulations Copyright Law Law0Patent Law Law1

Document Case (research) report Research report ofe-Tao Company

Document0

Research report ofSAP Company

Document1

Web information Related web information News for enterprisemanagement software

WebInfo0

News for softwareintegration services

WebInfo1

(2) Associations

Id Name Role 1 Role 2

a0 case-having-personnel person casea1 case-ruledby-law law casea2 case-having-document document casea3 case-having-webinfo webinfo casea4 case-serving-client client casea5 client-interestedin-market market clienta6 client-interestedin-product product clienta7 client-interestedin-service service clienta8 competitor-interestedin-market market competitora9 market-having-product product marketa10 market-having-service service marketa11 market-ruledby-law law marketa12 market-related-document document marketa13 market-related-webinfo webinfo marketa14 product-related-document document producta15 product-related-webinfo webinfo producta16 service-related-document document servicea17 service-related-webinfo webinfo servicea18 document-having-author document persona19 document-related-law law documenta20 document-related-webinfo webinfo document


(3) An incidence example of associationa17 (two related topics are also listed)

<association><instanceOf><topicRef xlink:href=“#service-related-webinfo”/></instanceOf><member>

<roleSpec><topicRef xlink:href=“#webinfo”/></roleSpec><topicRef xlink:href=“#WebInfo1”/>

</member><member>

<roleSpec><topicRef xlink:href=“#service”/></roleSpec><topicRef xlink:href=“#Service0”/>

</member></association><topic id=“webinfo”>

<baseName><baseNameString>Web information</baseNameString>

</baseName></topic><topic id=“WebInfo1”>

<instanceOf><topicRef xlink:href=“#webinfo” /></instanceOf><baseName>

<baseNameString>News for enterprise management software</baseNameString></baseName><occurrence>

<resourceData>SAP Launches mySAPTM ERP, Most Comprehensive and Extendable ERP Solution.</resourceData>

</occurrence><occurrence>

<resourceRef xlink:href= “www.sap.com/company/press/press.asp?pressID=2089”/></occurrence>

</topic>

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Ying Dong received the BSc degree inComputer Sciences from Peking Uni-versity, China, in 1998. She is cur-rently a PhD student at the Instituteof Software (IOS), Chinese Academy ofSciences (CAS). Her research interestsare knowledge management, e-business,multi-agent systems, intellectual property.

Mingshu Li is a professor and the Di-rector of Institute of Software at Chi-nese Academy of Sciences. He receiveda PhD degree from Department of Com-puter Science in 1993, a Master degree(secondary one) in Economics from De-partment of Social Sciences in 1995, bothat Harbin Institute of Technology, China.He took his post-doctoral research at IOSof CAS (1993–1995), and at Department

of Artificial Intelligence, University of Edinburgh (1995–1996).He is the Standing Member of the Chinese Computer Federationand a member of IEEE. His current research interests are softwareengineering, real-time systems, Internet/web technologies.

http://kg.ict.ac.cn

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http://www.topicmaps.net/pmtm4.htm

hyo-xtm: a set of hyper-graph operations on xml topic map toward knowledge management

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