Trust on the Semantic Web Pyramid: Some Issues and Challenges · 2017-09-27 · Trust on the Semantic Web Pyramid: Some Issues and Challenges 3 2 Some Issues and Challenges Trust

Trust on the Semantic Web Pyramid: SomeIssues and Challenges ?

Yuh-Jong Hu, Se-Ting, Chen, and Min-Huei, Yang

Emerging Network Technology (ENT) Lab.Dept. of Computer Science, National Chengchi University

Wen-Shan District, Taipei, Taiwan, 116{jong, g9011, 9020}@cs.nccu.edu.tw

Abstract. The ultimate goal of semantic web is evolving the existingWWW into a new frontier where agent has the ability to directly con-sume information without human intervention on the Web. We proposethe semantic web pyramid to explain why semantic web is so impor-tant on playing a pivoting role to other emerging core technologies. Eventrust was declared as the top of layer on the semantic web layer stack.Certainly, it will not be the last and least thing for us to consider whiledeveloping the semantic web. In this paper, we show how to achieveagent’s authenticate, authorize, and delegate operations using trust on-tology and trust rules for the semantic web of trust. A system imple-mentation framework for the trusted semantic web will be addressed todemonstrate the feasibility of our proposal.

Keywords: semantic web trust and security; semantic web inferenceschema; semantic web for web service

1 Introduction

There are several emerging core technologies being developed recently that areclosely related to the semantic web research. They are web services, agent, gridcomputing, peer-to-peer computing, and ubiquitous computing. If we put thesemantic web in the center of the pyramid base, then the successful develop-ment of semantic web will leverage other emerging core technologies to give usa comprehensive Web service environment (see Fig. 1).

In fact, some of above ongoing studies have been made some progress recentlyin this semantic web pyramid [1][7][16]. If we all agree that bring the trusted,ubiquitous, and semantic web services is the future moving direction, then thoseemerging core technologies will eventually converge to the web services corner-stone.

There are many facets of trust for the semantic web or other emerging coretechnologies. Trust can be defined as reputation mechanisms that rely on a

? This research was supported by Taiwan National Science Council (NSC) under grantsNo. NSC 91-2213-E-004-014.

2 Yuh-Jong Hu, Se-Ting, Chen, and Min-Huei, Yang

collaborative rating and personalized evaluation [27]. In terms of delegation,trust is a necessary but not a sufficient condition for delegation [4]. In [12][13],we define trust as an authentication, authorization, and delegation verificationproblem for agents using digital certificates showing and verification protocols.But we did not exclude the other trust facets when the ultimate trusted semanticweb is achieved.

Trust is usually the last but not the least thing for people to concern whenthey build a system. So why we worry about trust issue at this moment? Espe-cially when the trust layer was declared as the top of layer on the semantic weblayer cake. If we agree that proof and trust are applications rather than a newontology language on the layer stack, then it will not hurt to explore the trustissues at current stage [22].

There are several important results on agent trust based on psychology andsecurity viewpoints [4][10][12][25]. Trust (or security) is also one of the importantissues for web service and grid computing in the semantic web pyramid [15][20].When we compare with other emerging technologies, the research progress forthe trusted semantic web is very slow and the results are scarce [16][26].

Fig. 1. The trusted semantic web on a pyramid framework where all of emerging coretechnologies converge to web services cornerstone.

In this paper, trusted semantic web was defined as well-defined trust on-tologies and trust rules in the agent interaction protocols so that agent’s accesscontrol services, such as authentication, authorization, and delegation can beachieved. This approach not only solves the agent’s authenticity and authorityproblems but also provides the possible capacity to resolve information propa-gation authenticity, ontology and rule integrity issues in the future. In the trust

traversing path,(d)−→,

(b)−→,(c)−→ (see Fig. 1), the ontology and rule techniques

will be leveraged on agent trust control. In another trust traversing path,(e)−→,

(b)−→, ,(a)−→, agent technology will be leveraged on the building and verifying of

authenticity and integrity of ontology and rule.

Trust on the Semantic Web Pyramid: Some Issues and Challenges 3

2 Some Issues and Challenges

Trust sits on top of the semantic layer stack with proof, signature, and encryptionas its underlying layers [8]. The signature and encryption two pillars can beapplied to all stack layers to achieve the trusted semantic web’s ultimate goal.The possible issues and challenges to build the trusted semantic web will beshown as the followings:

1. We must find out what are the implications when trust sheds light on thepyramid base to leverage the semantic web for other emerging core technolo-gies. Because different traversing path from trust node to web services nodemight indicate different unresolving issues and challenges.

2. We need to explicitly specify the possible trust criteria so that trust conceptscan be shown as ontologies and rules for agent to initiate the trust controlprotocols. Furthermore, we also need to construct the trust logic frameworkto carry out the trust verification and proving processes via automatic rea-soning using those trust ontologies and trust rules.

3. Consequently, there are several design and implementation issues closelyrelated to above item 2’s main theme:– The trust ontologies and trust rules for trust verification and proving via

automatic reasoning.– The coverage and usage of security mechanisms on the declaration and

certification of trust criteria.– The agent communication language and protocols that have semantic

level messages exchange to realize the trust verification and validation.– The infrastructure of information processing flow to depict the trust

verification and validation processes.– The information collection and fusion mechanism to verify and validate

the satisfaction of trust criteria based on positive, negative or unknownstatus information.

In this paper, we examine some of the above issues and challenges for pro-viding total or partial solutions. First, we show several possible relations on the

semantic web pyramid. We primarily focus the triangle relations shown as(a)−→,

(b)−→,(c)−→. Then, we explain what are the implications for trust when it lights up

this triangle indicated as(d)−→,

(e)−→, and(f)−→. Finally, we show that agents have

semantic messages exchanged on the semantic web using agent communicationlanguage and protocols to achieve trust verification and validation.

3 Semantic Web Pyramid

We are going to analyze the structure of semantic web pyramid in perspective.First, several possible relations on the base of semantic web pyramid will beshown. Then, we show how to shed trust light on the pyramid base.


3.1 Relations on the Pyramid

– Semantic web vs. agent (see(b)−→)

One of the important driving forces for the development of semantic web isto semantically markup the information on the web so that agents can parseand consume information directly [11]. Consequently, agent plays the crucialrole in the success of semantic web. If agents can not unambiguously interactwith each other using ontologies and rules on the semantic web seamlessly,then we can not said we have successfully built the semantic web.

– Semantic web vs. web services (see(a)−→)

The opportunity and challenge for the semantic web is to bring semanticsto web services to enable intelligent web services. DAML-S is one of themost well-known ontologies for its automatic services discovery, selection,invocation, composition, and monitoring [1]. However, to distill mature se-mantic web technologies for web services and show paths to go still need toovercome.

– Agent vs. web services (see(c)−→)

Agent-mediate e-commerce (or e-service) was extensively studied in the agentresearch community for the past decade and it had delivered several impor-tant results [19][21].

– The triangle relation (see(a)−→,

(b)−→, and(c)−→)

The momentum of semantic web will be realized only when we have tremen-dous amount of agents on the fully loaded semantic web with ontologymarkup information. This vision was shown in a pivoting essay written byTim Berners-Lee et al. [23]:

The real power of the Semantic Web will be realized when people cre-ate many programs that collect Web content from diverse sources,process the information and exchange the results with other pro-grams. The effectiveness of such software agents will increase ex-ponentially as more machine-readable Web content and automatedservices (including other agents) become available · · · .

Unfortunately this dream is still far away for the lack of solid infrastructureand semantic level agent communication protocols to operate multi-agentsystems seamlessly on the web.

3.2 Trust Lights up the Triangle

The focus of this paper is to examine the issues and challenges when the trustlights up the triangle relation:

– Trust lights up agent (see(e)−→)

It has been studied from the agent research camp for the last few years . Themajor research issues are agent’s authentication, authorization, delegation,and recommendation, etc.


– Trust lights up semantic web (see(d)−→)

This will be one of the major issues when the semantic web has becomefully loaded. People are concerning about whether ontologies and rules canreally express their or their delegates (agents) intended meaning. The trustand integrity of the information should be preserved when we interchangeor merge our ontologies and rules. Of course, on purpose modification andfabrication of ontologies and rules should be prevented and detected.

– Trust lights up web services (see(f)−→)

The industry proposed trust (security) framework might give us some hintswhether it is worthy to go through semantic web to gain some momentumon achieving the trusted web services [15].

– Trust lights up agent and semantic web (see(e)−→ and

(d)−→)We did not yet solve the ontology and rule integrity problem instead wehave built trust ontology and trust rules for agents via using knowledgefusion technique to proceed trust verification protocols [6][8][18].

– Trust lights up semantic web and web services (see(d)−→ and

(f)−→)We are aware of trust lights up semantic web subsumes trust lights up webservices on policies and rules for agent’s authentication, authorization, anddelegation.

– Trust lights up the triangle (see(d)−→,

(e)−→, and(f)−→)

This problem can be reduced to trust lights up for agent and semantic webif we can bring semantic to web services. However, the implications for trust

path(d)−→,

(b)−→, ,(c)−→ might be different from the trust path for

(e)−→,(b)−→, ,

(a)−→ one.

4 Trust and Related Ontologies

An ontology is a formal, explicit specification of a shared conceptualization [9].The conceptualization we refer to is an abstract model in the world which iden-tifies the trust concepts for achieving agent’s authentication, authorization, anddelegation 1. The trust concepts use and the constraints on their use will be ex-plicitly defined as trust related ontologies and encoded as DAML+OIL ontologylanguage [18]. In addition, the satisfaction of trust criteria will be classified astrust related policies and encoded as RuleML [2]. Furthermore, the trust ontol-ogy and trust rules will be fused together to construct a trust knowledge base.The ontologies we propose for agent trust verification are classified as: trustontology, message ontology, role ontology, and service ontology.

4.1 Trust Ontology

The satisfaction of the certificate theory is a necessary but not a sufficient con-dition for the trust verification on the semantic web. Because it might include1 Here, we did not exclude the possibility to introduce other trust criteria, such as

recommendation.


other aspects in the trust verification of semantic web. Thus, the trust ontologynot only includes the certificate ontology but also possibly includes other trustverification criteria that can not express as certificate theory in the future.

The focus of this study is aiming at the trust verification for agent authen-tication, authorization, and delegation using certificate theory. Although thisapproach is not general enough to cover every possible aspects of trust, it stillprovides the capacity to handle other trust integrity and secrecy issues. Becausepeople still need the certificate ontology to ensure the integrity and secrecy of:(1)ontology schema with its markup information (2)logic rules and the underly-ing framework (3)automatic reasoning processes on the trust verification, etc.

We have built an agent-oriented public key infrastructure (APKI) to showthat the construction of trust path between cooperative agents is possible. Fur-thermore, if human h delegates the authority on human identification endorse-ment to one of the agent certification authorities (ACAs), then we also discoveredthat the human trust relationship can be directly established in the APKI [14].

The trust identification endorsement from human h to its agent a can beshown as: h said a speaks for h on identity impersonation. This statement isencoded as signing operation and indicated in agent identity certificate (AIDcert)as humanBindInfo = Sig(Pua)h to ensure nonrepudiation and liability of agent’sowner [12][17]. The generic agent a identity certificate AIDcert = IDACA 7→a −Cert is shown as [14]:

IDACA 7→a − Cert = (Ida, Pua, Sig(Pua)h, V, Option, SigACA)

where: Ida: a’s distinguished identity; Pua: a’s public key; Sig(Pua)h: agenta’s public key signed by human h’s private key; V : validation period; Option:optional information; SigACA: certificate signature signed by ACA’s private key.

Fig. 2. The terms in the trust ontology defines a taxonomy of digital certificates onagent and human’s authentication, authorization, and delegation. This trust ontologycan be extended with other trust criteria if available in the future.


The AUcert will be used in the agent authority verification based on SPKIand SDSI certificate format [5]. The generic entity (human or agent) autho-rization certificate is AUcert = AUp 7→q − Cert from entity p to entity q. Theauthority delegation can be shown as: p said q speaks for p on some authorityin a service domain.

AUp 7→q − Cert = (Pup, Puq, A,D, V, Sigp)

where: Pup: a public key for the issuer of principal p to grant authorization;Puq: a public key for the subject of principal q to receive authorization; A:expression for authorization, this can be defined as property in a specific domainservice ontology; D: delegation bit with value 0 or 1; V : validation period; Sigp:certificate signature signed by p’s private key.

We define a trust ontology corresponding to generic AIDcert and AUcert,(see Fig. 2). The terms declared with its underlying properties in the trust on-tology will be used in the agent trust verification rules, where hasExpressionin the AUcert is a hook that connects to the service ontology shown in Fig. 5.

4.2 Message Ontology

The message ontology is based on speech-act theory to describe the con-cepts for outer message representations in the agent interaction protocols. Theontology-based performatives construction is quite different from the FIPA stan-dardized performatives one. The incentives of building the message ontologyare to satisfy the open and scalable features of the semantic web. Besides,the ontology-based performatives generation are very easily integrated withontology-based inner content to have an integral and consistent message for-mat (see Fig. 3).

4.3 Role Ontology

The role ontology is the classification of agent, human, and certificates issuingorganization for the services requesting , providing, and endorsing in the agentauthentication, authorization, and delegation processes (see Fig. 4).

4.4 Service Ontologies

The two service ontologies inherited from generic upper services ontology, suchas DAML-S is proposed for a fictitious service scenario: Bank service ontologyand BookStore service ontology (see Fig 5). The service scenario is shown as webportal agent provides brokering service for ordinary agent to purchase books frombookstore agent. The bookstore agent is a service provider and web portal agentis a services requester. The bookstore agent first initiates a request authenticationprotocol to verify the web portal agent’s identity. Then, a request delegationprotocol will be initiated from bookstore agent to web portal agent in order


Fig. 3. The message ontology defines speech-act conversation actions , whereas associ-ated properties are inner content message restrictions w.r.t. these actions in the agentcommunication protocols for trust verification.

Fig. 4. The role ontology classifies human, organization, and agent’s play roles in thetrust control processes.


to obtain an authorization certificate for withdrawing money from the bank.Finally, a request authorization protocol is initiated to allow the web portal agentgranting an authorization certificate for bookstore agent (see Sect. 6).

Fig. 5. The generic service ontologies includes bank services ontology and book servicesontology for the descriptions of fictitious service scenario.

5 Trust Policy and Related Ruleset

In addition to RDF(S), DAML+OIL (or OWL) has become one of the mostimportant ontology languages for the semantic web [18]. The DAML+OIL isdescription logic (DL)-based ontology language that creates the equation forontology = taxonomy + axioms [2]. However, the axioms in this equation pri-marily provides ontology integrity and subsumption checking of class/subclassand property/subproperty.

On the other hand, RuleML is an Horn logic program (HLP)-based rulemarkup language that explicitly declares reaction rules and derivation rules toderive implicit information using inference engine. In fact, this HLP-based rulelanguage complements the axioms expression’s shortcomings found in the ontol-ogy equation [8]. Two deliberately design examples are shown to indicate thisfeature (see Fig. 6).

5.1 Trust Ontology vs. Trust Rule

The integration of ontology and rule is one of the most well-known unsettledproblems for the semantic web research. Based on the preliminary results in[8], we choose knowledge fusion mechanism to build trust rules on top of trustontologies. This approach enables the trust and related rules to have access theprimitives defined in the DAML+OIL’s trust and related ontologies.

In Table 1, rules are classified as two categories: derivation rule and ac-tion rule. The (8)action rule actives the action for querying knowledge base or


Fig. 6. The delegation predicate with three variables axiom that can not express in DL-based ontology language (left); The IsPublicKeyOf predicate is across multiple classesthat also can not express in DL-based ontology language (right). But both of them canbe shown as HLP-based rules.

Table 1. The authentication, authorization, and delegation policies w.r.t. their under-lying rulesets and rules where each rule can be directly encoded as RuleML.


checking certificate integrity. The remaining rule indications are derivation ruleswhich shown from (1) to (9). Each subcategory in the derivation rule has its ownpurpose. For example, the (1)enhance rule is proposed to enhance the propertychaining expression, which can not shown in the tree-based ontology structure.The trust policies are classified as authentication, authorization, and delegationthree categories. Each trust policy category is defined as a ruleset selected fromthe policy base to indicate the trust and service constraints. For example, theruleset for the delegation policy is {1,3,5,6,7,8}.

6 Interaction Protocols for Trust Verification

The trust verification process is composed of three interaction protocols, i.e.,authentication, authorization, and delegation. Each of these interactions proto-cols uses the ontologies and policies we defined to indicate the message passingsemantics (see Sect. 4 and Sect. 5).

Fig. 7. The request delegation protocols for service provider agent to request a dele-gation permit (or AUcert) from service requester agent.

One of the trust verification protocols, e.g. request delegation protocols willbe explicitly shown in the following analysis (see Fig. 7). When ontologies orrules are imported dynamically for each agent from the Web, one of the possiblemessages (1)Request Delegation fan out w.r.t. the request delegation protocolsis shown in Fig. 8.

The request delegation protocols demonstrate how the ontologies and rulesare used in this interaction scenario. First, the terms for outer speech-act actionswere defined in the message ontology in Sect. 4.2. Second, the inner contentwithin the speech-act might be simple certificate instance or requirement ruleinstance (see Fig. 2 and Table 1). Third, there should exist a mechanism toprocess the message for each agent to decide what are the feasible steps toexchange interaction messages.


Fig. 8. The messages fan out for the (1)Request Delegation when ontologies and rulesare imported dynamically from the web.

6.1 Finite State Machines

The finite state machines (FSMs) are operation mechanisms of semantic messageexchange. We show two FSMs for service requester agent and service provideragent when they are executing the request delegation protocols (see Fig. 9). Thisis not a brand new idea to use FSM as a structure for conversations of actionbetween human and agent or between agent and agent [3][24]. But it is still achallenge for semantic web designer to discover how to construct the FSM(s)in each agent to have a semantic level conversation. In this paper, the FSM(s)are embedded in each agent’s action controller (AC) module to indicate how themessages should exchange between service provider agent and service requesteragent (see Sect. 7).

7 A Framework for Semantic Information Processing

We have been building a framework to realize what will be the possible semanticinformation processing flow (IPF) for trust verification (see Fig. 10). This IPF isshown as a single site for either service requester agent or service provider agentto process the semantic information on the web. In this IPF, we are going toshow how each module processes the incoming and outgoing messages on trustverification, i.e., authentication, authorization, and delegation.

1. [Message Processor (MP)]: the MP receives and examines each incoming

message (see(1)−→) to decide whether it is a well-defined agent communication

language (ACL) message. When an outgoing information receives from AC

(see(8)←−) it will be formatted as a well-defined ACL message and sent to

another MP (see(9)←−).

2. [Action Controller (AC)]: the AC receives a well-defined ACL message

(see(2)−→) and classifies this message using FSM based on each ACL’s outer

speech-act and inner content described by rules and ontologies to select oneof the following branching points:– [Certificate Instances Processing]: if the ACL message is a digital

certificate instance, then certificate ontology schema will be brought in


Fig. 9. The FSM for service provider agent and service requester agent when they areexecuting the request delegation protocols.

Fig. 10. The information processing flow (IPF) for each module to achieve a specifictrust verification on the semantic web


from the Web (see(3a)L99) and input to Jena for data consistency checking

(see(3b)−→). If checking is successful, this instance will be transformed by

XSLT into RuleML and fed into Mandarax for further processing (see(3f)−→) .

– [Trust Policy Verification]: if the agent in this site is a:• Verification subject: it goes to the PS to verify AIDcert and AUcert

sent from service requester agent via associated trust policy (see(3d)−→).

• Verification object: it sends the trust verification ruleset to infer-

ence engine to analyze the requirements (see(3e)−→). Then, the digital

certificates for verification will be sent to the remote site’s verifierthrough its AC and MP (see

(7)←−).3. [Policy Selector (PS)]: the PS chooses one of the trust policies with its

associated ruleset from the policy base 2 in order to verify each trust criteriaso this ruleset will be deliver to the PP for further classification and inference(see

(4)−→).4. [Policy Processor (PP)]: the PP processes each rule in the ruleset depends

on each rule’s category.– [Derivation Rule]: if it is a derivation rule, including a variety of sub-

category, such as requirement rule, constraint rule, etc, then it will be

directly fed into Mandarax inference engine (see(5a)−→).

– [Reaction Rule]: if it is a reaction rule and the indicated action mightbe:• Query knowledge base on the Web: the retrieved knowledge will be

fed into Jena for further processing (see(5d)−→).

• Check certificate integrity: inference engine can not process the va-lidity of each digital certificate so we need to call CP to validate it

(see(5b)−→).

5. [Jena]: it is the datastoring and manipulation module for storing ontologyschema with its instances and checking their consistency and integrity.

6. [XSLT]: it is a message transformation module to convert Jena outputs into

RuleML expression (see(3f)−→).

7. [Mandarax]: an inference engine for reasoning the knowledge base to answerthe query, derive implicit information, and check the rule satisfaction.

8. [Crypto-Processor (CP)]: a crypto-processor verifies and validates anydigital certificate that was encoded by well-known security mechanisms, suchas RSA, DES, etc. The PP fetches a certificate instance from the cyberspace

(see(5c)L99), then it is sent to the CP for further verification and validation. Fi-

nally, it is fed into the inference engine, i.e., Mandarax for further processing

(see(6)←−).

2 The policy and rule’s classification see Table 1


8 Conclusion and Further Studies

Building the trusted semantic web will be one of the most important issues whenthe web is fully loaded with semantic annotation. Unfortunately, the researchersin the semantic web community did not pay much attention on this issue. There-fore, the results for trusted semantic web are very scarce. We first construct thesemantic web pyramid to demonstrate the relations between semantic web andother emerging core technologies. Then, we examine the issues and challengeswhen trust lights up the triangle in the pyramid base. The possible incentiveswe found for merging these technologies are based on the trust traversing pathconstructed from trust, semantic web, agent, and web services.

We design and implement trust ontologies and trust rules for agents to realizetheir trust verification and validation protocols. We deliberately use one of thetrust control protocols, i.e., request delegation to demonstrate our approach isfeasible. Finally, a complete framework for information processing flow of eachagent was shown to demonstrate the proposed ontologies and rules on the agent’strust verification and validation.

The mechanism we use for the semantic message exchange is very similar tothe speech-act-based agent communication language. But we design an extensiblemessage ontology to express all kinds of speech-act performatives instead ofusing standardized FIPA conversation acts. The expressive power for exchangeinformation semantics between these two approaches needs further studies.

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Trust on the Semantic Web Pyramid: Some Issues and Challenges · 2017-09-27 · Trust on the Semantic Web Pyramid: Some Issues and Challenges 3 2 Some Issues and Challenges Trust