INFORMATION SPACES AS A BASIS FOR PERSONALISING

THE SEMANTIC WEB

Ian Oliver

Nokia Research Center, It¨amerenkatu 11-13, Helsinki, Finland

Keywords:

Space based computing, Semantic Web, Mobile devices.

Abstract:

The future of the Semantic Web lies not in the ubiquity, addressability and global sharing of information but

rather in localised, information spaces and their interactions. These information spaces will be made at a much

more personal level and not necessarily adhere to globally agreed semantics and structures but rely more upon

ad hoc and evolving semantic structures.

1 INTRODUCTION

In this paper we position our vision of the continu-

ation of the development or evolution of the Seman-

tic Web (Berners-Lee et al., 2001). This is best visu-

alised as the Giant Global Graph concept popularised

by Tim Berners-Lee

Most information however is not ubiquitous but

personalised, hidden, private and interpreted locally -

this information tends to be the personal, highly dy-

namic information that one stores about oneself: con-

tact lists, friends, media ﬁles, ‘my’ current context,

‘my’ family, ‘my’ home etc and the interweaving and

linking between these entities through ad hoc personal

structures.

We elaborate on the ideas of ubiquitous informa-

tion, the role of reasoning and knowledge, the lo-

cation of the information with relation to its ubiq-

uity through the concept of projections from the Gi-

ant Global Graph called spaces. We then describe an

implementation of an environment supporting these

ideas in a mobile and personal context as well as many

of the issues that this directly brings up with regards

to what are semantics and how information is going

to be dealt with in this context.

In the following sections we outline our position

and areas of research relating to notions of personali-

sation, Semantic Web, informationand its meaing and

semantics as well as our implementation.

http : //en.wikipedia.org/wiki/Giant Global Graph

2 PERSONALISATION

AND SPACES

The Semantic Web is succeeding in relatively small-

scale, speciﬁc situations which are restricted to a

given domain. If we expand the notion of a domain in

a more orthogonal sense to encompass personal level

then this suggests that we have a notion of a ‘Personal

Semantic Web’ in which one can organise their own

information according to these principles. The advan-

tages of a Semantic Web based approach is that cer-

tain structures, schemata and semantics can be ﬁxed

enabling some - and this is an important point, we

should not (and can not?) try to attempt everything -

meaningful communication, reasoning and interoper-

ability to take place.

Mobile devices with various methods of connec-

tivity which now constitute for many as being the pri-

mary gateway to the internet and also being a ma-

jor storage point for much personal information (Ide-

hen and Erling, 2008; Lassila and Adler, 2003). This

is in addition to the normal range of personal com-

puters and furthermore sensor devices plus ‘internet’

based providers. Combining these devices together

and lately the applications and the information stored

by those applications is a major challenge of interop-

erability (Tolk and Muguira, 2003; Turnitsa, 2005).

This is achieved through numerous, individualand

personal spaces in which persons, groups of persons

etc can place, share, interact and manipulate webs of

information (Krummenacher, 2008; Khushraj et al.,

2004) with their own locally agreed semantics with-

out necessarily conforming to an unobtainable, global

179

Oliver I. (2009).

INFORMATION SPACES AS A BASIS FOR PERSONALISING THE SEMANTIC WEB.

In Proceedings of the 11th International Conference on Enterprise Information Systems - Software Agents and Internet Computing, pages 179-184

DOI: 10.5220/0002155901790184

 SciTePress

whole. These spaces are projections of the ‘Giant

Global Graph’ in which one can apply semantics and

reasoning at a local level. A detailed survey of such

space-based systems is given in (Nixon et al., 2008).

This approach we feel addresses at least two of the

counter-arguments against the Semantic Web vision:

feasibility and privacy by directly addressing notions

of locality or ubiquity and ownership. Feasibility be-

cause we are changing the problem to address much

smaller-scale structures through setting clear bound-

aries in terms of computation.

In order to apply reasoning and other manipula-

tions (such as sharing) of that information we are re-

quired to construct processes which have access to

that information - typically these are known as agents

in the ‘traditional’ sense of the word although we

tend towards the classiﬁcation given in (Haag et al.,

2003). Agents are either personal in that they per-

form tasks either directly decided upon by the user or

autonomously for or on behalf of the user, monitor

particular situations or reason/data-mine the existing

information.

Personalisation is achieved through explicitly de-

marking a space in which information is stored and

agents have access. Within each space information is

organised according to the owner (or owners) of that

space. For an agent to obtain entry to that space then

it is made on the terms of that space. Similarly for two

spaces to interact directly similar contracts must also

hold. Interactions with spaces is described in section

The kinds of information that are stored in a per-

sonal space vary but initially contacts lists, media ﬁles

(links to media), personal information management

data (calendars etc), email and other personal com-

munication etc. This easily expands to information

feeds such as those provided through RSS and even

WWW interfaces, family or community information,

social networking and so on. These kinds of informa-

tion can be then further augumented by tagging, inter-

nal links and more sophisticated equivalence relation-

ships such as might be seen between social network-

ing contacts, contacts lists, calendars etc. In addition

more static and thus more externalisable information

can be stored or referenced in the same manner - such

information might be census records, telephone direc-

tories or even cultural information (Hyv¨onen et al.,

2008).

Of course there are issues regarding the interpre-

tation of information and how the meaning or seman-

tics is preserved across spaces and agents; this also

includes deciding whether two independent structures

actually represent the same piece of information and

can be merged or coalesced. Furthermore issues re-

garding trust and security need to be addressed - we

do not speciﬁcally discuss this problem in this paper.

3 INTERACTION AND SHARING

WITH SPACES

Consider the follow scenarios. In ﬁgure 1 Alice inter-

acts with her personal space - through ‘agents’

run-

ning on a multitude of devices. This space contains a

corpus of information A which through local reason-

ing and deductive closure algorithms - a feature of our

spaces - provides her with the corpus R(A). Informa-

tion is represented using Semantic Web standards, ie:

RDF, RDFS, OWL, FOAF etc, rule sets in RuleML

etc.

Alice

R(A)

Alice’s "agents"

executing on

interacting with

uses

...and others...

Figure 1: Alice’s Agents, Devices, Spaces and Information.

In this case the boundary of R(A) is the limit of

Alice’s personal space. If Alice has two spaces or

corpii of information she might bind these together to

produce a much larger space. These individual corpii

of information may be overlapping in terms of their

content. Alice can interact simultaneously with many

discrete spaces. These situations are visualised in ﬁg-

ure 2; for simplicity we only show the spaces and clo-

sures. Here the total information available for a given

space is the union of the deductive closure over all the

individual corpii.

Alice

R(C)

R(D)

R(A u B)

Figure 2: Alice and Alice’s Spaces.

Agent is rather a loaded word, but alternatives aren’t numer-

ous: executives, nodes, UIs, programs, etc

ICEIS 2009 - International Conference on Enterprise Information Systems

180

Alice can decide to break and reconﬁgure her cur-

rent spaces into many smaller spaces. This may be

made in any manner including removing all informa-

tion and creating multiple individual smaller or even

empty spaces to making a complete copies of the cur-

rent space.

We now introduce Bob, who has a space of his

own that is constructed from a single corpus of infor-

mation. At this point we can say nothing about the

relationship between the contents of the corpii A and

B with C; they may potentially all contain the same

information.

Interaction between Alice and Bob can be made in

three different forms. The ﬁrst is simple in that Bob

only needs to give Alice’s ‘agents’ access to his space

as visualised in ﬁgure 3. Bob of course has his own

ideas about privacy and grants Alice access to only

a portion of his space. Alice has direct access to a

subset of Bob’s space - if she has write access then

potentially this could have an effect on the space as a

whole.

Privacy is asymmetric - it is on the sharer’s terms

only thus precluding the need for a globally agreed

privacy mechanism. If Alice just so happens to

be able to satisfy the criteria for accessing Bob’s

space then Alice is granted access at whatever level

Bob’s privacy mechanisms allow. Alice’s own privacy

mechanisms do not affect Bob’s mechanisms and vice

versa.

Alice

Bob

Figure 3: Alice Interaction with Bob’s Space.

The second form of interaction is a variation of

this: Bob to partitions off a given subspace to which

Alice has access, then any changes can be kept lo-

cal and the merge back controlled by Bob explicitly.

In both of these cases access policy including trust

mechanisms are local to Bob - there is no need for

Alice to know about what mechanisms are in place.

The ﬁst two forms we expect to be the most com-

mon methods of interaction, the third offers a set of

different possibilities based around the merging of

the spaces. This is more complicated as Alice and

Bob must both agree to the merge (ﬁg. 4) both in

terms of personally agreeing through their trust mech-

anisms (might just be personal trust) but also through

the shared semantics of the information.

Alice

Bob

Figure 4: Alice’s and Bob’s (Merged) Space.

This then constitutes how spaces are related but

has not yet addressed certain speciﬁc ideas about in-

formation and the semantics of that information and

these are discussed in section 4.

4 INFORMATION

AND SEMANTIC ISSUES

We can classify the issues with this approach as:

• Non-monotonicity of Deductive Closure and

Rules

• Graph Provenance

• Semantics of the Information

• Uncertainty, Incompleteness, Inconsistency and

Undeﬁnedness of Information

Given a single space s, the information contained

in that space is i(s), the rules ρ(s) and the deductive

closure calculated as R(i(s),ρ(s)). A merge of two

spaces s

and s

results in a single space s

where s

is calculated as R(i(s

) ∪ i(s

),δ(ρ(s

),ρ(s

)). The

function δ determines the set of rules to apply and

is constructed from a mechanism which prioritises

the rules somehow - the exact mechanism would of

course vary but we envisage would be similar to those

found in certain kinds of non-monotonic calculi, eg.

(Mueller, 2006).

Graph provenance and the related semantics

problem are the major problems in any Semantic

Web/interoperability system as there, beyond pre-

agreed addressing and strict, standardised ontologies,

no deﬁnitive method for relating structures represent-

ing the same information together exists.

While information about typing etc is carried

within the space, deeper semantics is not. Identiﬁ-

cation of larger meaningful structures such as RDF

molecules (Ding et al., 2005) provide at this time

the strongest basis for provenance analysis and ad-

dressing deeper semantically meaningful structures.

In the most part we must rely upon local convention

between any two merging or interacting spaces with

the hope that this leads to a more global convention

INFORMATION SPACES AS A BASIS FOR PERSONALISING THE SEMANTIC WEB

181

(Afraz Jaffri, 2008; Shaﬁq et al., 2008).

The meaning of the information to Alice might be

very different to the meaning ascribed by Bob (see

also provenance above). The meaning or semantics

of the information can only be made by the reader

(Burcea et al., 2003); the writer of the information

only gives hints through typing and tagging and other

relationships to what the intended meaning might be.

This hints to the question how do we guaran-

tee that two agents understand the information in the

same way, our response to this is that we don’t care;

at least to the point where there is no internal mech-

anism for this (Kim and Anseo-Dong, 2002). As it

is, the meaning of some ontological structure evolves

over time and there appears to be no reasonable mech-

anism for communicatingdeeper level semantic struc-

tures. For true, reliable communication how many

semantic levels are required is unknown. However,

since that if access to a space is granted or a merge

made then this is implicit agreement between the par-

ties and particularly the readers over the intended se-

mantics of the information.

In (G¨ardenfors, 2000) is provided a detailed dis-

cussion of the style of intentional semantics (Kim and

Anseo-Dong, 2002) we propose here that it is not just-

ing typing but the whole construct of properties of an

object - and then the scope over which we deﬁne an

object - that must be taken into consideration when

deciding how an agent interprets a given structure.

This also applies when dealing with the aforemen-

tioned graph provenance issue.

To complicate matters further, the notion of se-

mantics embodied within these kinds of information

structures is little more than meta-data whereas one

really needs to describe a further relationship into

the ‘real-world’ (Smith, 1996). Solutions based upon

lexical and semantic analysis through resources such

as Wordnet(Fellbaum, 1998) appear to be the most

promising here with regard to issues surrounding se-

mantics similarity (Jan Wielemaker and Wielinga,

2003; Ruotsalo and Hyv¨onen, 2007). However,

despite this we can (an no current computer sys-

tem) never be sure that any two agents actually act

upon their respective interpretations information in

the same way.

Currently we are seeing two mechanisms for se-

mantic agreement: the ﬁrst is through standardisa-

tion of ontologies (W3C) and the second is through

folksonomic evolution of initially personal and infor-

mal structures into ad hoc ontologies which become

more concretised as social agreements form. Even in

the strict, formal ontology development scenario evo-

lution of the ontology takes place as usage changes

and develops (Ruotsalo et al., 2008), however this is

much slower than the folksonomic cases. A fairly

common mechanism for agreeing on semantics is the

upper ontology approach, but again this suffers from

the problem of many upper ontologies (for example:

(Hyv¨onen, 2008))- an interesting discussion of this is

made in (Wheeler, 2004)

Finally the problem of uncertain, incomplete,

inconsistent and undeﬁned information requires a

much more formal approach within the various agent

and reasoning structures (Lassila, 2008). Within

the Semantic Web we must further explore notions

of undeﬁnedness, modality, probability and non-

monotonicity. This is left for future work though

mechanisms are already present within our imple-

mentation for attaching and modality properties.

5 IMPLEMENTATION

Supporting these ideas we already have the comput-

ing and networking infrastructure. Our particular

solution builds a space-based computing framework

(Oliver and Honkola, 2008; Oliver et al., 2008) based

upon the Piglet/Wilbur (Lassila, 2007) RDF++ en-

gine (Lassila, 2002). The notion of space being con-

structed out of a number of individual, linked (totally

routable) brokers. Interaction with the space is via a

agents which may reside on any suitable device with

suitable connectivityand computing capabilities; sim-

ilarly the brokers. Figure 5 shows one possible con-

ﬁguration.

Agents may connect to one or more spaces at a

time and to which spaces may vary over the lifetime

of an agent. Mobility in this sense is provided in

a ‘pi-calculus’ manner (Milner, 1999) in that links

may move rather than the physical running process.

Agents can save state and become ‘mobile’ when an-

other agent restores that state. We have not addressed

code mobility in the current implementation and this

remains low priority at this moment.

Agents themselves are anonymous and indepen-

dent of each other - there is no explicit control ﬂow

other than that provided through preconditions to

agent actions. A coordination model based around ex-

pressing coordination structures as ﬁrst-order entities

is being investigated, however we are more focussed

on collecting and reasoning over context. Control

ﬂow can be made outside of the space through agents

explicitly sharing details of their external interfaces

through the space - this has been successfully used in

coordinating media streaming and storage devices via

UPnP

and NoTA

for example.

Universal Plug and Play http://upnp.org

Network onTerminal Architecture http:// www.notaworld.org/

ICEIS 2009 - International Conference on Enterprise Information Systems

182

Space "A" Space "B"

Individual Brokers +

Information Stores

connections to spaces

Agents +

Control Flow

Mechanisms

explicitPotential

Figure 5: An Example Implementation Conﬁguration.

The brokers each contain a corpus of information

and when linked together to form a space distribute

the information in an asymmetric manner - some in-

formation is not replicated because of computational

resources, connectivity, storage reasons and even le-

gal issues such as copyright. This distribution layer

is also responsible for the efﬁcient distributed com-

putation of queries and marshalling the resultant re-

sponses such that calculating the deductive closure

can be made. In this manner we can distribute com-

plex computation away from the original agent over

the whole space.

This distribution can be used in a number of ways

and one of the most interesting for us is giving the

user temporary access to a larger body of information

which can be used to selectively enhance their current

corpus of information, for example, a body of linking

information based around the

owl:sameAs

construct

(Passant, 2008). Other examples include personal ac-

cess to more dynamic corpii such as news, weather or

similar services.

We make no attempt to enforce consistency of the

information rather letting the writer of the informa-

tion have freedom to express what they want and leave

the interpretation to the reader. The semantics of the

information is merely intensional in that the writer

provides ‘clues’ through typing, tagging and other

means. Repair of information according to schemata

or other criterion can be enforced within the space and

for some kinds of information would even be desir-

able. If consistency of particular structures is required

then this can be achieved through agent implementa-

tion and speciﬁc belief revision models.

6 CONCLUSIONS

In summary we believe that the Semantic Web will

move from being a global information corpus to mul-

tiple, individual, linked and personal corpii. Seman-

tics, reasoning and processing about the information

will be localised and personalised within these corpii.

As corpii are shared, linked, merged and split, certain

schemata will coalesce and evolute into ﬁxed or stan-

dard structures in an evolutionary form.

There are still questions regarding what precisely

is semantics and how this is preserved across informa-

tion structures but techniques do exist for reasoning

about this and the related graph/information prove-

nance problems and these are currently being imple-

mented and trialled within the framework we have de-

scribed.

REFERENCES

Afraz Jaffri, Hugh Glaser, I. M. (2008). Uri disambiguation

in the context of linked data. In Linked Data on the

Web (LDOW 2008), Beijing, China.

Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The

semantic web. Scientiﬁc American, 284(5):34–43.

Burcea, I., Petrovic, M., and Jacobsen, H.-A. (2003). I know

what you mean: semantic issues in internet-scale pub-

lish/subscribe systems. In Cruz, I. F., Kashyap, V.,

Decker, S., and Eckstein, R., editors, SWDB, pages

51–62.

Ding, L., Finin, T., Peng, Y., Silva, P. P. D., and L, D.

(2005). Tracking rdf graph provenance using rdf

molecules. Technical report, 2005, Proceedings of the

Fourth International Semantic Web Conference.

Fellbaum, C. (1998). Wordnet: An electronic lexical

database.

INFORMATION SPACES AS A BASIS FOR PERSONALISING THE SEMANTIC WEB

183

G¨ardenfors, P. (2000). Conceptual Spaces: The geometry

of thought. The MIT Press. 0-262-57219-2.

Haag, S., Cummings, M., and McCubbrey, D. J. (2003).

Management Information Systems for the Information

Age. McGraw-Hill Companies. 4th edition. 978-

0072819472.

Hyv¨onen, E. (2008). Finnonto-malli kansallisen semant-

tisen webin sis¨alt¨oinfrastruktuurin perustaksi - visio ja

sen toteus (ﬁnnonto model as the basis for a national

semantic web infrastructure - vision and itsimplemen-

tation). In Paper presented at the publication event of

the Finnish Ontology Library Service ONKI.

Hyv¨onen, E., M¨akel¨a, E., Kauppinen, T., Alm, O., Kurki,

J., Ruotsalo, T., Sepp¨al¨a, K., Takala, J., Puputti, K.,

Kuittinen, H., Viljanen, K., Tuominen, J., Palonen, T.,

Frosterus, M., Sinkkil¨a, R., Paakkarinen, P., Laitio, J.,

and Nyberg, K. (2008). Culturesampo – a collective

memory of ﬁnnish cultural heritage on the semantic

web 2.0.

Idehen, K. and Erling, O. (2008). Linked data spaces and

data portability. In Linked Data on the Web (LDOW

2008), Beijing, China.

Jan Wielemaker, G. S. and Wielinga, B. (2003). Prolog-

based infrastructure for rdf: performance and scala-

bility. In In: D. Fensel, K. Sycara and J. Mylopou-

los (eds.) The Semantic Web - Proceedings ISWC’03,

Sanibel Island, Florida. Lecture Notes in Computer

Science, volume 2870, Springer-Verlag, pages 644–

658.

Khushraj, D., Lassila, O., and Finin, T. (22-26 Aug. 2004).

stuples: semantic tuple spaces. Mobile and Ubiqui-

tous Systems: Networking and Services, 2004. MO-

BIQUITOUS 2004. The First Annual International

Conference on, pages 268–277.

Kim, H.-G. and Anseo-Dong (2002). Pragmatics of the

semantic web. In Semantic Web Workshop. Hawaii,

USA.

Krummenacher, R. (2008). The smartest space of all: A

global space of (machine-understandable) knowledge.

In 1st Russian Conference on SmartSpaces, ruSmart

2008, St.Petersburg, Russia.

Lassila, O. (2002). Taking the rdf model theory out for a

spin. In ISWC ’02: Proceedings of the First Inter-

national Semantic Web Conference on The Semantic

Web, pages 307–317, London, UK. Springer-Verlag.

Lassila, O. (2007). Programming Semantic Web Applica-

tions: A Synthesis of Knowledge Representation and

Semi-Structured Data. PhD thesis, Helsinki Univer-

sity of Technology.

Lassila, O. (2008). Some personal thoughts on semantic

web and non-symbolic ai. In 4th International Work-

shop on Uncertainty Reasoning for the Semantic Web.

Lassila, O. and Adler, M. (2003). Semantic gadgets: Ubiq-

uitous computing meets the semantic web. In Fensel,

D., Hendler, J. A., Lieberman, H., and Wahlster, W.,

editors, Spinning the Semantic Web: Bringing the

World Wide Web to Its Full Potential, pages 363–376.

MIT Press. 0-26206-232-1.

Milner, R. (1999). Communicating and Mobile Systems:

the Pi-Calculus. Cambridge University Press. 0-521-

65869-1.

Mueller, E. T. (2006). Commonsense Reasoning. Morgan

Kaufmann. 0-12-369388-8.

Nixon, L. J. B., Simperl, E., Krummenacher, R., and

Martin-Recuerda, F. (2008). Tuplespace-based com-

puting for the semantic web: a survey of the state-

of-the-art. The Knowledge Engineering Review,

23(2):181–212.

Oliver, I. and Honkola, J. (2008). Personal semantic web

through a space based computing environment. In

Middleware for Semantic Web 08 at ICSC’08, Santa

Clara, CA, USA.

Oliver, I., Honkola, J., and Ziegler, J. (2008). Dynamic, lo-

calised space based semantic webs. In WWW/Internet

Conference, Freiburg, Germany.

Passant, A. (2008). :me owl:sameas ﬂickr:33669349@n00.

In Linked Data on the Web (LDOW 2008), Beijing,

China.

Ruotsalo, T. and Hyv¨onen, E. (2007). A method for de-

termining ontology-based semantic relevance. In Pro-

ceedings of the International Conference on Database

and Expert Systems Applications DEXA 2007, Re-

gensburg, Germany. Springer.

Ruotsalo, T., Sepp¨al¨a, K., Viljanen, K., M¨akel¨a, E., Kurki,

J., Alm, O., Kauppinen, T., Tuominen, J., Frosterus,

M., Sinkkil¨a, R., and Hyv¨onen, E. (2008). Ontology-

based approach for interoperability of digital collec-

tions. Signum, (5).

Shaﬁq, O., Scharffe, F., Wutke, D., and del Valle, G. T.

(2008). Resolving data heterogeneity issues in open

distributed communication middleware. In Proceed-

ings of 3rd International Conference on Internet and

Web Applications and Services (ICIW 2008), Athens,

Greece.

Smith, B. C. (1996). On the Origin of Objects. The MIT

Press. 0-262-69209-0.

Tolk, A. and Muguira, J. A. (2003). The levelsof conceptual

interoperability model (lcim). In Proceedings IEEE

Fall Simulation Interoperability Workshop, IEEE CS

Press.

Turnitsa, C. D. (2005). Extending the levels of conceptual

interoperability model. In Proceedings IEEE Summer

Computer Simulation Conference, IEEE CS Press.

Wheeler, Q. D. (2004). Taxonomic triage and the poverty

of phylogeny.

ICEIS 2009 - International Conference on Enterprise Information Systems

184