research pages

Computer-mediated communication of structured knowledge

These pages contain a high level description of work that has been performed and obtained results. Some of this work may be discontinued. The detailed presentation of these results is in the cited papers.

Goal and motivation

Our expectation is that it is useful to express formalised knowledge on a computer, not especially for the need of the computer, but for those of communication. We consider that, in future information systems, formalised knowledge will be massively exchanged. However, there is no reason why this knowledge should be expressed in the same format or by reference to the same vocabulary (or ontology). In order to interoperate, these representations will have to be matched and transformed.

Moreover, in this communication process computers can add value to their memory and medium role by formatting, filtering, classifying, consistency checking or generalising knowledge. These manipulations can be thought of as transformations and also have to be carried out so that they facilitate communication. Our goal is to provide tools so that these manipulations satisfy expected properties, mostly with respect to semantics.

We have developed the Alignment API and Alignment server. The current alignment API is being extended in order to offer its services to these applications (through agent communication protocols and web service invocation) and generating or processing the various transformations required by the applications.

We currently investigate three practical applications of such an infrastructure: (1) Matching context and needs in ambient computing requires such an infrastructure and would benefit sharing alignments on a large scale. (2) Annotated resource sharing in peer-to-peer architecture requires to query peers with heterogeneous ontologies. The alignment service is used there to infer, edit and store the alignments and provide appropriate mediators for processing the queries. (3) The ontology infrastructure of the semantic web is made of networked ontologies for which alignments can be the relations between ontologies.

Alignments, transformations and properties

Such an alignment infrastructure is the occasion to adapt Exmo's objective of providing an environment guaranteeing properties of the transformations. Instead of directly considering the properties of transformations, we can consider those of alignments and generate transformation (or any other kind of mediators) from these alignments.

For that purpose, we are currently studying how alignment properties can be obtained by construction from the type of algorithm used for computing the alignment. We are also developing alignment composition operators that will be inserted in the alignment service. We will have to study how the transformation generators preserve these properties (and what distortion is introduced by these generators).

In the meantime we continue to investigate some particular interesting problems in which we can still progress:

the design of semantically grounded alternative to precision and recall for comparing ontology alignments;
the design and development of efficient alignment algorithms;
the semantic (and rhetoric) adaptation of multimedia documents (with WAM).

On a longer term, we want to explore Ť semiotic ť properties, i.e., properties which concern the interpretation of the communicated representation by a numan user. This goal should require an analysis of the extra-semantic rules that govern the choice of subsets of models.

Anticipated applications are in transformation system engineering (in which the information system is seen as a transformation flow) and the semantic web infrastructure.

Achievements

We summarise four recent outcome of our work that we think particularly important. There are many other results, accessible through the table of content.

Semantics of networks of ontologies

We have introduced a semantics for networks ontologies related by alignments. This semantics considers alignments as constraining the meaning of ontologies so that each ontology is made more precise and entails more knowledge. This semantics is used for defining other operators on the networks such as consequence computation, alignment composition or network revision.

Alignment API and server

We develop and maintain an API and implementation of alignment structure which is used by more than 30 teams around the world. It allows for piping matching algorithms, manipulating alignments (trimming and hardening), generating processing output and comparing alignments. This API is also embedded in an Alignment server providing support for storing, finding, and sharing alignments over various communication means (web service, http, agent languages). The Alignment API is used in yearly matching evaluations.

Ontology distance and matchers

We design metrics to be used for comparing ontologies and entities which are available in the OntoSim library. We develop ontology matchers based on these measures (Aroma, OLA). Ontology metrics can be used in other applications such as finding peer affinity in semantic social networks.

Querying RDF graphs with paths

We have extended the standard SPARQL query language for dealing with paths of unknown length and provided complete algorithms for answering queries. This extension, PSPARQL, has been shown sufficient for answering SPARQL queries modulo RDF schemas. This language has been further extended for supporting constraints. This is sufficient to answer CPSPARQL queries modulo RDF Schema.

Content

After setting the applicative context:

Our programme is presented around six axes:

Content and knowledge representation is the kind of languages we manipulate,
Ontology alignments for representing correspondences between models:
Ontology matching aims at finding alignments that can be used as transformations:
Transformations and properties is the general framework that we promote and apply to various contexts:
Semantic interoperability focuses on semantic properties of transformations:
Distributed and dynamic aspects of matching and exploiting alignments:
Infrastructure presents the steps toward a safe tranformation infrastructure:
Computational semiology, on a longer term, considers the opportunity to transmit more context during the communication.