The Semantic Web vision is largely based on a classical, "symbolic" view of artificial intelligence. One might argue that much of the criticism leveled against the Semantic Web is similar to what symbolic AI has faced. Since the "thawing of the AI Winter", much of the success of AI could be credited to "non-symbolic" methods (data mining, neural networks, machine learning, etc.). Realistic Semantic Web applications have to be able to deal with imprecise and/or "noisy" data, uncertain inferences, and like. In this talk I share some personal observations on the practicality of the Semantic Web vision, specifically with respect to the success of non-symbolic AI.

The Semantic Web relies on carefully structured, well defined data to allow machines to communicate and understand one another. In many domains (e.g. geospatial) the data being described contains some uncertainty, often due to bias, observation error or incomplete knowledge. Meaningful processing of this data requires these uncertainties to be carefully analysed and integrated into the process chain. Currently, within the Semantic Web there is no standard mechanism for interoperable description and exchange of uncertain information, which renders the automated processing of such information implausible, particularly where error must be considered and captured as it propagates through a processing sequence. In particular we adopt a Bayesian perspective and focus on the case where the inputs / outputs are naturally treated as random variables. This paper discusses a solution to the problem in the form of the Uncertainty Markup Language (UncertML). UncertML is a conceptual model, realised as an XML schema, that allows uncertainty to be quantified in a variety of ways: i.e. realisations, statistics and probability distributions. The INTAMAP (INTeroperability and Automated MAPping) pro ject provides a use case for UncertML. This paper demonstrates how observation errors can be quantified using UncertML and wrapped within an Observations & Measurements (O&M) Observation. An interpolation Web Processing Service (WPS) uses the uncertainty information within these observations to influence and improve its prediction outcome. The output uncertainties from this WPS may also be encoded in a variety of UncertML types, e.g. a series of marginal Gaussian distributions, a set of statistics, such as the first three marginal moments, or a set of realisations from a Monte Carlo treatment. Quantifying and propagating uncertainty in this way allows such interpolation results to be consumed by other services. This could form part of a risk management chain or a decision support system, and ultimately paves the way for complex data processing chains in the Semantic Web.

The Rule Interchange Format (RIF) is an emerging W3C format that allows rules to be exchanged between rule systems. Uncertainty is an intrinsic feature of real world knowledge, hence it is important to take it into account when building logic rule formalisms. However, the set of truth values in the Basic Logic Dialect (RIF-BLD) currently consists of only two values (t and f). In this paper, we first present two techniques of encoding uncertain knowledge and its fuzzy semantics in RIF-BLD presentation syntax. We then propose an extension leading to an Uncertainty Rule Dialect (RIF-URD) to support a direct representation of uncertain knowledge. In addition, rules in Logic Programs (LP) are often used in combination with the other widely-used knowledge representation formalism of the Semantic Web, namely Description Logics (DL), in order to provide greater expressive power. To prepare DL as well as LP extensions, we present a fuzzy extension to Description Logic Programs (DLP), called Fuzzy DLP, and discuss its mapping to RIF. Such a formalism not only combines DL with LP, as in DLP, but also supports uncertain knowledge representation.

The paper argues for an alternative, empirical (instead of analytical) approach to a Semantic Web-ready KR&R, motivated by the so far largely untackled need for a feasible emergent content processing.

We outline DL-Media, an ontology mediated multimedia information retrieval system, which combines logic-based retrieval with multimedia feature-based similarity retrieval. An ontology layer is used to define (in terms of a fuzzy DLR-Lite like description logic) the relevant abstract concepts and relations of the application domain, while a content-based multimedia retrieval system is used for feature-based retrieval. We will illustrate its logical model, its architecture, its representation and query language and the preliminary experiments we conducted.

This paper proposes a probabilistic description logic that combines (i) constructs of the well-known ALC logic, (ii) probabilistic assertions, and (iii) limited use of nominals. We start with our recently proposed logic crALC, where any ontology can be translated into a relational Bayesian network with partially specified probabilities. We then add nominals to restrictions, while keeping crALC’s interpretation-based semantics. We discuss the clash between a domain-based semantics for nominals and an interpretation-based semantics for queries, keeping the latter semantics throughout. We show how inference can be conducted in crALC and present examples with real ontologies that display the level of scalability of our proposals.

We investigate on modeling uncertain concepts via rough description logics, which are an extension of traditional description logics by a simple mechanism to handle approximate concept definitions through lower and upper approximations of concepts based on a rough-set semantics. This allows to apply rough description logics for modeling uncertain knowledge. Since these approximations are ultimately grounded on an indiscernibility relationship, the paper explores possible logical and numerical ways for defining such relationships based on the considered knowledge. In particular, the notion of context is introduced, allowing for the definition of specific equivalence relationships, to be used for approximations as well as for determining similarity measures, which may be exploited for introducing a notion of tolerance in the indiscernibility.

In this paper, we describe an ongoing reasoner implementation for reasoning with generalized Bayesian dl-programs and thus for dealing with deterministic ontologies and logic programs and probabilistic (mapping) rules in an integrated framework.

One of the important problems of semantic web is checking whether two datasets describe the same quantity. The existing solution to this problem is to use these datasets’ ontologies to deduce that these datasets indeed represent the same quantity. However, even when ontologies seem to confirm the identify of the two corresponding quantities, it is still possible that in reality, we deal with somewhat different quantities. A natural way to check the identity is to compare the numerical values of the measurement results: if they are close (within measurement errors), then most probably we deal with the same quantity, else we most probably deal with different ones. In this paper, we show how to perform this checking.

The great evolution of ontologies during the last decade, bred the need for storage and querying for the Semantic Web. For that purpose, many RDF tools capable of storing a knowledge base, and also performing queries on it, were constructed. Recently, fuzzy extensions to description logics have gained considerable attention especially for the purposes of handling vague information in many applications. In this paper we investigate on the issue of using classical RDF storing systems in order to provide persistent storing and querying over large-scale fuzzy information. To accomplish this we first propose a novel way for serializing fuzzy information into RDF triples thus classical storing systems can be used without any extensions. Additionally, we extend the existing query languages of RDF stores in order to support expressive fuzzy queries proposed in the literature. These extensions are implemented through the FiRE fuzzy reasoning engine, which is a fuzzy DL reasoner for fuzzy-SHIN. Finally, the proposed architecture is evaluated using an industrial application scenario about casting for TV commercials and spots.

We present a novel procedure FixIt(ALC) for deciding knowledge base satisfiability in the Fuzzy Description Logic (FDL) ALC. FixIt(ALC) does not search for tree-structured models as in tableau-based proof procedures, but embodies a fixpoint-computation of canonical models that are not necessarily tree-structured. Conceptually, the procedure is based on a type-elimination process. Soundness, completeness and termination are proven. To the best of our knowledge it is the first fixpoint-based decision procedure for FDLs, hence introducing a new class of inference procedures into FDL reasoning.

Classical ontologies are not suitable to represent imprecise or vague pieces of information, which has led to fuzzy extensions of Description Logics. In order to support an early acceptance of the OWL 1.1 ontology language, we present DeLorean, the first reasoner that supports a fuzzy extension of the Description Logic SROIQ, closely equivalent to it. It implements some interesting optimization techniques, whose usefulness is shown in a preliminary empirical evaluation.

Interpreting Semantic Web Data by different human experts can end up in scenarios, where each expert comes up with different and conflicting ideas what a concept can mean and how they relate to other concepts. Software agents that operate on the Semantic Web have to deal with similar scenarios where the interpretation of Semantic Web data that describes the heterogeneous sources becomes contradicting. One such application area of the Semantic Web is ontology mapping where different similarities have to be combined into a more reliable and coherent view, which might easily become unreliable if the conflicting beliefs in similarities are not managed effectively between the different agents. In this paper we propose a solution for managing this conflict by introducing trust between the mapping agents based on the fuzzy voting model.

Fuzzy Description Logics (f-DLs) are extensions of classic DLs that are capable of representing and reasoning about imprecise and vague knowledge. Though reasoning algorithms for very expressive fuzzy DLs have been explored, an open issue in the fuzzy DL community is the study of tractable systems. In this paper we introduce the fuzzy extension of EL++, we provide its syntax and semantics together with a reasoning algorithm for the fuzzy concept subsumption problem, in which other problems related to fuzzy DLs can be reduced.

An Ontology of Uncertainty, like the one proposed by the W3C’s UR3W-XG incubator group, provides a vocabulary to annotate different sources of information with different types of uncertainty. Here we argue that such annotations should be clearly mapped to corresponding reasoning and representation strategies. This mapping allows the system to analyse the information on the basis of its uncertainty model, running the inference proccess according to the respective uncertainty. As a proof of concepts we present a data integration system implementing a semantics-aware matching strategy based on an ontological representation of the uncertain/inconsistent matching relations generated by the various matching operators. In this scenario the sources of information to be analyzed according to different uncertainty models are independent and no intersection among them is to be managed. This particular case allows a straight-forward use of the Ontology of Uncertainty to drive the reasoning process, although in general the assumption of independence among the source of information is a lucky case. This position paper highlights the need of additional work on the Ontology of Uncertainty in order to support reasoning processes when combinations of uncertainty models are to be applied on a single source of information.

In this position paper we discuss the what, who, when, where, why and how of uncertain reasoning based on achievements of URW3-XG, our experiments and some future plans.

The Uncertainty Reasoning for the World Wide Web Incubator Group (URW3-XG) was chartered as a means to explore and better define the challenges of reasoning with and representing uncertain information in the context of the World Wide Web. The objectives of the URW3-XG were: (1) To identify and describe situations on the scale of the World Wide Web for which uncertainty reasoning would significantly increase the potential for extracting useful information; and (2) To identify methodologies that can be applied to these situations and the fundamentals of a standardized representation that could serve as the basis for information exchange necessary for these methodologies to be effectively used. This paper describes the activities undertaken by the URW3-XG, the recommendations produced by the group, and next steps required to carry forward the work begun by the group.