Abstract: The paper proposes an approach to information retrieval based on the use of a fuzzy conceptual structure (ontology) that is used both for indexing document and expressing user queries. The conceptual structure is hierarchical and it encodes the knowledge of the topical domain of the considered documents. It is formally represented as a weighted tree. In this approach, the evaluation of conjunctive queries is based on the comparison of minimal sub-trees containing the two sets of nodes corresponding to the concepts expressed in the document and the query respectively. The comparison is based on the computation of a multiple-valued degree of inclusion. Some candidate implications are discussed on the basis of their respective semantics. The proposed approach generalizes standard fuzzy information retrieval and its evaluation on benchmark example is also presented.

Abstract: Continuous t -norm based fuzzy predicate logic is surveyed as a generalization of classical predicate logic; then a kind of fuzzy description logic based on our fuzzy predicate logic is briefly described as a powerful but still decidable formal system of description logic dealing with vague (imprecise) concepts.

Abstract: The explosive growth in volumes of data and the growing number of disparate data sources are exposing researchers to a ‘new’ challenge — how to acquire, maintain and share knowledge from large and distributed databases in the context of rapidly evolving research. Our approach addresses such questions by integrating soft computing techniques and ontology engineering. The primary outcome of this work is a process that facilitates the creation of evolving ontologies that are able to represent the dynamic and uncertain nature of domains in order to provide constant and ongoing support for the decision making process over time.

Abstract: Ontology is an essential ingredient to improve information search efficiency and success. Ontology can be used to provide semantic-based access to the Web documents and extract meaningful information from texts. This chapter presents an information search model based on ontology encoded by fuzzy relations. The model uses the principles of fuzzy set theory and approximate reasoning for knowledge representation and information search. Two query algorithms are developed emphasizing, without loss of generality, document search. Experimental results show that the fuzzy relational ontological model performs better when compared with two alternative approaches based on thesauri and fuzzy conceptual network.

Abstract: Fuzzy logic and soft computing have important roles to play in the development of the semantic web. Ontologies represent a means of representing and sharing knowledge between users and systems, and are important tool for information retrieval. However many different ontologies have been established, and communication between them can be difficult. In particular, many terms are located in multiple locations within large ontologies. Different stakeholders in the information retrieval process, “Authors”, “Readers” and “Librarians”, are identified. This work deals with the concept of the “Fuzzy Ontology”, which is designed to allow the representation of differing viewpoints within a single framework. The means of constructing, refinement and use of the fuzzy ontology are described. Fuzzy ontology membership values can be learned from documents or users, based around existing ontologies such as the Unified Medical Language System (UMLS). Use of the fuzzy ontology approach for information retrieval may allow the efficient sharing of knowledge between users and groups.

Abstract: This paper presents three systems that are fully implemented and a proposal for a fourth one. scKasimir is a knowledge based-system using an ad hoc formalism similar to a simple description logic with concrete domains which is used for representing decision protocols in oncology. scFuzzy-Kasimir is an extension of scKasimir with fuzzy concrete domains taking into account discontinuities in the decision that are due to numerical thresholds. Another extension of scKasimir has led to embed it into a semantic portal for oncology, which has been motivated by the need to share knowledge for geographically distributed physicians and has led to change the ad hoc formalism to the standard OWL DL. A combination of these two extensions of scKasimir is currently under implementation and will lead to a semantic portal for oncology with fuzzy datatypes.

Abstract: It is generally accepted that knowledge based systems would be smarter if they can manage uncertainty. In this paper we extend Description Logics, well-known logics for managing structured knowledge, towards the management of uncertainty. We allow (i) to express that a sentence is not just true or false, but certain to some degree, which is taken from a certainty lattice; and (ii) combine the logic with annotated logic programming, in which the management of uncertainty is based on so-called annotation terms.

Abstract: The paper is dealing with the problem of finding best (top- k ) answers depending on scoring multicriterial user requirements which appears quite often on the web. Our solution is based on formal models of many valued logic (fuzzy, annotated) where user preference (score) is modeled by ordering of truth values — meaning best answer is the one with biggest truth value. The user global score is modeled by fuzzy aggregation (annotation) operators of all user attribute score. In this paper, we present several many valued definite logic programming based techniques with fuzzy aggregation. We present models for both deductive and inductive tasks and also an extension with similarities and finite domains. We advocate for an extension of web rule languages by fuzzy aggregation.

Abstract: The combination of information from multiple semi-structured (or structured) sources is a widespread problem. Given two sources s1 and s2 which refer to approximately the same sets of real world entities, the information fusion task is to determine whether an object from source s1 refers to the same real-world entity as an object from source s2, and how the properties correspond (e.g., author and composer should correspond almost exactly to creator, business-name should correspond to company-name, etc.). It is rare for two sources to adhere to precisely the same conventions. Even where agreed conventions exist, interpretations may differ — for example, consider an electronic order for goods with a piece of information marked as shippingDate. It is not clear whether shippingDate refers to the time when goods are delivered to the customer or the time when they leave the supplier. This is an example of a semantic gap — a term which can be interpreted in multiple and possibly inconsistent ways. The semantic web vision is for “islands of standardisation” in which there are small, agreed ontologies for particular sets of users or communities. Wherever two communities meet or overlap, integration of information is a potential problem. In this paper we briefly outline the background to this problem, and argue that it is essential to account for the uncertainty inherent in the process. We go on to describe a framework for integrating information sources, given a number of approximate mappings between their attributes.

Abstract: As highlighted by the World Wide Web Consortium, XML has been proposed to deal with huge volumes of electronic documents and is playing an increasing important role in the exchange of a wide variety of data on the Web. However, when dealing with such large and heterogeneous data sources, it is necessary to have an idea on the way these data sources are structured. This information is indeed essential in order to build mediator schemas. These mediator schemas are required to query data in a uniform way. Moreover, this information is interesting since it provides users with a semantic structure of the data they can query. Recently schema mining approaches have been proposed to extract in an efficient way the commonly occurring schemas from a collection. Nevertheless, according to the semantic point of view, such approaches suffer from different drawbacks. In this work, we propose thus a fuzzy approach, showing why and how fuzziness is useful in order to extract frequent approximate schemas.

Abstract: We propose a perception-based search methodology established on fuzzy semantic for improving the web search. Unlike most existing relevant work that focuses on filtering the returned search result, we study the search query semantic and expand the search to include information related to the linguistic and numerical fuzzy counterparts of the search query. The top 20 search results returned from this search methodology showed impressive improvement over the conventional one. Fifty percent average gain in search relevancy is obtained when our search methodology is applied to websites matching the chosen fuzzy semantic theme. We demonstrate the effectiveness of our methodology on the search domain of health and food.

Abstract: The capability for intelligent human like question answering is a fundamental goal of the semantic web. This capability requires a semantically enhanced, human like representation of various types of knowledge. It also requires a framework for providing a global plan for mobilizing the available knowledge to address the question posed. This should also direct us in seeking additional knowledge to help in the task. It also needs rules for making deductions from subsets of the knowledge base, performing local reasoning. We describe how the semantically rich fuzzy set based theory of approximate reasoning and the related paradigm of precisiated natural language can aid in the process of representing knowledge. The idea of a knowledge tree is introduced to provide a global framework for mobilizing the knowledge base in response to a query. We discuss how protoforms can be used to aid in deduction and local manipulation of knowledge. In addition to considering ordinary categorical knowledge we look at some types commonsense and default knowledge. These additional types of knowledge requires us to address the complexity of the non-monotonicity that these types of knowledge often display.

Abstract: Semantic Web, as extension of the current one, promises to provide more automated services based on machine-processable semantics of data and heuristics that make use of metadata. A key challenge of the Semantic Web is the adaptation of Web contents according to web parameters, ontologies, dictionaries. Processing metadata scalability involves infrastructures to interpret, translate and convert different metadata languages automatically. In addition, contextual information and user exigencies should also be considered to assure flexible and proactive interaction. The emergence of sharing semantic knowledge moves towards comfortable reuse of knowledge (thanks to the availability of metadata dictionaries) and machine-processable semantics of data, (through agent-based services, middleware applications, ontology-based system). In this sense, benefits of garnering information come from middle-way services (brokering, matchmaking assistance) aimed to enable more interoperability among heterogeneous approaches. This work is based on a web service infrastructure for semantic-based information discovery. Through techniques of fuzzy clustering, human comprehensible information (relevant metadata) are captured from an RDF-oriented collection of machine-readable documents, in order to support semantic navigation into the Web that is very heterogeneous space, in terms of languages, dictionaries and ontologies. This approach proposes a semantic RDF-oriented classification of documents as prerequisite for a suitable characterization of relationships between concepts and web resources.

Abstract: World Wide Web search engines have become the most heavily-used online services, with millions of searches performed each day. Their popularity is due, in part, to their ease of use. It is important to note that while the Semantic Web is dissimilar in many ways from the World Wide Web, the Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries through the World Wide Web. In this paper, we would like to go beyond the traditional semantic web which has been defined mostly as a mesh or distributed databases within the World Wide Web. For this reason, our view is that “Before one can use the power of semantic web, the relevant information has to be mined through the search mechanism and logical reasoning”. The central tasks for the most of the search engines can be summarized as (1) query or user information request — do what I mean and not what I say!, (2) model for the Internet, Web representation — web page collection, documents, text, images, music, etc., and (3) ranking or matching function — degree of relevance, recall, precision, similarity, etc. Design of any new intelligent search engine should be at least based on two main motivations: (1) the web environment is, for the most part, unstructured and imprecise. To deal with information in the web environment what is needed is a logic that supports modes of reasoning which are approximate rather than exact. While searches may retrieve thousands of hits, finding decision-relevant and query-relevant information in an imprecise environment is a challenging problem, which has to be addressed and (2) another, and less obvious, is deduction in an unstructured and imprecise environment given the huge stream of complex information. In this paper, we will first present the state of the search engines and Internet. Then we will focus on development of a framework for reasoning and deduction in the web. A web-based model to decision model for analysis of structured database will be presented. A framework to incorporate the information from web sites into the search engine will be presented as a model that will go beyond current semantic web idea. Another important and unique component of our system is compactification algorithm or Z-Compact. Z-Compact algorithm developed by L.A. Zadeh and it has been implemented for the first time as part of BISC-DSS for automatons multi-agents modeling as part of ONR project and has been extended to handle linguistic variables with deduction capability and currently is part of the BISC-DSS software and its has been applied in several applications.

Abstract: The World Wide Web is moving to its next generation called Semantic Web. Managing and searching for web information goes beyond the conventional relational database model, as the data are semi-structured and inexact answers are often the case. This research work studies similarity and subsumption measures for matching knowledge graphs based on the non-directed and directed semantic distances between their entity types and attribute values. A knowledge base represented in RDF is constructed on an ontology that defines its concept types and relation types with their signatures. A query engine is developed and implemented to query the knowledge base, returning approximate answers with similarity and subsumption degrees. On the one hand, for a user-friendly interface and easily readable query expressions, conceptual graphs are employed at the front-end. On the other hand, in order to take the advantage of the existing platform of Sesame, the query modification tactic is first used to retrieve the knowledge graphs that are close to a query graph, before the matching degrees of those answer graphs are calculated.

Abstract: The actual “Version” of the World Wide Web is surely one of the most important achievements of the last decades. But it suffers from the intrinsic deficit to include the semantic structure of the offered information or knowledge as HTML was developed to format the content instead of describing the logical structure. While the actual research on semantic web and RDF offer improvements for this situation, it is likely that the problem will not be solved completely. This is mainly caused by the fuzziness of human language and the fact that the boundaries of ontologies cannot be defined exactly. This article shows a description and a general methodology based on fuzzy inference in order to close this gap. The usage of fuzzy logic allows it to represent and process ambiguous facts making it possible to reconstruct the fuzziness of the real world and human way of thinking. Based on this representation and ability of processing it, better search algorithms can be developed and implemented in order to gain better results.

Abstract: The best pair of shoes you can find are the ones that fit perfectly. No inch too short, no inch too wide or long. The same, of course, holds for applications in all fields of computer science. It should serve our needs perfectly. If it does more, it usually comes with a tradeoff in performance or scalability. On top of that, for logic based systems, the maintenance of a consistent knowledge base is important. Hence a decidable language is needed to maintain this consistency computationally. Recently, the restriction of the Semantic Web standard OWL to bivalent logic has been increasingly criticized for its inability to semantically express uncertainties. We will argue for the augmentation of the current standard to close this gap. We will argue for an increased expressiveness at different layers of the cake and we want to show that only a spiced up version of some of the layers can take the blandness out of it. We want to show that it is possible to have a mixture that can account for reasoning based on uncertainties, possibilistic measures, but also for other epistemologically relevant concepts, such as belief or trust.

Abstract: In this paper we present a fuzzy version of SHOIN ( d ), the corresponding Description Logic of the ontology description language OWL DL. We show that the representation and reasoning capabilities of fuzzy SHOIN ( d ) go clearly beyond classical SHOIN ( d ). Interesting features are: (i) concept constructors are based on t-norm, t-conorm, negation and implication; (ii) concrete domains are fuzzy sets; (iii) fuzzy modifiers are allowed; and (iv) entailment and subsumption relationships may hold to some degree in the unit interval

Abstract: This short paper intends first to emphasize the basic distinction between gradual truth and uncertainty, and its relevance when dealing with classification. Then, the representation capabilities of first-order possibilistic logic are pointed out, before briefly providing some hints, which may be of interest for dealing with uncertainty and handling some fuzzy features in description logic.

Abstract: Existing search engines, with Google at the top, have many truly remarkable capabilities. Furthermore, constant progress is being made in improving their performance. But what is not widely recognized is that there is a basic capability which existing search engines do not have: deduction capability — the capability to synthesize an answer to a query by drawing on bodies of information which reside in various parts of the knowledge base. By definition, a question-answering system, or Q/A system for short, is a system which has deduction capability. Can a search engine be upgraded to a question-answering system through the use of existing tools — tools which are based on bivalent logic and probability theory? A view which is articulated in the following is that the answer is: No. The first obstacle is world knowledge — the knowledge which humans acquire through experience, communication and education. Simple examples are: “Icy roads are slippery,” “Princeton usually means Princeton University,” “Paris is the capital of France,” and “There are no honest politicians.” World knowledge plays a central role in search, assessment of relevance and deduction. The problem with world knowledge is that much of it is perception-based. Perceptions — and especially perceptions of probabilities — are intrinsically imprecise, reflecting the fact that human sensory organs, and ultimately the brain, have a bounded ability to resolve detail and store information. Imprecision of perceptions stands in the way of using conventional techniques — techniques which are based on bivalent logic and probability theory — to deal with perception-based information. A further complication is that much of world knowledge is negative knowledge in the sense that it relates to what is impossible and/or non-existent. For example, “A person cannot have two fathers,” and “Netherlands has no mountains.” The second obstacle centers on the concept of relevance. There is an extensive literature on relevance, and every search engine deals with relevance in its own way, some at a high level of sophistication. But what is quite obvious is that the problem of assessment of relevance is quite complex and far from solution. There are two kinds of relevance: (a) question relevance and (b) topic relevance. Both are matters of degree. For example, on a very basic level, if the question is q: Number of cars in California? and the available information is p: Population of California is 37,000,000, then what is the degree of relevance of p to q? Another example: To what degree is a paper entitled “A New Approach to Natural Language Understanding” of relevance to the topic of machine translation. Basically, there are two ways of approaching assessment of relevance: (a) semantic; and (b) statistical. To illustrate, in the number of cars example, relevance of p to q is a matter of semantics and world knowledge. In existing search engines, relevance is largely a matter of statistics, involving counts of links and words, with little if any consideration of semantics. Assessment of semantic relevance presents difficult problems whose solutions lie beyond the reach of bivalent logic and probability theory. What should be noted is that assessment of topic relevance is more amendable to the use of statistical techniques, which explains why existing search engines are much better at assessment of topic relevance then question relevance. The third obstacle is deduction from perception-based information. As a basic example, assume that the question is q: What is the average height of Swedes?, and the available information is p: Most adult Swedes are tall. Another example is: Usually Robert returns from work at about 6 pm. What is the probability that Robert is home at about 6:15 pm? Neither bivalent logic nor probability theory provide effective tools for dealing with problems of this type. The difficulty is centered on deduction from premises which are both uncertain and imprecise. Underlying the problems of world knowledge, relevance and deduction is a very basic problem — the problem of natural language understanding. Much of world knowledge and web knowledge is expressed in a natural language. A natural language is basically a system for describing perceptions. Since perceptions are intrinsically imprecise, so are natural languages, especially in the realm of semantics. A prerequisite to mechanization of question-answering is mechanization of natural language understanding, and a prerequisite to mechanization of natural language understanding is precisiation of meaning of concepts and proposition drawn from a natural language. To deal effectively with world knowledge, relevance, deduction and precisiation, new tools are needed. The principal new tools are: Precisiated Natural Language (PNL); Protoform Theory (PFT); and the Generalized Theory of Uncertainty (GTU). These tools are drawn from fuzzy logic — a logic in which everything is, or is allowed to be, a matter of degree. The centerpiece of new tools is the concept of a generalized constraint. The importance of the concept of a generalized constraint derives from the fact that in PNL and GTU it serves as a basis for generalizing the universally accepted view that information is statistical in nature. More specifically, the point of departure in PNL and GTU is the fundamental premise that, in general, information is representable as a system of generalized constraints, with statistical information constituting a special case. This, much more general, view of information is needed to deal effectively with world knowledge, relevance, deduction, precisiation and related problems. In summary, the principal objectives of this paper are: (a) to make a case for the view that a quantum jump in search engine IQ cannot be achieved through the use of methods based on bivalent logic and probability theory; and (b) to introduce and outline a collection of non-standard concepts, ideas and tools which open the door to addition of deduction capability to search engines.

Abstract: In this chapter, several flexible techniques aimed at extracting, maintaining and enriching semantic-web style metadata are discussed. Such techniques were designed for being applied in the framework of dynamic Communities of Practice (CoP) interactions. Namely, we present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete objects. Unlike huge, “supply-side” normative ontologies, our bottom-up ontologies are based on use of implicit and, therefore, parsimonious part-whole and is-a relations. This makes them suitable for the ad-hoc style of conceptualization used within communities of practice and peer-to-peer (P2P) communities. Also we discuss how metadata based on bottom-up ontologies can be associated with a flexible degree of trust by collecting user feedback. Our bottom-up extraction method complements current practice, where, as a rule, ontologies are built top-down. It is not claimed that bottom-up construction is a generally valid recipe; rather, the approach is intended to enrich the ontology developer's palette when designing and implementing Semantic Web applications.