Maria I. Sessa

Similarity-based unification

Unification plays a central rule in Logic Programming. We ”soften” the unification process by admitting that two first order expressions can be ”similar” up to a certain degree and not necessarly identical. An extension of the classical unification theory is proposed accordingly. Indeed, in our approach, inspirated by the unification algorithm of Martelli-Montanari, the systems of equations go through a series of ”sound” transformations until a solvable form is found yielding a substitution that is proved to be a most general extended unifier for the given system of equations.

Symbol-Relation Grammars

A common approach to the formal description of pictorial and visual languages makes use of formal grammars and rewriting mechanisms. The present paper is concerned with the formalism of Symbol?Relation Grammars (SR grammars, for short). Each sentence in an SR language is composed of a set of symbol occurrences representing visual elementary objects, which are related through a set of binary relational items. The main feature of SR grammars is the uniform way they use context-free productions to rewrite symbol occurrences as well as relation items. The clearness and uniformity of the derivation process for SR grammars allow the extension of well-established techniques of syntactic and semantic analysis to the case of SR grammars. The paper provides an accurate analysis of the derivation mechanism and the expressive power of the SR formalism. This is necessary to fully exploit the capabilities of the model. The most meaningful features of SR grammars as well as their generative power are compared with those of well-known graph grammar families. In spite of their structural simplicity, variations of SR grammars have a generative power comparable with that of expressive classes of graph grammars, such as the edNCE and the N-edNCE classes.

Similarity in Logic Programming

By introducing a similarity relation R between constant and predicate symbols in the language of a logic program P, it is possible to perform approximate inferences. Indeed, it allows us to manage alternative instances of entities that can be considered“equal” with a given degree. We analyze the semantics of this approach exploiting an abstract interpretation technique. The abstract domain is obtained by considering suitable equivalence relations associated with the similarity R. The optimality of the abstract semantics is proved and the definition of fuzzy Herbrand model is also introduced.

Similarity-based SLD resolution and its role for web knowledge discovery

This work presents the implementation of an extension of SLD resolution towards approximate reasoning and its implementation in an extended Prolog system. The proposed refutation procedure overcomes failures in the unification process by exploiting similarity relations defined between predicate and constant symbols. This enables to compute approximate solutions, with an associated approximation degree, when failures of the exact inference process occur. In this paper we outline the main ideas of this approach and we present an extended PROLOG interpreter, named SiLog, which implements this inference procedure. Then we point out on a web-based platform, usable for knowledge discovery, that exploits as inner feature the similarity-based SLD resolution.

Translations and similarity-based logic programming

Abstract In order to provide approximate reasoning capabilities, in Gerla G, Sessa MI (1999) Chen G, Ying M, Cai K-Y (Eds) Fuzzy Logic and Soft computing, 19–31, Kluwer Academic Publishers, Boston an extension of Logic Programming has been proposed. Logic programs on function-free languages are considered, and approximate and imprecise information are represented by introducing a similarity relation ? in the set of predicate names and object names of the language. The inference system exploits the classical resolution rule of the Logic Programming paradigm. Moreover, the notion of fuzzy least Herbrand model is also provided. In this paper, by introducing the general notion of structural translation of languages, we generalize these results to the case of logic programs with function symbols. Some properties of the similarity relations are also proven.

Web navigation support by means of proximity-driven assistant agents

The explosive growth of the Web and the consequent exigency of the Web personalization domain have gained a key position in the direction of customization of the Web information to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the users navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. This work presents an agent-based framework designed to help a user in achieving personalized navigation, by recommending related documents according to the users responses in similar-pages searching mode. Our agent-based approach is grounded in the integration of different techniques and methodologies into a unique platform featuring user profiling, fuzzy multisets, proximity-oriented fuzzy clustering, and knowledge-based discovery technologies. Each of these methodologies serves to solve one facet of the general problem (discovering documents relevant to the user by searching the Web) and is treated by specialized agents that ultimately achieve the final functionality through cooperation and task distribution.

Similarity-based SLD resolution and its implementation in an extended Prolog system

This paper presents an extension of SLD resolution towards approximate reasoning. The proposed refutation procedure overcomes failures in the unification process by exploiting similarity relation defined between predicate and constant symbols. This enables to compute approximate solutions, with an associated approximation degree, when failures of the exact inference process occur. In this paper we outline the main ideas of this approach and we present an extended PROLOG interpreter, named SiLog, which implements this inference procedure.

Interactive knowledge management for agent‐assisted web navigation

Web information may currently be acquired by activating search engines. However, our daily experience is not only that web pages are often either redundant or missing but also that there is a mismatch between information needs and the webs responses. If we wish to satisfy more complex requests, we need to extract part of the information and transform it into new interactive knowledge. This transformation may either be performed by hand or automatically. In this article we describe an experimental agent‐based framework skilled to help the user both in managing achieved information and in personalizing web searching activity. The first process is supported by a query‐formulation facility and by a friendly structured representation of the searching results. On the other hand, the system provides a proactive support to the searching on the web by suggesting pages, which are selected according to the users behavior shown in his navigation activity. A basic role is played by an extension of a classical fuzzy‐clustering algorithm that provides a prototype‐based representation of the knowledge extracted from the web. These prototypes lead both the proactive suggestion of new pages, mined through web spidering, and the structured representation of the searching results.

Discovering related Web pages through fuzzy-context reasoning

The rapid growth of Web resources makes very difficult the task of Web search engines. Nevertheless powerful search crawlers have been developed to aid in locating unfamiliar document (by means of category, contents or subject based approaches), often queries return inconsistent results. The main lack of Web searching is in the deduction capability: nowadays Web searching put much attention in matching users queries that are too weak to cope with the users expressiveness. First attempts in extending searching towards deduction capability are essentially based on two-valued logic and standard probability theory. The complexity of the problem (8.4 million of Web sites), the features of the space domain (unstructured data, immature standards) demand a strong deviation from this trend. This work presents some results stemmed from a research projects where different technologies (in particular mobile agents and approximate reasoning) have been merged into an operational architecture suitable for Web searching/Web discovering. This paper discusses a different approach to Web searching where the input to the retrieval process is described through a Web page. The system reacts to this kind of query by returning a set of Web pages that reflect a similar context and deals with related arguments.

Redundancy Elimination and Loop Checks for Logic Programs

A simple analysis of the arguments developed by Bol et al. (Theoret. Comput. Sci.86, 35-79 (1991)) shows that an actual reason for the nonexistence of a complete sound simple check for all function-free programs is the presence in the resolvents of potentially unlimited sequences of atoms chained by common variables. This hints that a limitation of the number of variables generating this kind of chain could guarantee the applicability of complete simple loop checks. This line is followed in the paper, and quite general classes of logic programs are characterized, without any direct imposition on the structures of the rules. This objective is accomplished by exploiting a variant of SLD-resolution, which is able to perform a systematic elimination of redundant atoms from resolvents. As a notable result, it turns out that the equality loop check is complete for our class of logic programs. This seems to suggest that the necessity of using subsumption loop checks instead of equality checks is essentially due to the presence of redundant atoms in resolvents.