Rosario Girardi

A domain model of Web recommender systems based on usage mining and collaborative filtering

Considering the increasing demand of multi-agent systems, the practice of software reuse is essential to the development of such systems. Multi-agent domain engineering is a process for the construction of domain-specific agent-based reusable software artifacts, like domain models, representing the requirements of a family of multi-agent systems in a domain, and frameworks, implementing reusable agent-based design solutions to those requirements. This article describes the domain modeling tasks of the MADEM methodology and a case study on the application of GRAMO, a MADEM technique, for the construction of the domain model of ONTOWUM, specifying the common and variable requirements of a family of Web recommender systems based on usage mining and collaborative filtering.

A system of agent-based software patterns for user modeling based on usage mining

In adaptive hypermedia systems, a user can select explicitly an adaptation effect or he/she can leave the system execute some of these functions. An important component of an adaptive system is the ability to model the users of the system according to their goals and preferences. Web usage mining aims at discover interesting patterns of use by analyzing Web usage data. This information can be used to capture implicitly user models and used them for the adaptation of systems. User modeling and system adaptability can be approached through the agent paradigm. This article summarizes a system of architectural and detailed design patterns describing known agent-based solutions to recurrent problems of user modeling based on usage mining along with the description of a general purpose problem-solving architectural pattern used by some of the first ones. Patterns are derived from recurrent designs of specific agent-based applications. The proposed patterns are being developed in the context of a Multi-Agent Domain Engineering research project, which approaches software complexity and productivity through the construction of techniques and tools promoting software reuse in Multi-Agent Domain Engineering.

A domain-independent process for automatic ontology population from text

Ontology Population looks for instantiating the constituent elements of an ontology, like properties and non-taxonomic relationships. Manual population by domain experts and knowledge engineers is an expensive and time consuming task. Fast ontology population is critical for the success of knowledge-based applications. Thus, automatic or semi-automatic approaches are needed. This work proposes a generic process approaching the Automatic Ontology Population problem by specifying its phases and the techniques used to perform the activities on each phase. The main contribution of the work here described is a domain-independent process for the automatic population of ontologies from text that applies natural language processing and information extraction techniques to acquire and classify ontology instances. This is a new approach for automatic ontology population that uses an ontology to automatically generate rules to extract instances from text and classify them in ontology classes. These rules can be generated from ontologies of any domain, making the proposed process domain-independent and therefore, allowing the instantiation of ontologies quickly and at a low cost. Four experiments using a legal and a tourism corpora were conducted in order to evaluate the proposed process. Results indicate that this approach can extract and classify instances with high effectiveness with the additional advantage of domain independence. Some techniques representing the state of the art of this field are also described along with the solutions they adopt for each phase of the Automatic Ontology Population process with their advantages and limitations. We systematize the problem of Automatic Ontology Population.We propose a domain-independent process for Automatic Ontology Population.Our process overcomes the limitation of others of dependence of a domain.We conduct four experiments using a legal and a tourism corpora.Good effectiveness against its peers and adaptability are its main advantages.

Extracting ontology concept hierarchies from text using Markov logic

Ontologies have proven to be a powerful tool for many tasks such as natural language processing and information filtering and retrieval. However their development is an error prone and expensive task. One approach for this problem is to provide automatic or semi-automatic support for ontology construction. This work presents the Probabilistic Relational Hierarchy Extraction (PREHE) technique, an approach for extracting concept hierarchies from text that uses statistical relational learning and natural language processing for combining cues from many state-of-the-art techniques. A Markov Logic Network has been developed for this task and is described here. A preliminary evaluation of the proposed approach is also outlined.

A Process for Extracting Non-Taxonomic Relationships of Ontologies from Text

Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semiautomatic approaches are needed. Ontology learning looks for identifying ontology elements like non-taxonomic relationships from information sources. These relationships correspond to slots in a frame-based ontology. This article proposes an initial process for semiautomatic extraction of non-taxonomic relationships of ontologies from textual sources. It uses Natural Language Processing (NLP) techniques to identify good candidates of non-taxonomic relationships and a data mining technique to suggest their possible best level in the ontology hierarchy. Once the extraction of these relationships is essentially a retrieval task, the metrics of this field like recall, precision and f-measure are used to perform evaluation.

PARNT: A Statistic based Approach to Extract Non-Taxonomic Relationships of Ontologies from Text

Learning Non-Taxonomic Relationships is a sub-field of Ontology learning that aims at automating the extraction of these relationships from text. This article proposes PARNT, a novel approach that supports ontology engineers in extracting these elements from corpora of plain English. PARNT is parametrized, extensible and uses original solutions that help to achieve better results when compared to other techniques for extracting non-taxonomic relationships from ontology concepts and English text. To evaluate the PARNT effectiveness, a comparative experiment with another state of the art technique was conducted.

An ontology-based knowledge base for the representation and reuse of software patterns

The description, localization and effective reuse of software patterns and systems of patterns can be approached through an ontology-based formalism. An ontology is an explicit specification of objects, concepts and entities of an area of interest, besides the relationships between these concepts expressed through axioms. This work introduces ONTOPATTERN, an ontology that represents knowledge about how patterns are described and about their relationships in a pattern system. Patterns are included as instances of classes in the ontology, thus turning ONTOPATTERN a knowledge base where concepts are semantically related and where searches and inferences can be made thus facilitating the understanding and reuse of patterns. The use of ONTOPATTERN is illustrated through an example on the construction of a multi-agent framework.

Evaluating techniques for learning non-taxonomic relationships of ontologies from text

Abstract Learning non-taxonomic relationships is a sub-field of Ontology Learning that aims at automating the extraction of these relationships from text. Several techniques have been proposed based on Natural Language Processing and Machine Learning. However just like for other techniques for Ontology Learning, evaluating techniques for learning non-taxonomic relationships is an open problem. Three general proposals suggest that the learned ontologies can be evaluated in an executable application or by domain experts or even by a comparison with a predefined reference ontology. This article proposes two procedures to evaluate techniques for learning non-taxonomic relationships based on the comparison of the relationships obtained with those of a reference ontology. Also, these procedures are used in the evaluation of two state of the art techniques performing the extraction of relationships from two corpora in the domains of biology and Family Law.

Architectural design of a multi-agent recommender system for the legal domain

Legal information sources are characterized by their growth and dynamism since new laws are written every day. Recommender systems are used as an approach to the information overload problem. Thus they can help professionals of the legal area to deal with legal information sources. This paper describes the architectural design of Infonorma, a multi-agent recommender system for the legal domain. Infonorma monitors a repository of legal normative instruments and classifies them into legal branches. Each user specifies his/her interests for certain legal branches and receives recommendations of instruments they might be interested in. The information source is entirely written according to Semantic Web standards. Infonorma was developed under the guidelines of MAAEM, a software development methodology for multi-agent application engineering.

DDEMAS: a domain design technique for multi-agent domain engineering

Multi-agent Domain Engineering is a process for the construction of domain-specific agent-oriented reusable software artifacts, like domain models representing the requirements of a family of multi-agent systems, and frameworks, implementing an agent-oriented solution to those requirements. This work describes DDEMAS, an ontology-based technique for the architectural and detailed design of multi-agent frameworks providing a solution to the requirements of a family of multi-agent software systems specified in a domain model. DDEMAS is part of MADEM, a methodology for domain analysis and design of a family of multi-agent systems in a domain. Domain models and multi-agent frameworks are part of a knowledge base constructed through the instantiation of ONTOMADEM, an ontology that represents the knowledge of MADEM. Some examples from a case study on the application of DDEMAS on the construction of a multi-agent framework for the development of usage mining-based Web personalization systems are also described.