Catálogo de publicaciones - libros

Ontologies evolve when the underlying domain world changes at different points of time. The result then is a series of ontologies whose concepts are related with each other not only within one ontology valid at a moment but through the time, too. This chapter presents a model for representing ontology time series. The focus is on modeling partial overlap between concepts evolving over long periods of time, and the domain of application is historical geospatial reasoning. A framework is presented for representing and reasoning about conceptual overlap of concepts that evolve over an ontology time series. The idea is to provide the ontology developer with an intuitive change ontology for expressing local ontological changes in a declarative way. An algorithm is presented for reasoning about overlapping concepts globally over long periods of time. This algorithm can be applied, e.g., in concept-based information retrieval for ranking search results according to their relevance.

Ontologies are an essential component in Information Systems since they enable knowledge re-use and sharing in a formal, homogeneous and unambiguous way. A domain ontology captures knowledge in a static way, as it is snapshot of knowledge from a particular point of view in a specific time-period. However, in open and dynamic settings, where knowledge changes and evolves, ontology maintenance methods are required to keep knowledge up-to-date. In this chapter we tackle the problem of ontology maintenance as an ontology population problem of the evolving ontologies proposing an incremental ontology population methodology that exploits machine learning techniques and is enforced with a bootstrapping technique in order to tackle large scale problems. The methodology is enriched with fine-tuning methods towards improving the quality and the number of the discovered instances. Finally, experimental results are shown, which prove the applicability and effectiveness of the proposed methodology.

Ontologies can play a very important role in information systems. They can support various information system processes, particularly information acquisition and integration. Ontologies themselves need to be designed, built and maintained. An important part of the ontology engineering cycle is the ability to keep a handcrafted ontology up to date. Therefore, we have developed a tool called MnM that helps during the ontology maintenance process. MnM extracts information from texts and populates ontology. It uses NLP (Natural Language Processing), Information Extraction and Machine Learning technologies. In particular, MnM was tested using an electronic newsletter consisting of news articles describing events happening in the Knowledge Media Institute (KMi). MnM could constitute an important part of an ontology-driven information system, with its integrated web-based ontology editor and provision of open APIs to link to ontology servers and to integrate with information extraction tools.

Too many systems constructed by the software industry fail to meet users’ needs. Requirements elicitation is the set of activities performed to understand users’ needs for a system. Although most texts focus on a few elicitation techniques, there are numerous variations of these basic techniques. So, the question arises, how can an analyst understand all these techniques and their variations? Moreover, most experts today agree that the selection of an appropriate technique must be a function of the situation. But, a seemingly infinite number of situational characteristics exist. So, how can an analyst know which of these many situational characteristics should be taken into account when trying to select elicitation techniques? And, how does an analyst select a technique that makes sense given those situational characteristics?

The overarching goal of this research is to construct an information system to aid novice analysts in selecting the most effective requirements elicitation techniques for their project situation. Fundamental to the success of this endeavor is the creation of an ontology which: (1) sets the context for requirements elicitation and elicitation technique selection; (2) defines key characteristics of elicitation techniques that highlight their essential similarities and differences; and (3) identifies the important characteristics of a situation that should be considered when selecting an elicitation technique. This chapter describes the iterative ontology engineering approach used, summarizes the proposed requirements elicitation ontology, and demonstrates how the ontology will be used as a basis for an information system to assist analysts in selecting an appropriate elicitation technique. As a result, this chapter, rather than focusing on ontology research per se, focuses on the application of ontologies to improve the state of research and practice in one specific information systems discipline — requirements elicitation.

Knowledge intensive business processes are a category of business processes that rely on experience and expert judgment. Automating such processes is a challenge for most enterprises. This chapter introduces the characteristics of such processes, provides some examples and describes the architecture for implementing a system that caters to knowledge intensive business processes.

In this paper we introduce and discuss our approach to creating an object model from a problem domain text description as a basic deliverable of the analysis phase in Object-Oriented Software Engineering using ontologies. For this purpose we first briefly compare object models with ontologies. The object model of a system consists of objects, identified from the text description and structural linkages corresponding to existing or established relationships. The ontologies provide metadata schemas, offering a controlled vocabulary of concepts. At the center of both object models and ontologies are objects within a given problem domain. The both concepts are based on reusability using intensively libraries. The major difference is that while the object model contains explicitly shown structural dependencies between objects in a system, including their properties, relationships and behavior, the ontologies are based on related terms (concepts) only. Because ontology is accepted as a formal, explicit specification of a shared conceptualization, we can naturally link ontologies with object models, which represent a system-oriented map of related objects. To become usable programming entities these objects should be described as Abstract Data Types (ADTs). This paper addresses ontologies as a basis of a complete methodology for object identification and their modeling as (converting to) ADTs, including procedures and available tools such as CORPORUM OntoExtract and VisualText, which can help the conversion process. This paper describes how the developers can implement this methodology on the base of an illustrative example.

This chapter takes the complex knowledge systems of metaphors and shows that their structured knowledge can be represented and predicted by ontology. The complex knowledge system of metaphors contains two knowledge systems, source domain and target domain, as well as the knowledge mapping between the two domains. Hence metaphors offer a test case of how structured knowledge can be manipulated in an information system. In terms of the theory of metaphor, we integrate the Conceptual Mapping Model with an ontology-based knowledge representation. We demonstrate that conceptual metaphor analysis can be restricted and eventually, automated. In terms of knowledge processing, we argue that the knowledge structure encoded in ontology, such as the Suggested Upper Merged Ontology (SUMO), is the necessary foundation for manipulating information from multi-domain and multilingual sources. We first extract source domain knowledge structure based on ontology. Next we show that the ontological account allows correct explanation of the parallel yet different use of the same source domain in two different languages. Thirdly, we showed that the restricted set of upper ontology can be combined with the open lexical knowledgebase of wordnets to provide a principled, yet robust, general coverage of language-based knowledge systems.

The Knowledge Collective is a multi-layer, multi-agent framework for information reuse in an intelligent knowledge base that supports a collection of agents called MicroDroids, which provide information management capabilities through a variety of interfaces for experts, human users, and software components. This information is stored in a variety of internal structures (e.g., Java objects, rules, database structures). The main concept is that information is stored in a format that is natural to the type of information being maintained (e.g., data, metadata, ontologies, concept maps, lexicons, rules). The Knowledge Collective will make ontology based information accessible to many end users, maintainable by domain experts and reusable by many users across many applications without knowing how or where the information is stored. The Knowledge Collective’s first use is in version 4 of CIRCSIM-Tutor, an Intelligent Tutoring System developed at the Illinois Institute of Technology in Chicago, IL.

The emergence of Web technologies has made widespread the use of hypermedia systems as the underlying support for Information Systems in organizations. Hypermedia elements and their associated functionality in this context become organizational assets that are created, improved and delivered to users in an attempt to increase the overall value of the system. Semantic Web approaches to Information Systems focus on providing computational semantics to resources by means of shared meanings encoded as part of formal ontologies. These meanings in turn are intended to enable a higher degree of automation and delegation of tasks to software agents. This chapter addresses the fundamental elements of the ontological representation of hypermedia structures and their connection to the main aspects of Information Systems in the organizational context. Concretely, the integration of hypermedia concepts in a Knowledge Management framework is described, and the role of adaptiveness is characterized as a function driven by organizational value inside such framework. The resulting ontological framework provides ground for the development of ontology-based Information Systems in which hypermedia assets are managed.

Many large organizations have their data and processing spread across multiple independent database applications. These data sources, with their own schemas, need to inter-operate to meet new requirements, both within and across organizations. In this paper, we propose a vision of an ontology-enabled database management systems (called OeDBMS) so that the end users can co-relate and integrate ontologies associated with individual sources and extract, co-relate and integrate data from different sources. We propose the architecture and ontology model for OeDBMS. We propose many useful extensions to the RDF/S-based ontology models that are emerging as standards, and provide a graph-based abstraction for the model. This becomes a basis for defining many useful ontology operators and an ontology query language for browsing, searching, matching and maintaining ontologies. We also address the need for ontology evolution by providing temporal support for ontology.