How to Support Human Operator in "Uncertainty" Managing during the Ontology Learning Process

Ontology plays a very important role in supporting knowledge-based applications. In cloud computing, ontology learning technology is facing new challenges in dealing with heterogeneous data sources from different domains and researchers, which may contain various particular concepts and relations. Traditional ontology learning frameworks usually focus only on the extraction of concepts and taxonomic relations from the multi-structured corpus. However, former researches rarely studied the interactions during ontology learning process among different researchers. Lack of interactions among people who build ontology in different domains may cause inconsistent ontology. Besides, lack of incentive during the ontology building process will also result in low efficiency. To address these challenges, this paper specifies a novel solution to perform ontology learning. The solution includes a service-oriented ontology interaction framework, a service-oriented ontology learning strategy. It shows that it advances ontology learning to a higher level of performance and portability with a number of experiments in demo system.

Most approaches to ontology learning combine techniques from different areas (hybrid approaches) to increase the efficiency of the ontology learning process. However, the results from the ontology learning process do not fully satisfy the users at present. An important problem is that there is a lack of quantitative and comparative data about the efficiency of techniques and technique combinations applied to ontology learning. In this paper we present a quantitative comparison of technique combinations for concept extraction and a software system (OntoLancs) to support the evaluation of techniques. By applying OntoLancs, users are able to assist the process of building ontologies by semi- automatically acquiring concepts from large-scale domain document collections and experiment with different combinations of knowledge acquisition techniques to refine and organize domain concepts into a taxonomy. Quantitative and comparative studies about the performance of several techniques and user experiences indicate the applicability and usefulness of our approach.

Smart cities are designed to overcome problems that traditional cities face like the exponential increase of the population and related needs. Smart cities design is a relatively new research field that appeared with the concept of smart city. Unfortunately, it is a laborious and error prone task which suggests designers' expertise and adequate skills. To overcome this issue, we attempt to acquire expertise from existing strategies and projects about smart cities. For this end, we develop strategy ontology. The strategy ontology allows not only to formalise and conceptualise the strategy related concepts but also to analyse existing projects and strategies. We attempt thereafter to enrich it via automatic learning techniques. We use therefore automatic annotation of smart cities related documents. In addition to automatic annotation, we also use the ontology-based information extraction (OBIE) for ontology population and enrichment with new concepts and instances. The followed process is composed of five tasks: corpus construction, ontology population, ontology enrichment, ontology's consistency verification and ontology learning evaluation. The resulting ontology will allow sharing the gathered knowledge between people participating in the activities of smart cities design and thus learning from smart cities previous projects and strategies in order to create new ones.

This paper proposes an ontology learning method which is used to generate a graphical ontology structure called ontology graph. The ontology graph defines the ontology and knowledge conceptualization model, and the ontology learning process defines the method of semiautomatic learning and generates ontology graphs from Chinese texts of different domains, the so-called domain ontology graph (DOG). Meanwhile, we also define two other ontological operations—document ontology graph generation and ontology graph-based text classification, which can be carried out with the generated DOG. This research focuses on Chinese text data, and furthermore, we conduct two experiments: the DOG generation and ontology graph-based text classification, with Chinese texts as the experimental data. The first experiment generates ten DOGs as the ontology graph instances to represent ten different domains of knowledge. The generated DOGs are then further used for the second experiment to provide performance evaluation. The ontology graph-based approach is able to achieve high text classification accuracy (with 92.3 % in f-measure) over other text classification approaches (such as 86.8 % in f-measure for tf–idf approach). The better performance in the comparative experiments reveals that the proposed ontology graph knowledge model, the ontology learning and generation process, and the ontological operations are feasible and effective.

This paper analyzes and evaluates, in the context of Ontology learning, some techniques to identify and extract candidate terms to classes of a taxonomy. Besides, this work points out some inconsistencies that may be occurring in the preprocessing of text corpus, and proposes techniques to obtain good terms candidate to classes of a taxonomy.

Organizations are demanding an efficacious knowledge management. Consequently, they are increasing their system innovation investments to turn information into useful knowledge for decision making obtained from heterogeneous Knowledge Sources (KSOs) such as databases, documents, and even ontologies. Methodological Resources (MRs) for the required knowledge discovering and recovering purposes have gradually become more elaborated and mature in the framework of Knowledge Engineering. Particularly, in the Ontology Learning (OL) field, there is a lack of integrated and open methodologies that could involve all the optional KSOs. In this sense, a systemic perspective is introduced combining MRs associated to diverse KSOs to improve the quality of an integral and continuous Knowledge Acquisition (KA) process. The main contributions provided by this work are on one hand, a novel Systemic Methodology for OL (SMOL) from heterogeneous KSOs which is applied for a case study and on the other hand, an evaluation of SMOL.

The research and development of artificial intelligence (AI) involve a generic representation known as ontology. Ontology is a generic representation of the domain data like medical ontology, manufacturing ontology, etc. The present work deals with the construction of the ontologies with Web pages like Naukri, Monster and Time Jobs Web sites, and the name of the ontology is job portals ontology. The reason behind the development of this work is to propose simple methods and models to create the ontology, to handle the changes happened to the source data and to apply the changes dynamically to the existing ontology. The concept of dynamic ontology creates more impact on the research of ontology models. We believe that the change management aspect is also helpful to the researchers so as to track the changes caused in the source data, in this case the Web portal data. The other dimension of the work is integration of recommender systems with ontology model so as to emit the resultant technologies which are not having sufficient resources in the job market. The proposed work involves the simple way of ontology creation, change management and dynamic ontology creation based on the changes of the source data. The outcome of the work is ontology creation, change management, dynamic ontology generation and a brief description of recommender systems usage.

In this paper we present Text2Onto, a framework for ontology learning from textual resources. Three main features distinguish Text2Onto from our earlier framework TextToOnto as well as other state-of-the-art ontology learning frameworks. First, by representing the learned knowledge at a meta-level in the form of instantiated modeling primitives within a so called Probabilistic Ontology Model (POM), we remain independent of a concrete target language while being able to translate the instantiated primitives into any (reasonably expressive) knowledge representation formalism. Second, user interaction is a core aspect of Text2Onto and the fact that the system calculates a confidence for each learned object allows to design sophisticated visualizations of the POM. Third, by incorporating strategies for data-driven change discovery, we avoid processing the whole corpus from scratch each time it changes, only selectively updating the POM according to the corpus changes instead. Besides increasing efficiency in this way, it also allows a user to trace the evolution of the ontology with respect to the changes in the underlying corpus.

The main claim of this paper is that machine learning can help integrate the construction of ontologies and extraction grammars and lead us closer to the Semantic Web vision. The proposed approach is a bootstrapping process that combines ontology and grammar learning, in order to semi-automate the knowledge acquisition process. After providing a survey of the most relevant work towards this goal, recent research of the Software and Knowledge Engineering Laboratory (SKEL) of NCSR “Demokritos” in the areas of Web information integration, information extraction, grammar induction and ontology enrichment is presented. The paper concludes with a number of interesting issues that need to be addressed in order to realize the advocated bootstrapping process.

A formal ontology does not contain lexical knowledge; it is by nature language-independent. Mappings can be added between the ontology and, arbitrarily, many lexica in any number of languages. The result of this operation is what is here referred to as a cross-language ontology. A cross-language ontology can be a useful resource for machine translation or cross-language information retrieval. This chapter focuses on ways of automatically building an ontology by exploiting cross-language information from parallel corpora. The goal is to improve the automatic learning results compared to learning an ontology from resources in a single language. The authors present a framework for cross-language ontology learning, providing a setting in which cross-language evidence (data) can be integrated and quantified. The aim is to investigate the following question: Can cross-language data teach us more than data from a single language for the ontology learning task?

Arabic ontology extraction model from unstructured text

Property Graph databases (PGs) are emerging as efficient graph stores with flexible schemata. This raises the need to have a unified view over heterogenous data produced from these stores. Ontology based Data Access (OBDA) has become the most dominant approach to integrate heterogeneous data sources by providing a unified conceptual view (ontology) over them. The corner stone of any OBDA system is to define mappings between the data source and the target (domain) ontology. However, manual mapping generation is time consuming and requires great efforts. This paper proposes ProGOMap ( Pro perty G raph to O ntology M apper) system that automatically generates mappings from property graphs to a domain ontology. ProGOMap starts by generating a putative ontology with direct axioms from PG. A novel ontology learning algorithm is proposed to enrich the putative ontology with subclass axioms inferred from PG. The putative ontology is then aligned to an existing domain ontology using string similarity metrics. Another algorithm is proposed to align object properties between the two ontologies considering different modelling criteria. Finally, mappings are generated from alignment results. Experiments were done on eight data sets with different scenarios to evaluate the effectiveness of the generated mappings. The experimental results achieved mapping accuracy up to 97% and 81% when addressing PG-to-ontology terminological and structural heterogeneities, respectively. Ontology learning by inferring subclass axioms from a property graph helps to address the heterogeneity between the PG and ontology models.

Situational Awareness (SAW) is a concept widely used in areas that require critical decision making, such as in the field of emergency management. SAW is related to the level of perception and understanding that an individual has about real events occurring in complex scenarios, which must be managed by critical systems. Such critical systems require specialized user interfaces (UI) to give operators a dynamic understanding of what is happening in the environment. A challenging issue in the design of SAW-oriented interfaces is to determine how the human-computer interface process can be constructed for SAW enrichment, considering environments with heterogeneous data sources, limitations in data quality, and ever-changing situations. The problem increases when information is subject to uncertainties, which may compromise the situation analysis process. In addition, humans make decisions based on their own understanding of the event, which allied with experience and knowledge can be valuable assets to be used to process situational information about emergencies for the acquisition of SAW. The objective of this work is to demonstrate how to include a SAI-oriented UI in the process of evaluating emergency situations and to present the development of a UI that promotes the management of situational information of emergencies to promote the acquisition of SAW. The results present a specified routine for employing specialized UIs in SAW as part of a situation assessment process, which supports a strong integration between the human operator and other phases of the process, such as quality assessment, data fusion and information visualization , As well as a prototype interface that meets the process. A case study with a critical scenario of a theft event is also presented to demonstrate the applicability of the proposed approach

With the increase of the number of independent database applications, Large organizations are eager to an effective solution of heterogeneous and distributed databases integration. Ontology is defined as an explicit formal specification of a shared conceptualization. It can provide semantics through relations between concepts and can be widely used on heterogeneous data. This paper mainly discusses two key techniques in ontology-based heterogeneous database integration system: mapping between database schema and creation of global ontology. In our approach, structured, semi-structured, and unstructured data sources are incorporated and provide users and applications with a uniform view. Our initial experiments have shown that the method is able to find the appropriate data often enough to be useful as an exploratory tool, while sheltering users from schema heterogeneity and automatically filling in some incomplete data.

Ontology learning has four main successive mining phases: term, concept, relation and axiom extraction. Since term extraction (TE) is the foundation of the ontology learning process, its inaccuracy will propagate in the following layers. Moreover, for any general-purpose ontology system some domain independent term extraction is needed. To achieve domain independent and precise TE in Farsi the present paper has presented a new hybrid system. It is based on the combination of a linguistic filter on Ezafe construction in Farsi and a statistical filter on C-value algorithm.