Learning Context-Sensitive Domain Ontologies from Folksonomies: A Cognitively Motivated Method

Ontology learning from text is one of the knowledge acquisition processes that facilitates construction of ontology. Considerable research is being done on learning concepts and relations, especially on acquiring semantic relations between concepts for a specific domain. However, more of the research contributions are in learning either taxonomic relations or semantic relations but not in both. Even those few research works that address learning of both types of relations deal with simple sentences only resulting in low recall value. Further, these approaches are semi-automatic, which require either user's feedback or domain expert's knowledge. In this paper, we propose a single framework that is automatic and domain-independent that helps in learning both taxonomic and non-taxonomic relations. We have developed a clause-based approach that automatically extracts the relations for concepts from unstructured text documents. Our approach is capable of handling complex sentences by identifying hidden triples present in the sentences. We have evaluated our methodology of relation learning for the concepts specified by AGROVOC and Open Directory Project using a corpus of web documents. The precision, recall and F1-measure of our method were observed to be considerably higher than those of state-of-the-art methodologies for relation learning.

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.

Ontology learning tries to find ontological relations, by an automatic process. Similarity relationships are one of non-taxonomic relations which may be included in ontology. Our idea is that in presence of taxonomic relations we are able to extract more useful non-taxonomic similarity relations. In this paper we investigate the specifications of an implemented system for extracting these relations by means of new context extraction method which uses taxonomic relations

Ontology learning from texts has recently been proposed as a new technology helping ontology designers in the modelling process. Discovery of non–taxonomic relations is understood as the least tackled problem therein. We propose a technique for extraction of lexical entries that may give cue in assigning semantic labels to otherwise ‘anonymous’ relations. The technique has been implemented as extension to the existing Text-to-Onto tool, and tested on a collection of texts describing worldwide geographic locations from a tour–planning viewpoint.

Since the boom of biomedical big data studies, various big data processing technologies have been developed rapidly. As an important form of knowledge representation, ontology has become an important means for the utilization and integration of biomedical big data. The emergence of new technologies for ontology development has resulted in the generation of many biomedical ontologies by many ontology development communities. The Open Biological and Biomedical Ontology Foundry, an academic organization for bio-ontology developers, has provided a set of principles to guide community-based open ontology construction. The Open Biological and Biomedical Ontology Foundry have also built many widely used ontologies, such as Gene Ontology, Human Phenotype Ontology, and Chemical Entities of Biological Interest. Other various ontology repositories have also been created and used to support ontology reuse. Many efficient tools for ontology applications, such as data annotation and terms mapping, have also been developed. High quality ontologies are also being used to develop new methods and tools for biomedical data analysis. The applications of Gene Ontology and Human Phenotype Ontology for data analysis and integration in recent years are reviewed here. To promote the development and applications of biomedical ontologies in China, a research community, OntoChina, was founded recently. OntoChina aims to support the development of reference ontologies, especially bilingual and Chinese translated ontologies. OntoChina also encourages ontology developers to follow the Open Biological and Biomedical Ontology Foundry principles.

In this paper we explore some of the opportunities and challenges for machine learning on the Semantic Web. The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. Semantic Web standards are used to describe meta data but also have great potential as a general data format for data communication and data integration. Within a broad range of possible applications machine learning will play an increasingly important role: Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of unstructured data, and to integrate semantic information into web mining. Machine learning will increasingly be employed to analyze distributed data sources described in Semantic Web formats and to support approximate Semantic Web reasoning and querying. In this paper we discuss existing and future applications of machine learning on the Semantic Web with a strong focus on learning algorithms that are suitable for the relational character of the Semantic Web's data structure. We discuss some of the particular aspects of learning that we expect will be of relevance for the Semantic Web such as scalability, missing and contradicting data, and the potential to integrate ontological background knowledge. In addition we review some of the work on the learning of ontologies and on the population of ontologies, mostly in the context of textual data.

The essence of all kinds of ontology applications is similarity measuring. In recent years, a variety of learning approaches are introduced to ontology similarity computation and ontology mapping. The purpose of these ontology learning technologies is to get an ontology score function which maps each vertex to a real number, and then the similarity between ontology vertices are judged according to the difference of their scores. However, such learning data should containing all pairs of sample vertices. In this paper, we raise a new ontology learning algorithm in which its training sample set only contains important edges (vertex pair) of which the similarities are to be determined by us here. Our method is based on the Kronecker kernel technologies and the solution is attributed to solving the linear system. Two simulation experiments reveal that our new ontology learning model has higher precision ratio on plant ontology and humanoid robotics ontology for similarity measuring and ontology mapping applications.

The authors propose probabilistic models for learning ontologies that expand existing ontologies taking into account both corpus-extracted evidence and the structure of the generated ontologies. The model exploits structural properties of target relations such as transitivity during learning. They then propose two extensions of the probabilistic models: a model for learning from a generic domain that can be exploited to extract new information in a specific domain and an incremental ontology learning system that puts human validations in the learning loop. This latter provides a graphical user interface and a human-computer interaction workflow supporting the incremental leaning loop.

The lack of formally expressed semantics in web services complemented with the increasing number of available web services is the main obstacle in analyzing and using the existing web services exposed in the Web. In the absence of appropriate reference domain ontology, annotation of existing web services is dependent on ontology development and ontology learning techniques. In this paper we present an unsupervised ontology learning approach tailored to learning from WSDL documents. The most specific feature of the suggested approach is that it constructs (semi-) automatically ontology fragments from a collection of WSDL documents, that lack any extra textual documentation, by just exploiting element names in the WSDL document. The suggested approach combines both linguistic and statistic analysis techniques such as lexico-syntactic patterns and term co-occurrence analysis. The preliminary results show that the generated ontology captures correctly more than half of the semantic classes and instances as well as taxonomic and non-taxonomic relations, hence, providing a reasonable basis for automatic web services annotation.

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.

With the advent of the Semantic Web, the field of domain ontology engineering has gained more and more importance. This innovative field may have a big impact on computer-based education and will certainly contribute to its development. This chapter presents a survey on domain ontology engineering and especially domain ontology learning. The chapter focuses particularly on automatic methods for ontology learning. It summarizes the state of the art in natural language processing techniques and statistical and machine learning techniques for ontology extraction. It also explains how intelligent tutoring systems may benefit from this engineering and talks about the challenges that face the field.

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.

Since the inception of the Web 2.0, World Wide Web is widely being used as a platform by customers and manufactures to share experiences and opinions regarding products, services, marketing campaigns, social events, etc. As a result, there is enormous growth in user-generated contents (e.g. customer reviews), providing an opportunity for data analysts to computationally evaluate users’ sentiments and emotions for developing real-life applications for business intelligence, product recommendation, enhanced customer services, and target marketing. Since users’ feedbaks (aka reviews) are very useful for products development and marketing, large business houses and corporates are taking interest in opinion mining and sentiment analysis systems. In this chapter, we propose the design of an Ontology Learning and Sentiment Analysis (OntoLSA) system for ontology learning and sentiment analysis using rule-based and machine learning approaches. The rule-based approach aims to identify candidate concepts, which are analyzed using a customized HITS algorithm to compile a list of feasible concepts. Feasible concepts and their relationships (both structural and non-structural) are used to generate a domain ontology, which is later on used for opinion mining and sentiment analysis . The proposed system is also integrated with a visualization module to facilitate users to navigate through review documents at different levels of granularity using a graphical user interface.

This chapter introduces several fundamental models and algorithms for probabilistic clustering, including mixture models, Expectation-Maximization (EM) algorithm, and probabilistic topic models. It focuses on general framework of modeling, the probabilistic explanation, the standard algorithms to learn the model, and its applications. Mixture models are probabilistic models which are increasingly used to find the clusters for univariate and multivariate data. The chapter begins the discussion of mixture models, in which the values of the discrete latent variables can be interpreted as the assignments of data points to specific components of the mixture. It uses the Gaussian mixture model to motivate the EM algorithm in an informal way, and then gives a more general view of the EM algorithm, which is a standard learning algorithm for many probabilistic models. The chapter presents two popular probabilistic topic models, i.e., probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), for document clustering and analysis.

The conceptual structure of the ontology is usually represented by a graph, and the related information of this concept is encapsulated by a vector with uniform dimension. The essence of the similarity calculation of the ontology concept is the calculation of the distance of the vector corresponding to the vertex in the high-dimensional space. This paper continues to consider the ontology learning algorithm of the multi-dividing setting, and proposes a MB based learning strategy under this framework. The experimental data verifies the effectiveness of the given new algorithm.