A Survey of Ontology Learning Approaches
---
paper_title: Reengineering Thesauri for New Applications: the AGROVOC Example

paper_content:

Existing classification schemes and thesauri are lacking in well-defined semantics and structural consistency. Empowering end users in searching collections of ever increasing magnitudes with performance far exceeding plain free-text searching (as used in many Web search engines), and developing systems that not only find but also process information for action, requires far more powerful and complex knowledge organization systems (KOSs). The paper presents a conceptual structure and transition procedure to support the shift from a traditional KOS towards a full-fledged and semantically rich KOS. The proposed structure also complies with other interoperability approaches like RDFS and XML in the Web environment. AGROVOC, a traditional thesaurus developed and maintained by the Food and Agriculture Organization (FAO) of the United Nations, serves as a case study for exploring the reengineering of a traditional thesaurus into a fully-fledged ontology. We start the process of developing an inventory of specific relationship types with well-defined semantics for the agricultural domain and explore the rules-as-you-go approach to streamlining the reengineering process.
---
paper_title: WordNet: An Electronic Lexical Database

paper_content:

A teaching device to acquaint dental students and also patients with endodontic root canal techniques performed by dentists and utilizing an electronic oscillator having a scale reading in electric current measurement and a pair of electrical circuit conductors being connected at one end to the terminals of the oscillator and the opposite ends thereof respectively being connectable to one or more small diameter metal wires which simulate dental reamers and files which are movable in root canal-simulating passages of uniform diameter complementary to that of said wires and formed in a transparent model of a human tooth including a root and cusp thereon and mounted in a transparent enclosure in which the root portion of the tooth extends with the cusp of the model extending above the upper end of the enclosure.
---
paper_title: The state of the art in ontology learning: a framework for comparison

paper_content:

In recent years there have been some efforts to automate the ontology acquisition and construction process. The proposed systems differ from each other in some factors and have many features in common. This paper presents the state of the art in Ontology Learning (OL) and introduces a framework for classifying and comparing OL systems. The dimensions of the framework concern what to learn, from where to learn it and how it may be learnt. They include features of the input, the methods of learning and knowledge acquisition, the elements learned, the resulting ontology and also the evaluation process. To extract this framework, over 50 OL systems or modules thereof that have been described in recent articles are studied here and seven prominent ones, which illustrate the greatest differences, are selected for analysis according to our framework. In this paper after a brief description of the seven selected systems we describe the dimensions of the framework. Then we place the representative ontology learning systems into our framework. Finally, we describe the differences, strengths and weaknesses of various values for our dimensions in order to present a guideline for researchers to choose the appropriate features to create or use an OL system for their own domain or application.
---
paper_title: OntoEdit: Collaborative Ontology Development For The Semantic Web

paper_content:

Ontologies now play an important role for enabling the semantic web. They provide a source of precisely defined terms e.g. for knowledge-intensive applications. The terms are used for concise communication across people and applications. Typically the development of ontologies involves collaborative efforts of multiple persons. OntoEdit is an ontology editor that integrates numerous aspects of ontology engineering. This paper focuses on collaborative development of ontologies with OntoEdit which is guided by a comprehensive methodology.
---
paper_title: The state of the art in ontology learning: a framework for comparison

paper_content:

In recent years there have been some efforts to automate the ontology acquisition and construction process. The proposed systems differ from each other in some factors and have many features in common. This paper presents the state of the art in Ontology Learning (OL) and introduces a framework for classifying and comparing OL systems. The dimensions of the framework concern what to learn, from where to learn it and how it may be learnt. They include features of the input, the methods of learning and knowledge acquisition, the elements learned, the resulting ontology and also the evaluation process. To extract this framework, over 50 OL systems or modules thereof that have been described in recent articles are studied here and seven prominent ones, which illustrate the greatest differences, are selected for analysis according to our framework. In this paper after a brief description of the seven selected systems we describe the dimensions of the framework. Then we place the representative ontology learning systems into our framework. Finally, we describe the differences, strengths and weaknesses of various values for our dimensions in order to present a guideline for researchers to choose the appropriate features to create or use an OL system for their own domain or application.
---
paper_title: Learning domain ontologies for Web service descriptions: an experiment in bioinformatics

paper_content:

The reasoning tasks that can be performed with semantic web service descriptions depend on the quality of the domain ontologies used to create these descriptions. However, building such domain ontologies is a time consuming and difficult task.We describe an automatic extraction method that learns domain ontologies for web service descriptions from textual documentations attached to web services. We conducted our experiments in the field of bioinformatics by learning an ontology from the documentation of the web services used in myGrid, a project that supports biology experiments on the Grid. Based on the evaluation of the extracted ontology in the context of the project, we conclude that the proposed extraction method is a helpful tool to support the process of building domain ontologies for web service descriptions.
---
paper_title: Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis

paper_content:

We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris' distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.
---
paper_title: Learning domain ontologies for Web service descriptions: an experiment in bioinformatics

paper_content:

The reasoning tasks that can be performed with semantic web service descriptions depend on the quality of the domain ontologies used to create these descriptions. However, building such domain ontologies is a time consuming and difficult task.We describe an automatic extraction method that learns domain ontologies for web service descriptions from textual documentations attached to web services. We conducted our experiments in the field of bioinformatics by learning an ontology from the documentation of the web services used in myGrid, a project that supports biology experiments on the Grid. Based on the evaluation of the extracted ontology in the context of the project, we conclude that the proposed extraction method is a helpful tool to support the process of building domain ontologies for web service descriptions.
---
paper_title: Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis

paper_content:

We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris' distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.
---
paper_title: Learning domain ontologies for Web service descriptions: an experiment in bioinformatics

paper_content:

The reasoning tasks that can be performed with semantic web service descriptions depend on the quality of the domain ontologies used to create these descriptions. However, building such domain ontologies is a time consuming and difficult task.We describe an automatic extraction method that learns domain ontologies for web service descriptions from textual documentations attached to web services. We conducted our experiments in the field of bioinformatics by learning an ontology from the documentation of the web services used in myGrid, a project that supports biology experiments on the Grid. Based on the evaluation of the extracted ontology in the context of the project, we conclude that the proposed extraction method is a helpful tool to support the process of building domain ontologies for web service descriptions.
---
paper_title: OntoMiner: bootstrapping ontologies from overlapping domain specific web sites

paper_content:

In this paper, we present automated techniques for bootstrapping and populating specialized domain ontologies by organizing and mining a set of relevant overlapping Web sites provided by the user. We develop algorithms that detect and utilize HTML regularities in the Web documents to turn them into hierarchical semantic structures encoded as XML. Next, we present tree-mining algorithms that identify key domain concepts and their taxonomical relationships. We also extract semi-structured concept instances annotated with their labels whenever they are available. Experimental evaluation for the News, Travel, and Shopping domains indicates that our algorithms can bootstrap and populate domain specific ontologies with high precision and recall.
---
paper_title: Ontology learning from domain specific web documents

paper_content:

Ontologies play a vital role in many web- and internet-related applications. This work presents a system for accelerating the ontology building process via semi-automatically learning a hierarchal ontology given a set of domain-specific web documents and a set of seed concepts. The methods are tested with web documents in the domain of agriculture. The ontology is constructed through the use of two complementary approaches. The presented system has been used to build an ontology in the agricultural domain using a set of Arabic extension documents and evaluated against a modified version of the AGROVOC ontology.
---
paper_title: OntoMiner: bootstrapping ontologies from overlapping domain specific web sites

paper_content:

In this paper, we present automated techniques for bootstrapping and populating specialized domain ontologies by organizing and mining a set of relevant overlapping Web sites provided by the user. We develop algorithms that detect and utilize HTML regularities in the Web documents to turn them into hierarchical semantic structures encoded as XML. Next, we present tree-mining algorithms that identify key domain concepts and their taxonomical relationships. We also extract semi-structured concept instances annotated with their labels whenever they are available. Experimental evaluation for the News, Travel, and Shopping domains indicates that our algorithms can bootstrap and populate domain specific ontologies with high precision and recall.
---
paper_title: Ontology learning from domain specific web documents

paper_content:

Ontologies play a vital role in many web- and internet-related applications. This work presents a system for accelerating the ontology building process via semi-automatically learning a hierarchal ontology given a set of domain-specific web documents and a set of seed concepts. The methods are tested with web documents in the domain of agriculture. The ontology is constructed through the use of two complementary approaches. The presented system has been used to build an ontology in the agricultural domain using a set of Arabic extension documents and evaluated against a modified version of the AGROVOC ontology.
---
paper_title: Ontology Selection: Ontology Evaluation on the Real Semantic Web

paper_content:

The increasing number of ontologies on the Web and the appearance of large scale ontology repositories has brought the topic of ontology selection in the focus of the semantic web research agenda. Our view is that ontology evaluation is core to ontology selection and that, because ontology selection is performed in an open Web environment, it brings new challenges to ontology evaluation. Unfortunately, current research regards ontology selection and evaluation as two separate topics. Our goal in this paper is to explore how these two tasks relate. In particular, we are interested to get a better understanding of the ontology selection task and filter out the challenges that it brings to ontology evaluation. We discuss requirements posed by the open Web environment on ontology selection, we overview existing work on selection and point out future directions. Our major conclusion is that, even if selection methods still need further development, they have already brought novel approaches to ontology evaluation.
---
paper_title: OntoMiner: bootstrapping ontologies from overlapping domain specific web sites

paper_content:

In this paper, we present automated techniques for bootstrapping and populating specialized domain ontologies by organizing and mining a set of relevant overlapping Web sites provided by the user. We develop algorithms that detect and utilize HTML regularities in the Web documents to turn them into hierarchical semantic structures encoded as XML. Next, we present tree-mining algorithms that identify key domain concepts and their taxonomical relationships. We also extract semi-structured concept instances annotated with their labels whenever they are available. Experimental evaluation for the News, Travel, and Shopping domains indicates that our algorithms can bootstrap and populate domain specific ontologies with high precision and recall.
---
paper_title: On How to Perform a Gold Standard Based Evaluation of Ontology Learning

paper_content:

In recent years several measures for the gold standard based evaluation of ontology learning were proposed. They can be distinguished by the layers of an ontology (e.g. lexical term layer and concept hierarchy) they evaluate. Judging those measures with a list of criteria we show that there exist some measures sufficient for evaluating the lexical term layer. However, existing measures for the evaluation of concept hierarchies fail to meet basic criteria. This paper presents a new taxonomic measure which overcomes the problems of current approaches.
---
paper_title: OntoMiner: Bootstrapping and populating ontologies from domain specific Web sites

paper_content:

Key to the Semantic Web idea are ontologies that can transform legacy HTML documents into Semantic Web documents and encode domain knowledge to facilitate automated reasoning. The techniques presented in the paper can help bootstrap and populate specialized domain ontologies.
---
paper_title: Ontology learning from domain specific web documents

paper_content:

Ontologies play a vital role in many web- and internet-related applications. This work presents a system for accelerating the ontology building process via semi-automatically learning a hierarchal ontology given a set of domain-specific web documents and a set of seed concepts. The methods are tested with web documents in the domain of agriculture. The ontology is constructed through the use of two complementary approaches. The presented system has been used to build an ontology in the agricultural domain using a set of Arabic extension documents and evaluated against a modified version of the AGROVOC ontology.
---
paper_title: Learning domain ontologies for Web service descriptions: an experiment in bioinformatics

paper_content:

The reasoning tasks that can be performed with semantic web service descriptions depend on the quality of the domain ontologies used to create these descriptions. However, building such domain ontologies is a time consuming and difficult task.We describe an automatic extraction method that learns domain ontologies for web service descriptions from textual documentations attached to web services. We conducted our experiments in the field of bioinformatics by learning an ontology from the documentation of the web services used in myGrid, a project that supports biology experiments on the Grid. Based on the evaluation of the extracted ontology in the context of the project, we conclude that the proposed extraction method is a helpful tool to support the process of building domain ontologies for web service descriptions.
---

Title: A Survey of Ontology Learning Approaches

Section 1: INTRODUCTION
Description 1: This section provides an introduction to the research, background of ontology, its importance, and the problem statement.

Section 2: ONTOLOGY LEARNING APPROACHES
Description 2: This section describes the disadvantages of manual ontology acquisition and highlights various semi-automatic and automatic methods for constructing ontologies. It discusses the use of different information sources and methods from fields like machine learning, natural-language processing, and information retrieval.

Section 3: LEARNING FROM UNSTRUCTURED DATA
Description 3: This section focuses on methods and systems proposed for learning ontologies from unstructured data like books and journals, incorporating different approaches such as statistical, natural language processing, and integrated methods.

Section 4: LEARNING FROM SEMI-STRUCTURED DATA
Description 4: This section addresses ontology building from semi-structured data such as HTML and XML files, discussing the use of traditional data mining and web content mining techniques.

Section 5: EVALUATION METHODS
Description 5: This section introduces various methods for evaluating the ontologies built automatically or semi-automatically, categorizing them into gold standard evaluation, application-based evaluation, data-driven evaluation, and human evaluation.

Section 6: CONCLUSIONS
Description 6: This final section offers concluding remarks, summarizing the key points discussed and the future directions for research in ontology learning approaches.