Learning non-taxonomic relationships from web documents for domain ontology construction

In recent years, much effort has been put in ontology learning. However, the knowledge acquisition process is typically focused in the taxonomic aspect. The discovery of non-taxonomic relationships is often neglected, even though it is a fundamental point in structuring domain knowledge. This paper presents an automatic and unsupervised methodology that addresses the non-taxonomic learning process for constructing domain ontologies. It is able to discover domain-related verbs, extract non-taxonomically related concepts and label relationships, using the Web as corpus. The paper also discusses how the obtained relationships can be automatically evaluated against WordNet and presents encouraging results for several domains.     

Detection and resolution of semantic inconsistency and redundancy in an automatic ontology merging system

In recent years, researchers have been developing algorithms for the automatic mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. We are detecting semantic heterogeneities that occur due to conflicts among the set of Generalized Concept Inclusions, Property Subsumption Criteria, and Constraint Satisfaction Mechanism in local heterogeneous ontologies, which become obstacles for the generation of semantically consistent global merged ontology. We present several algorithms to detect such semantic inconsistencies based on subsumption analysis of concepts and properties in local ontologies from the list of initial mappings. We provide ontological patterns for resolving these inconsistencies automatically. This results global merged ontology free from ??circulatory error in class/property hierarchy??, ??common class between disjoint classes/properties??, ??redundancy of subclass/subproperty of relations?? and other types of ??semantic inconsistency?? errors. Experiments on the real ontologies show that our algorithms save time and cost of traversing local ontologies, improve system??s performance by producing only consistent accurate mappings, and reduce the users?? dependability for ensuring the satisfiability of merged ontology.     

OWL-FC: an upper ontology for semantic modeling of Fuzzy Control

This work introduces an OWL-based upper ontology, called OWL-FC (Ontology Web Language for Fuzzy Control), capable to support a semantic definition of Fuzzy Control. It focuses on the fuzzy rules representation by providing domain independent ontology, supporting interoperability and favoring domain ontologies re-usability. The main contribution is that OWL-FC exploits Fuzzy Logic in OWL to model vagueness and uncertainty of the real world. Moreover, OWL-FC enables automatic discovery and execution of fuzzy controllers, by means of context aware parameter setting: appropriate controllers can be activated, depending on the parameters proactively identified in the work environment. In fact, the semantic modeling of concepts allows the characterization of constraints and restrictions for the identification of the right matches between concepts and individuals. OWL-FC ontology provides a wide, semantic-based interoperability among different domain ontologies, through the specification of fuzzy concepts, independently by the application domain. Then, OWL-FC is coherent to the Semantic Web infrastructure and avoids inconsistencies in the ontology.     

Towards an automatic extraction of synonyms for Quranic Arabic WordNet

In this paper, we developed an automatic extraction model of synonyms, which is used to construct our Quranic Arabic WordNet (QAWN) that depends on traditional Arabic dictionaries. In this work, we rely on three resources. First, the Boundary Annotated Quran Corpus that contains Quran words, Part-of-Speech, root and other related information. Second, the lexicon resources that was used to collect a set of derived words for Quranic words. Third, traditional Arabic dictionaries, which were used to extract the meaning of words with distinction of different senses. The objective of this work is to link the Quranic words of similar meanings in order to generate synonym sets (synsets). To accomplish that, we used term frequency and inverse document frequency in vector space model, and we then computed cosine similarities between Quranic words based on textual definitions that are extracted from traditional Arabic dictionaries. Words of highest similarity were grouped together to form a synset. Our QAWN consists of 6918 synsets that were constructed from about 8400 unique word senses, on average of 5 senses for each word. Based on our experimental evaluation, the average recall of the baseline system was 7.01 %, whereas the average recall of the QAWN was 34.13 % which improved the recall of semantic search for Quran concepts by 27 %.     

Semantic indexing of Arabic texts for information retrieval system

As part of information retrieval systems (IRS) and in the context of the use of ontologies for documents and queries indexing, we propose and evaluate in this paper the contribution of this approach applied to Arabic texts. To do this we indexed a corpus of Arabic text using Arabic WordNet. The disambiguation of words was performed by applying the Lesk algorithm. The results obtained by our experiment allowed us to deduct the contribution of this approach in IRS for Arabic texts.     

Learning from syntax generalizations for automatic semantic annotation

Nowadays, there is a huge amount of textual data coming from on-line social communities like Twitter or encyclopedic data provided by Wikipedia and similar platforms. This Big Data Era created novel challenges to be faced in order to make sense of large data storages as well as to efficiently find specific information within them. In a more domain-specific scenario like the management of legal documents, the extraction of semantic knowledge can support domain engineers to find relevant information in more rapid ways, and to provide assistance within the process of constructing application-based legal ontologies. In this work, we face the problem of automatically extracting structured knowledge to improve semantic search and ontology creation on textual databases. To achieve this goal, we propose an approach that first relies on well-known Natural Language Processing techniques like Part-Of-Speech tagging and Syntactic Parsing. Then, we transform these information into generalized features that aim at capturing the surrounding linguistic variability of the target semantic units. These new featured data are finally fed into a Support Vector Machine classifier that computes a model to automate the semantic annotation. We first tested our technique on the problem of automatically extracting semantic entities and involved objects within legal texts. Then, we focus on the identification of hypernym relations and definitional sentences, demonstrating the validity of the approach on different tasks and domains.     

Semantic conflict resolution ontology (SCROL): an ontology for detecting and resolving data and schema-level semantic conflicts

Establishing semantic interoperability among heterogeneous information sources has been a critical issue in the database community for the past two decades. Despite the critical importance, current approaches to semantic interoperability of heterogeneous databases have not been sufficiently effective. We propose a common ontology called semantic conflict resolution ontology (SCROL) that addresses the inherent difficulties in the conventional approaches, i.e., federated schema and domain ontology approaches. SCROL provides a systematic method for automatically detecting and resolving various semantic conflicts in heterogeneous databases. SCROL provides a dynamic mechanism of comparing and manipulating contextual knowledge of each information source, which is useful in achieving semantic interoperability among heterogeneous databases. We show how SCROL is used for detecting and resolving semantic conflicts between semantically equivalent schema and data elements. In addition, we present evaluation results to show that SCROL can be successfully used to automate the process of identifying and resolving semantic conflicts.     

Deep semantic mining of big multimedia data advertisements based on needs ontology construction

Multimedia Tools and Applications - Constructing an ontology of human needs is to enable computers to perform deep semantic mining so that valuable information can be extracted from social media....     

从Web中提取中文本体非分类关系的方法

为了有效地学习本体中的非分类关系以协助知识工程师构建领域本体,提出了一种在中文领域本体学习环境中自动获取概念之间非分类关系的方法,该方法以Web为数据源来提取候选关系并计算信息分布的统计特征,把动词作为发现非分类关系的中心点,把领域相关的动词作为种子来检索领域相关概念并用来标记相应的关系.该方法的学习结果是一个多级分类关系和非分类关系组成的语义体系.最后,通过对"癌"本体相应关系的提取及其性能分析,表明了该方法的学习结果和性能.     

A methodology for constructing of philosophy ontology based on philosophical texts

An ontology is a crucial factor for the success of the Semantic Web and other knowledge-based systems in terms of share and reuse of domain knowledge. However, there are a few concrete ontologies within actual knowledge domains including learning domains. In this paper, we develop an ontology which is an explicit formal specification of concepts and semantic relations among them in philosophy. We call it a philosophy ontology. Our philosophy is a formal specification of philosophical knowledge including knowledge of contents of classical texts of philosophy. We propose a methodology, which consists of detailed guidelines and templates, for constructing text-based ontology. Our methodology consists of 3 major steps and 14 minor steps. To implement the philosophy ontology, we develop an ontology management system based on Topic Maps. Our system includes a semi-automatic translator for creating Topic Map documents from the output of conceptualization steps and other tools to construct, store, retrieve ontologies based on Topic Maps. Our methodology and tools can be applied to other learning domain ontologies, such as history, literature, arts, and music.     

Content annotation for the semantic web: an automatic web-based approach

Semantic Annotation is required to add machine-readable content to natural language text. A global initiative such as the Semantic Web directly depends on the annotation of massive amounts of textual Web resources. However, considering the amount of those resources, a manual semantic annotation of their contents is neither feasible nor scalable. In this paper we introduce a methodology to partially annotate textual content of Web resources in an automatic and unsupervised way. It uses several well-established learning techniques and heuristics to discover relevant entities in text and to associate them to classes of an input ontology by means of linguistic patterns. It also relies on the Web information distribution to assess the degree of semantic co-relation between entities and classes of the input domain ontology. Special efforts have been put in minimizing the amount of Web accesses required to evaluate entities in order to ensure the scalability of the approach. A manual evaluation has been carried out to test the methodology for several domains showing promising results.     

Measuring semantic similarity between land-cover classes for spatial analysis: an ontology hierarchy exploration approach

Resolving semantic heterogeneity is one of the major research challenges involved in many fields of study, such as, natural language processing, search engine development, document clustering, geospatial information retrieval, knowledge discovery, etc. When semantic heterogeneity is often considered as an obstacle for realizing full interoperability among diverse datasets, proper quantification of semantic similarity is another challenge to measure the extent of association between two qualitative concepts. The proposed work addresses this issue for any geospatial application where spatial land-cover distribution is crucial to model. Most of the these applications such as: prediction, change detection, land-cover classification, etc. often require to examine the land-cover distribution of the terrain. This paper presents an ontology-based approach to measure semantic similarity between spatial land-cover classes. As land-cover distribution is a qualitative information of a terrain, it is challenging to measure their extent of similarity among each other pragmatically. Here, an ontology is considered as the concept hierarchy of different land-cover classes which is built using domain experts’ knowledge. This work can be considered as the spatial extension of our earlier work presented in [1]. The similarity metric proposed in [1] is utilized here for spatial concepts. A case study with real land-cover ontology is presented to quantify the semantic similarity between every pair of land-covers with semantic hierarchy based similarity measurement (SHSM) scheme [1]. This work may facilitate quantification of semantic knowledge of the terrain for other spatial analyses as well.     

基于本体的语义标引研究与实现

标引是资源管理与检索的基础.传统的标引方式仅停留在关键字异同的逻辑层面,忽略了文档语义层面上的信息.以本体的知识组织体系为基础,以抽取文档的语义向量为目标,提出了基于本体的语义标引思想,为基于概念匹配的语义检索创造条件.为了更清晰的描述标引过程,建立了基于本体的语义标引模型,并对模型中各环节进行详细的功能定义.参照具体的实例本体进行实验和分析.     

Extraction of newspaper headlines from microfilm for automatic indexing

This paper proposes a document image analysis system that extracts newspaper headlines from microfilm images with a view to providing automatic indexing for news articles in microfilm. A major challenge in achieving this is the poor image quality of microfilm as most images are usually inadequately illuminated and considerably dirty. To overcome the problem we propose a new effective method for separating characters from noisy background since conventional threshold selection techniques are inadequate to deal with this kind of image. A run length smoothing algorithm is then applied to the headline extraction. Experimental results confirm the validity of the approach.Received: 15 November 2002, Accepted: 19 May 2003, Published online: 30 January 2004Correspondence to: Chew Lim Tan     

An ontology for causal relationships between news and financial instruments

In recent years, people have begun to pay more and more attention to the effect of news on financial instrument markets (i.e., the markets for trading financial instruments). Researchers in the financial domain have conducted many studies demonstrating the effect of different types of news on trade activities in financial instrument markets such as volatility in trade price, trade volume, trading frequency, and so on. In this paper, an ontology for knowledge about news regarding financial instruments is provided. The ontology contains two parts: the first part presents a hierarchy framework for the domain knowledge that primarily includes classes of news, classes of financial instrument markets participants, classes of financial instruments, and primary relations between these classes. In the second part, a causal map is used to demonstrate how classes of news are causally related with classes of financial instruments. Finally, a case concerning the “9/11 American terror attack” is analyzed. On the basis of the ontology, it is first comprehensive to understand the knowledge about news in financial instrument markets; second, it helps building trading models based on news in the financial instrument markets; third, systems (e.g., systems for prediction of stock price based on news, systems for supporting financial market participants to search relevant news) design and development in this domain are facilitated and supported by this ontology.     

Semi-automatic construction of domain ontology for agent reasoning

One of the key elements of the Semantic Web technologies is domain ontologies and those ontologies are important constructs for multi-agent system. The Semantic Web relies on domain ontologies that structure underlying data enabling comprehensive and transportable machine understanding. It takes so much time and efforts to construct domain ontologies because these ontologies can be manually made by domain experts and knowledge engineers. To solve these problems, there have been many researches to semi-automatically construct ontologies. Most of the researches focused on relation extraction part but manually selected terms for ontologies. These researches have some problems. In this paper, we propose a hybrid method to extract relations from domain documents which combines a named relation approach and an unnamed relation approach. Our named relation approach is based on the Hearst’s pattern and the Snowball system. We merge a generalized pattern scheme into their methods. In our unnamed relation approach, we extract unnamed relations using association rules and clustering method. Moreover, we recommend candidate relation names of unnamed relations. We evaluate our proposed method by using Ziff document set offered by TREC.     

A framework for modeling and evaluating automatic semantic reconciliation

The introduction of the Semantic Web vision and the shift toward machine understandable Web resources has unearthed the importance of automatic semantic reconciliation. Consequently, new tools for automating the process were proposed. In this work we present a formal model of semantic reconciliation and analyze in a systematic manner the properties of the process outcome, primarily the inherent uncertainty of the matching process and how it reflects on the resulting mappings. An important feature of this research is the identification and analysis of factors that impact the effectiveness of algorithms for automatic semantic reconciliation, leading, it is hoped, to the design of better algorithms by reducing the uncertainty of existing algorithms. Against this background we empirically study the aptitude of two algorithms to correctly match concepts. This research is both timely and practical in light of recent attempts to develop and utilize methods for automatic semantic reconciliation.Received: 6 December 2002, Accepted: 15 September 2003, Published online: 19 December 2003Edited by: V. Atluri.