A Knowledge-Based Approach to Ontologies Data Integration (original) (raw)
AQUA–ontology-based question answering system
2004
This paper describes AQUA, an experimental question answering system. AQUA combines Natural Language Processing (NLP), Ontologies, Logic, and Information Retrieval technologies in a uniform framework. AQUA makes intensive use of an ontology in several parts of the question answering system. The ontology is used in the refinement of the initial query, the reasoning process, and in the novel similarity algorithm. The similarity algorithm, is a key feature of AQUA.
A tool for knowledge base integration and querying
AAAI Spring Symposium
Introduction In recent years, we have witnessed the development of sev-eral efforts to incorporate semantics and knowledge-based reasoning in question and answering (QA) systems (eg, (Harabagiu 2001; Vicedo 2000; Pasca 2000)). This is nec-essary as queries, eg, concerning actions, ...
AquaLog: An ontology-driven question answering system to interface the semantic web
2006
Abstract The semantic web (SW) vision is one in which rich, ontology-based semantic markup will become widely available. The availability of semantic markup on the web opens the way to novel, sophisticated forms of question answering. AquaLog is a portable question-answering system which takes queries expressed in natural language (NL) and an ontology as input, and returns answers drawn from one or more knowledge bases (KB).
A Generic Platform for Ontological Query Answering
2012
Abstract The paper presents ALASKA, a multi-layered platform enabling to perform ontological conjunctive query answering (OCQA) over heterogeneously-stored knowledge bases in a generic, logic-based manner. While this problem knows today a renewed interest in knowledge-based systems with the semantic equivalence of different languages widely studied, from a practical view point this equivalence has been not made explicit.
A Framework for Ontology Integration
The Emerging Semantic …, 2002
One of the basic problems in the development of techniques for the semantic web is the integration of ontologies. Indeed, the web is constituted by a variety of information sources, each expressed over a certain ontology, and in order to extract information from such sources, their semantic integration and reconciliation in terms of a global ontology is required. In this paper, we address the fundamental problem of how to specify the mapping between the global ontology and the local ontologies. We argue that for capturing such mapping in an appropriate way, the notion of query is a crucial one, since it is very likely that a concept in one ontology corresponds to a view (i.e., a query) over the other ontologies. As a result query processing in ontology integration systems is strongly related to view-based query answering in data integration.
Ontology-Driven Question Answering in AquaLog
2004
The semantic web vision is one in which rich, ontology-based semantic markup is widely available, both to enable sophisticated interoperability among agents and to support human web users in locating and making sense of information. The availability of semantic markup on the web also opens the way to novel, sophisticated forms of question answering. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input and returns answers drawn from one or more knowledge bases (KBs), which instantiate the input ontology with domain-specific information. AquaLog makes use of the GATE NLP platform, string metrics algorithms, WordNet and a novel ontology-based relation similarity service to make sense of user queries with respect to the target knowledge base. Finally, although AquaLog has primarily been designed for use with semantic web languages, it makes use of a generic plug-in mechanism, which means it can be easily interfaced to different ontology servers and knowledge representation platforms.
Ontology Based Information Integration : A Survey 1
2019
An ontology makes a special vocabulary which describes the domain of interest and the meaning of the term on that vocabulary. Based on the precision of the specification, the concept of the ontology contains several data and conceptual models. The notion of ontology has emerged into wide ranges of applications including database integration, peer-to-peer systems, e-commerce, semantic web, etc. It can be considered as a practical tool for conceptualizing things which are expressed in computer format. This paper is devoted to ontology matching as a mean for information integration. Several matching solutions have been presented from various areas such as databases, information systems and artificial intelligence. All of them take advantages of different attributes of ontology like, structures, data instances, semantics and labels and its other valuable properties. The solutions have some common techniques and cope with similar problems, but use different methods for combining and expl...
Ontology Mapping with domain specific agents in the AQUA Question Answering system
2005
This paper describes a domain specific multi-agent ontology-mapping solution in the AQUA query answering system. In order to incorporate uncertainty inherent to the mapping process, the system uses the Dempster-Shafer model for dealing with incomplete and uncertain information produced during the mapping. A novel approach is presented how specialized agents with partial local knowledge of the particular domain achieve ontology mapping without creating global or reference ontology.
A Novel Approach for Semantic Integration of Data using Ontology
Indian Journal of Science and Technology, 2016
Integration process of data is recently recognized like a significant visualization of the Semantic Web explore for which researchers focus on numerous areas, such as integration of information, ontologies and databases. Objectives: Users generally requires an incorporated analysis of information accessible from various data and it was proposed to grant users with this view of data. Methods: The Meta data is created from different data sets like excel data set, RDF data set and XML data set. Findings: In this work a survey was made for integrating databases using ontology and a new approach for integration of databases is devised for finding correspondence between ontologies. Applications: View and Search functionality was provided by filtering data from Meta data. This provides easy access to the integrated data.
Ontology-based Question Answering System
Data and Information requirement is increasing with the Increase in the volumes of data in the repositories such as www etc, now question arises that out of this enormous data how to find the information which is required by the user and should be specific in nature. Information retrieval techniques solves the problem to an extent but they cannot help in a situation where only specific information pertaining to a question is required. Information retrieval engines will retrieve documents containing phrases and paragraphs which may have an answer to user query. This problem is addressed in this research paper which proposes a question answering system to satisfy users specific information need.
Ontology-Based Information Integration: A Survey
2001
In the past a lot of approaches concerning the integration of heterogeneous information sources are developed. In the last years the semantics, which play an important role during the integration task, come into the focus leading to the so called ontology-based integration approaches. This paper provides a survey of most prominent ontologybased integration approaches. The approaches are evaluated according four criterions, i.e. the role and the representation of the ontologies, the mapping relating sources and ontologies, and their support for ontology engineering. The evaluation gives an impression, how which problems are solved, and shows the need for further research.
Automatic fusion of knowledge stored in ontologies
Intelligent Decision Technologies, 2010
A person adds new knowledge to his/her mind, taking into account new information, additional details, better precision, synonyms, homonyms, redundancies, apparent contradictions, and inconsistencies between what he/she knows and new knowledge that he/she acquires. This way, he/she incrementally acquires information keeping it at all times consistent. This information can be represented by Ontologies. In contrast to human approach, algorithms of Ontologies fusion lack these features, merely being computer-aided editors where a person solves the details and inconsistencies. This article presents a method for Ontology Merging (OM), its algorithm and implementation to fuse or join two ontologies (obtained from Web documents) in an automatic fashion (without human intervention), producing a third ontology, and taking into account the inconsistencies, contradictions, and redundancies between both ontologies, thus delivering a result close to reality. The repeated use of OM allows acquisition of much information about the same topic. 2 synonyms among others cases. Nowadays, computers could do the same process (joining knowledge which comes from two different ontologies) through an editor [ §1.2] that makes preliminary alignment of concepts, and lets a person finally decide. It is a computer-aided fusion. The problem to solve is how to mechanize that fusion.
Knowledge accumulation through automatic merging of ontologies
Expert Systems with Applications, 2010
In order to compute intelligent answers to complex questions, using the vast amounts of information existing in the Web, computers have (1) to translate such knowledge, typically from text documents, into a data structure suitable for automatic exploitation; (2) to accumulate enough knowledge about a certain topic or area by integrating or fusing these data structures, taking into account new information, additional details, better precision, synonyms, homonyms, redundancies, apparent contradictions and inconsistencies found in the incoming data structures to be added; and (3) to perform deductions from that amassed body of knowledge, most likely through a general query processor.
Knowledge Baesd Question Answering System Using Ontology
In the modern era numerous information available in the World Wide Web. Question Answering systems aims to retrieve point-to-point answers rather than flooding with documents. It is needed when the user gets an in depth knowledge in a particular domain. When user needs some information, it must give the relevant answer. The basic idea of QA systems in Natural Language Processing (NLP) is to provide correct answers to the questions for the user. Here the question answering system has to be implemented as semantic web. This research will evaluate the answering system by the expert. The experts are personalized based on their domain and subject. Ontology is used to personalize the experts. Based on these, the answer has to be ranked and given back to the user. It consists of three phases such as User’s zone, Interface and Expert’s zone. Natural language processing techniques are used for processing the question and also for answer extraction. The domain knowledge is used for reformulating queries and identifying the relations.The highlight of this paper is providing most relative answer for the question provided by the end users in an efficient way.
AQUA: A Knowledge-Based Architecture for a Question Answering System
Abstract This paper describes AQUA, a question answering system. AQUA combines Natural Language processing (NLP), Ontologies, Logic, and Information Retrieval technologies in a uniform framework. AQUA makes intensive use of an ontology (which encode knowledge) in several parts of the question answering system.
Ontology Merging using Answer Set Programming and Linguistic Knowledge
2009
Abstract. With the increasing number of ontologies available on the web, the problem of merging ontologies from different sources to interoperate applications becomes important. This paper presents a novel approach for merging of light-weight ontologies based on answer set programming (ASP) and linguistic background knowledge. ASP provides a declarative execution environment for intuitive merging rules. WordNet provides broad linguistic knowledge that is used to identify corresponding concepts.
Integrating Ontological Data Sources Using Viewpoints-Based Approach
Journal of Computing and Information Technology, 2016
Within the development of Internet and intranets, information integration from various data sources becomes increasingly important and more challenging issue. Recently, the trend in data integration has favored the semantic integration using ontologies. However, the existing ontology-based approaches do not support the aspect of data multi-representations, which is important in the development of multiuser applications. The motivation of this paper is to address a novel semantic integration approach based on ontologies and viewpoints paradigms. This contribution combines the advantages of existing ontology-based integration approaches while avoiding their drawbacks. The proposed integration approach is evaluated using query processing. Profiles are introduced to offer answers to users according to their viewpoints and choices.
2009
Abstract Separate data sources on related domains of knowledge generally contain different but complementary information. There are queries that can only be answered with pieces of information from some of the separate sources. Thus, it is of considerable interest to enable query answering based on searching the information in an integrated collection of sources. However, independently developed and evolved data sources generally use different schemas to represent their data.
Cross ontology query answering on the semantic web: an initial evaluation
2009
PowerAqua 1 is a Question Answering system, which takes as input a natural language query and is able to return answers drawn from relevant semantic resources found anywhere on the Semantic Web. In this paper we provide two novel contributions: First, we detail a new component of the system, the Triple Similarity Service, which is able to match queries effectively to triples found in different ontologies on the Semantic Web. Second, we provide a first evaluation of the system, which in addition to providing data about PowerAqua's competence, also gives us important insights into the issues related to using the Semantic Web as the target answer set in Question Answering. In particular, we show that, despite the problems related to the noisy and incomplete conceptualizations, which can be found on the Semantic Web, good results can already be obtained.