AQUA: A Knowledge-Based Architecture for a Question Answering System (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.
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).
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.
Architecture of an Ontology-Based Domain-Specific Natural Language Question Answering System
International journal of Web & Semantic Technology, 2013
Question answering (QA) system aims at retrieving precise information from a large collection of documents against a query. This paper describes the architecture of a Natural Language Question Answering (NLQA) system for a specific domain based on the ontological information, a step towards semantic web question answering. The proposed architecture defines four basic modules suitable for enhancing current QA capabilities with the ability of processing complex questions. The first module was the question processing, which analyses and classifies the question and also reformulates the user query. The second module allows the process of retrieving the relevant documents. The next module processes the retrieved documents, and the last module performs the extraction and generation of a response. Natural language processing techniques are used for processing the question and documents and also for answer extraction. Ontology and domain knowledge are used for reformulating queries and identifying the relations. The aim of the system is to generate short and specific answer to the question that is asked in the natural language in a specific domain. We have achieved 94 % accuracy of natural language question answering in our implementation.
Aqualog: An ontology-portable question answering system for the semantic web
2005
As semantic markup becomes ubiquitous, it will become important to be able to ask queries and obtain answers, using natural language (NL) expressions, rather than the keyword-based retrieval mechanisms used by the current search engines. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input and returns answers drawn from the available semantic markup.
AquaLog: An ontology-driven question answering system for organizational semantic intranets
Journal of Web Semantics, 2007
The semantic web 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 and an ontology as input, and returns answers drawn from one or more knowledge bases (KBs). We say that AquaLog is portable because the configuration time required to customize the system for a particular ontology is negligible. AquaLog presents an elegant solution in which different strategies are combined together in a novel way. It makes use of the GATE NLP platform, string metric algorithms, WordNet and a novel ontology-based relation similarity service to make sense of user queries with respect to the target KB. Moreover it also includes a learning component, which ensures that the performance of the system improves over the time, in response to the particular community jargon used by end users.
AquaLog A Ontology-portable Question Answering interface for the Semantic Web
2005
As semantic markup becomes ubiquitous, it will become important to be able to ask queries and obtain answers, using natural language (NL) expressions, rather than the keyword-based retrieval mechanisms used by the current search engines. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input and returns answers drawn from the available semantic markup. We say that AquaLog is portable, because the configuration time required to customize the system for a particular ontology is negligible. AquaLog combines several powerful techniques in a novel way to make sense of NL queries and to map them to semantic markup. Moreover it also includes a learning component, which ensures that the performance of the system improves over time, in response to the particular community jargon used by the end users. In this paper we describe the current version of the system, in particular discussing its portability, its reasoning capabilities, and its learning mechanism.
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.
Using ontology in query answering systems: Scenarios, requirements and challenges
2003
Abstract. Equipped with the ultimate query answering system, computers would finally be in a position to address all our information needs in a natural way. In this paper, we describe how Language and Computing nv (L&C), a developer of ontology-based natural language understanding systems for the healthcare domain, is working towards the ultimate Question Answering (QA) System for healthcare workers.
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.
Implementation approaches for various categories of question answering system
2013 IEEE CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES, 2013
Search engines can return ranked documents as a result for any query from which the user struggle to navigate and search the correct answer. This process wastes user's navigation time and due to this the need for automated question answering systems becomes more urgent. We need such a system which is capable of replying the exact and concise answer to the question posed in natural language. The best way to address this problem is use of Question answering systems (QAS). The basic aim of QAS is to provide short and correct answer to the user saving his/her navigation time. The concept of Natural Language Processing plays an important role in developing any QAS. This paper provides an implementation approaches for various categories of QAS such as Closed Domain based QAS, Open Domain based QAS, WEBBASED QAS, Information Retrieval or Information Extraction (IR/IE) based QAS, and Rule based QAS which will be helpful for new directions of research in this area.
AQUA: A question answering system for heterogeneous sources
2004
ABSTRACT This paper describes AQUA our question answering over the Web. AQUA was designed to work over heterogeneous sources. This means that AQUA is equipped to work as closed domain and in addition to open-domain question answering. As a first instance, AQUA tries to answer a question using a Knowledge base. If a query cannot be satisfied over a knowledge base/database. Then, AQUA tries to find an answer on web pages (ie it uses as corpus the internet as resource).
A question answering system on domain specific knowledge with semantic web support
2011
In today's world the majority of information is accessible via the World Wide Web. A common way to access this information is through information retrieval applications like web search engines. We already know that web search engines flood their users with enormous amount of data from which they cannot figure out the essential and most important information. These disadvantages can be reduced with question answering systems. The basic idea of question answering systems is to be able to provide answers to a specific question written in natural language. The main goal of question answering systems is to find a specific answer. This paper presents an architecture of our ontology-driven system that uses semantic description of the processes, databases and web services for question answering system in the Slovenian language.
QASYO: A Question Answering System for YAGO Ontology
The tremendous development in information technology led to an explosion of data and motivated the need for powerful yet efficient strategies for data mining and knowledge discovery. Question Answering (QA) systems made it possible to ask questions and retrieve answers using natural language (NL) queries, rather than the keyword-based retrieval mechanisms used by current search engines. In Ontology-based QA system, the knowledge based data, where the answers are sought, has a structured organization. The question-answer retrieval of ontology knowledge base provides a convenient way to obtain knowledge for use, but the natural language need to be mapped to the query statement of ontology. QASYO is a sentence level question-answering system that integrates natural language processing, ontologies and information retrieval technologies in a unified framework. It accepts queries expressed in natural language and YAGO ontology as inputs and provides answers drawn from the available semantic markup. QASYO combines several powerful techniques in a novel way to make sense of NL queries and to map them to semantic markup. Semantic analysis of questions is performed in order to extract keywords used in the retrieval queries and to detect the expected answer type. In this paper we describe the current version of the system, in particular discussing its reasoning capabilities, and Performance.
Ontology-driven question answering system with semantic web services support
Nowadays the internet is becoming a huge dump of documents, links and all other sorts of information. Most common possibilities to explore this information are information retrieval applications such as web search engines. Despite the fact that search engines are doing an excellent job, they still return too much inaccurate information. The solution to this problem can be found in the form of question answering systems, where the user gives a question in natural language, similarly to talking with another person. The answer is the exact information instead of a list of possible results. This paper presents the design of our ontology-driven question answering system with semantic web services support.
A Semantic Approach to Question Answering Systems
Text REtrieval Conference, 2000
This paper describes the architecture, operation and results obtained with the Question Answering prototype developed in the Department of Language Processing and Information Systems at the University of Alicante. Our approach accomplishes question representation by combining keywords with a semantic representation of expected answer characteristics. Answer string ranking is performed by computing similarity between this representation and document sentences.
Proceedings of the 7th …, 2008
Many existing search engines do not have an important capability, the capability to deduce an answer to a query based on information which reside in various parts of documents. The levels-of-processing theory proposes that there are many ways to process and code information and thus the knowledge representation used as surrogate to documents are qualitatively different. The capability of deduction is much depended on the knowledge representation framework used. We propose a unified logical-linguistic model as knowledge representation framework as a basis for indexing of documents as well as deduction capability to provide answers to queries. The approach applies semantic analysis in transforming and normalising information from natural language texts into a declarative knowledge based representation of first order predicate logic. Retrieval of relevant information can then be performed through plausible logical implication and answer to query is carried out using theorem proving technique. This paper elaborates on the model and how it is used in information retrieval and question answering system as one unified model.
Advances in Intelligent Systems and Computing
The rising popularity of the Information Retrieval (IR) field has created a high demand for the services which facilitates the web users to rapidly and reliably retrieve the most pertinent information. Question Answering (QA) system is one of the services which provide the adequate sentences as answers to the specific natural language questions. Despite its importance, it lacks in providing the accurate answer along with the adequate, significant information while increasing the degree of ambiguity in the candidate answers. It encompasses three phases to enhance the performance of QA system using the web as well as the semantic knowledge. The WAD approach defines the context-aware candidate sentences by using the query expansion technique and entity linking method, second, Ranks the sentences by exploiting the conditional probability between the query and candidate sentences and the automated system, third, identifies the precise answer including the reasonable, adequate information by optimal answer type identification and validation using conditional probability and ontology structure. The WAD methodology provides an answer to a posted query with maximum accuracy than baseline method.
Developing an Intelligent Question Answering System
International Journal of Education and Management Engineering
The goal of an intelligent answering system is that the system can respond to questions automatically. For developing such kind of system, it should be able to answer, and store these questions along with their answers. Our intelligent QA (iQA) system for Arabic language will be growing automatically when users ask new questions and the system will be accumulating these new question-answer pairs in its database. This will speed up the processing when the same question(even if it is in different syntactical structure but semantically same) is being asked again in the future. The source of knowledge of our system is the World Wide Web(WWW). The system can also understand and respond to more sophisticated questions that need a kind of temporal inference.
PowerAqua: A Multi-Ontology Based Question Answering System–v1
Abstract. In this report, we present PowerAqua, a multi-ontology-based Question Answering (QA) system, which takes as input queries expressed in natural language and is able to return answers drawn from relevant distributed resources on the Semantic Web. In contrast with any other existing natural language front end, PowerAqua is not restricted to a single ontology and therefore provides the first comprehensive attempt at supporting open domain QA on the Semantic Web.