Automatic Extraction of Actionable Knowledge

Tatu, Marta; Balakrishna, Mithun; Werner, Steven; Erekhinskaya, Tatiana; Moldovan, Dan

doi:10.1109/icsc.2016.29

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…Besides the above literature, there are some natural language question/answering systems that pay attention to many other interesting research directions. Sun et al [26], Balakrishna et al [54] and Tatu et al [55] mined answers from integrated structured data and unstructured data. El-Ansari et al [56] presented a Question Answering system that combines multiple knowledge bases.…”

Section: Natural Language Question/answering Without Aggregation Over...mentioning

confidence: 99%

Natural language aggregate query over RDF data

Dang

Yao

et al. 2018

Information Sciences

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Natural language question/answering over RDF data has received widespread attention. Although there have been several studies that have dealt with a small number of aggregate queries, they have many restrictions (i.e., interactive information, controlled question or query template). Thus far, there has been no natural language querying mechanism that can process general aggregate queries over RDF data. Therefore, we propose a framework called NLAQ (Natural Language Aggregate Query). First, we propose a novel algorithm to automatically understand a user's query intention, which mainly contains semantic relations and aggregations. Second, to build a better bridge between the query intention and RDF data, we propose an extended paraphrase dictionary ED to obtain more candidate mappings for semantic relations, and we introduce a predicate-type adjacent set PT to filter out inappropriate candidate mapping combinations in semantic relations and basic graph patterns. Third, we design a suitable translation plan for each aggregate category and effectively distinguish whether an aggregate item is numeric or not, which will greatly affect the aggregate result. Finally, we conduct extensive experiments over real datasets (QALD benchmark and DBpedia), and the experimental results demonstrate that our solution is effective.

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Section: Natural Language Question/answering Without Aggregation Over...mentioning

confidence: 99%

Natural language aggregate query over RDF data

Dang

Yao

et al. 2018

Information Sciences

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“…Since the goal of this article is to detail the means of answering natural language questions using an RDF-triplebased framework, we will omit the description of the NLP module, which can be any suite of NLP tools that identify named entities, word senses, coreferring entities, and semantic relationships between the concepts of a document content. A more detailed description of one such knowledge extraction engine can be found in [14].…”

Section: Triple-based Qamentioning

confidence: 99%

Semantic question answering on big data

Tatu

Werner

Balakrishna

et al. 2016

Proceedings of the International Workshop on Semantic Big Data

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This article describes a high-precision semantic question answering (SQA) engine for large datasets. We employ an RDF store to index the semantic information extracted from large document collections and a natural language to SPARQL conversion module to find desired information. In order to be able to find answers to complex questions in structured/unstructured data resources, our system produces rich semantic structures from the data resources and then transforms the extracted knowledge into an RDF representation. In order to facilitate easy access to the information stored in the RDF semantic index, our system accepts a user's natural language questions, translates them into SPARQL queries and returns a precise answer back to the user. Our improvements in performance over a regular free text search index-based question answering engine prove that SQA can benefit greatly from the addition and consumption of deep semantic information.

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“…The idea of natural language queries being converted to formal querying languages is implemented through different natural language query formalization (NLQF) [4] pipelines such as AskNow [9] by Dubey et al that uses an intermediary canonical syntactic form called Normalized Query Architecture (NQS) or PAROT [28] (Recursive acronym) by Peter Ochieng, which uses a set of dependency-based heuristics to convert the natural language user queries to user's triples which are then processed into ontology triples. Most NLQF pipelines, however, suffer from issues about scalability, accuracy, and efficiency [13,31] . This can be attributed to challenges such as natural language being generally ambiguous and concealed in semantics and context, a general lack of widely used query-type standard, lexical mismatch of query tokens and intent mismatch due to missed contextual information.…”

Section: Introductionmentioning

confidence: 99%

Natural language query formalization to SPARQL for querying knowledge bases using Rasa

et al. 2021

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The idea of data to be semantically linked and the subsequent usage of this linked data with modern computer applications has been one of the most important aspects of Web 3.0. However, the actualization of this aspect has been challenging due to the difficulties associated with building knowledge bases and using formal languages to query them. In this regard, SPARQL, a recursive acronym for standard query language and protocol for Linked Open Data and Resource Description Framework databases, is a most popular formal querying language. Nonetheless, writing SPARQL queries is known to be difficult, even for experts. Natural language query formalization, which involves semantically parsing natural language queries to their formal language equivalents, has been an essential step in overcoming this steep learning curve. Recent work in the field has seen the usage of artificial intelligence (AI) techniques for language modelling with adequate accuracy. This paper discusses a design for creating a closed domain ontology, which is then used by an AI-powered chat-bot that incorporates natural language query formalization for querying linked data using Rasa for entity extraction after intent recognition. A precision–recall analysis is performed using in-built Rasa tools in conjunction with our own testing parameters, and it is found that our system achieves a precision of 0.78, recall of 0.79 and F1-score of 0.79, which are better than the current state of the art.

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Automatic Extraction of Actionable Knowledge

Cited by 8 publications

References 3 publications

Natural language aggregate query over RDF data

Natural language aggregate query over RDF data

Semantic question answering on big data

Natural language query formalization to SPARQL for querying knowledge bases using Rasa

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