OpenID Research Articles

HealthAid: Extracting domain targeted high precision procedural knowledge from on-line communities

Recent advances in semantic web have shown how entity related searches have benefited from entity-based knowledge graphs. However, much of the commonsense knowledge about the real world is in the form of procedures or sequences of actions. Also, search log analysis shows that ‘how-to queries’ make up a significant amount of users’ queries. Unfortunately, these kinds of knowledge are missing from most knowledge graphs and commonsense knowledge bases in use. To empower semantic search, and other intelligent applications, computers need a much broader understanding of the world properties of everyday objects, human activities, and more. Luckily, such knowledge is abundantly available on-line and can be accessed from how-to communities. One domain of interest by on-line communities is the health domain, whereby users usually seek home remedies to common health-related issues. An example of such queries might be ‘how to stop nausea using acupressure’ or ‘how to aid digestion naturally’. To answer such questions, we need systems that understand natural language and knowledge bases with task frames of solutions in a holistic approach, including the tools required, the agents involved, and the temporal order of the actions. Our goal is to construct a machine-readable domain targeted high precision procedural knowledge base containing task frames. We developed a pipeline of methods leveraging open information extraction tool to extract procedural knowledge by tapping into on-line communities. Also, we devised a mechanism to canonicalize the task frames into clusters based on the similarity of the problems they intend to solve. The resulting know-how knowledge base, HealthAidKB, consists of more than 71 K task frames which are structured hierarchically and categorically; and can be used in many applications such as semantic search, digital personal assistants, human-computer dialog and computer vision. A comprehensive evaluation of our knowledge base shows high accuracy.

Open Information Extraction for Waste Incineration NIMBY based on Bert Network in China

Unstructured data makes it difficult to build the emergency knowledge base. Information extraction can help people get structured information faster and more accurately. In this paper, a method of information extraction based on Bert neural network is proposed, which can extract information from the Chinese open text of Waste Incineration NIMBY. It provides a scientific scheme for the construction of a structured knowledge base of Waste Incineration NIMBY, so as to provide auxiliary decision-making for solving the conflict of Waste Incineration NIMBY. Experiments show that the precision rate, recall rate and F-value of the information extraction can reach 91% by using the Bert network, which basically meets the requirements of building a structured knowledge base of Waste Incineration NIMBY.

Event detection based on open information extraction and ontology

ABSTRACT Most of the information is available in the form of unstructured textual documents due to the growth of information sources (the Web for example). In this respect, to extract a set of events from texts written in natural language in the management change event, we have been introduced an open information extraction (OIE) system. For instance, in the management change event, a PERSON might be either the new coming person to the company or the leaving one. As a result, the Adaptive CRF approach (A-CRF) has shown good performance results. However, it requires a lot of expert intervention during the construction of classifiers, which is time consuming. To palpate such a downside, we introduce an approach that reduces the expert intervention during the relation extraction. Also, the named entity recognition and the reasoning, which are automatic and based on techniques of adaptation and correspondence, were implemented. Carried out experiments show the encouraging results of the main approaches of the literature.

Multi-document semantic relation extraction for news analytics

Given the overwhelming amounts of information in our current 24/7 stream of new incoming articles, new techniques are needed to enable users to focus on just the key entities and concepts along with their relationships. Examples include news articles but also business reports and social media. The fact that relevant information may be distributed across diverse sources makes it particularly challenging to identify relevant connections. In this paper, we propose a system called MuReX to aid users in quickly discerning salient connections and facts from a set of related documents and viewing the resulting information as a graph-based visualization. Our approach involves open information extraction, followed by a careful transformation and filtering approach. We rely on integer linear programming to ensure that we retain only the most confident and compatible facts with regard to a user query, and finally apply a graph ranking approach to obtain a coherent graph that represents meaningful and salient relationships, which users may explore visually. Experimental results corroborate the effectiveness of our proposed approaches, and the local system we developed has been running for more than one year.

Span Model for Open Information Extraction on Accurate Corpus

Open Information Extraction (Open IE) is a challenging task especially due to its brittle data basis. Most of Open IE systems have to be trained on automatically built corpus and evaluated on inaccurate test set. In this work, we first alleviate this difficulty from both sides of training and test sets. For the former, we propose an improved model design to more sufficiently exploit training dataset. For the latter, we present our accurately re-annotated benchmark test set (Re-OIE2016) according to a series of linguistic observation and analysis. Then, we introduce a span model instead of previous adopted sequence labeling formulization for n-ary Open IE. Our newly introduced model achieves new state-of-the-art performance on both benchmark evaluation datasets.

A Method of Constructing Marine Oil Spill Scenarios from Flat Text Based on Semantic Analysis.

Constructed emergency response scenarios provide a basis for decision makers to make management decisions, and the development of such scenarios considers earlier historical cases. Over the decades, the development of emergency response scenarios has mainly implemented the elements of historic cases to describe the grade and influence of an accident. This paper focuses on scenario construction and proposes a corresponding framework based on natural language processing (NLP) using text reports of marine oil spill accidents. For each accident, the original textual reports are first divided into sentence sets corresponding to the temporal evolution. Each sentence set is regarded as a textual description of a marine oil spill scenario. A method is proposed in this paper, based on parsing, named entity recognition (NER) and open information extraction (OpenIE) to process the relation triples that are extracted from the sentence sets. Finally, the relation triples are semantically clustered into different marine oil spill domains to construct scenarios. The research results are validated and indicate that the proposed scenario construction framework can be effectively used in practical applications.

KBPearl

Nowadays, most openly available knowledge bases (KBs) are incomplete, since they are not synchronized with the emerging facts happening in the real world. Therefore, knowledge base population (KBP) from external data sources, which extracts knowledge from unstructured text to populate KBs, becomes a vital task. Recent research proposes two types of solutions that partially address this problem, but the performance of these solutions is limited. The first solution, dynamic KB construction from unstructured text, requires specifications of which predicates are of interest to the KB, which needs preliminary setups and is not suitable for an in-time population scenario. The second solution, Open Information Extraction (Open IE) from unstructured text, has limitations in producing facts that can be directly linked to the target KB without redundancy and ambiguity. In this paper, we present an end-to-end system, KBPearl, for KBP, which takes an incomplete KB and a large corpus of text as input, to (1) organize the noisy extraction from Open IE into canonicalized facts; and (2) populate the KB by joint entity and relation linking, utilizing the context knowledge of the facts and the side information inferred from the source text. We demonstrate the effectiveness and efficiency of KBPearl against the state-of-the-art techniques, through extensive experiments on real-world datasets.

PragmaticOIE: a pragmatic open information extraction for Portuguese language

Information extraction (IE) involves the extraction of useful facts from texts. IE approaches have been categorized into two types: Traditional IE and Open IE. Traditional IE recognizes a predefined set of relationships between the arguments, and it has typically been applied to specific domains. Open IE extracts relationship descriptors expressing any semantic relationship between a pair of arguments in different domains. Although a sentence can have a different meaning, given the context and intention used, a single semantic analysis does not guarantee useful extractions. Extractions depend on the context and the intention inherited in a sentence that goes beyond the semantic meaning. Thus, a pragmatic analysis enhances the set of extractions by considering the contextual and intentional aspects. As a consequence, new facts can be extracted from this set of sentences. The combination of inference, context, and intention enables the extraction of implicit facts from texts achieving a first pragmatic level. This novel approach increases the number of facts, extracting relationships from a sentence analyzing inference, context, and intention. This is the first method to analyze a first pragmatic level from a sentence within a set of Portuguese text documents. Our method was performed over a set of Portuguese text documents and outperforms the most relevant related work comparing accuracy, number of extracted facts, and minimality measures.

Logical-linguistic model for multilingual Open Information Extraction

Open Information Extraction (OIE) is a modern strategy to extract the triplet of facts from Web-document collections. However, most part of the current OIE approaches is based on NLP techniques suc...

QA4IE: A Question Answering Based System for Document-Level General Information Extraction

Information Extraction (IE) is the task of distilling structured information from unstructured texts by identifying references to named entities as well as relationships between such entities. Existing IE solutions, including Relation Extraction and Open IE, can hardly take cross-sentence information like coreferences into account and are severely restricted by limited relation types as well as informal relation specifications (e.g., free-text based relation triples). In order to overcome the weaknesses, we propose a novel IE framework named QA4IE, which leverages the flexible question answering approaches to produce high-quality relation triples across sentences. Based on this framework, we develop a real-time IE system, which can perform general IE throughout the entire document. For training and evaluating our system, we build a large-scale IE benchmark using distant supervision under human evaluation. We deploy both component analyses and pipeline experiments to evaluate our system. The results show that our system can generalize on unseen entities and relations, as well as achieve significant improvements over existing IE systems.

Artificial Intelligence-Driven Structurization of Diagnostic Information in Free-Text Pathology Reports

Background: Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications. Methods: In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs). Results: Evaluation studies have demonstrated that the pipeline’s output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The precision and recall of extracted RTs based on experts’ assessment were 0.925 and 0.841 respectively (P <0.0001). Inter-rater agreement was significant at 93.6% and inter-rated reliability was 81.8%. Conclusion: The results demonstrated important properties of the pipeline such as high accuracy, minimality and adequate knowledge representation. Therefore, we conclude that the pipeline can be used in various downstream data-mining applications to assist diagnostic medicine.

A new representation of open information extraction in Persian language

Open information extraction is a new technology in the text mining process which is still at the beginning and requires many attempts and considerations for improvement. These attempts includes both the representation and extraction of information. The complication and instability of language intensify the problems of the open information extraction. In this article an advanced representation of information is presented for the Persian language; a representation which can be a favorable cover for the open information extraction by identifying dependency analysis relationships. In the present article, it is tried to reach the feasibility of this representation, the representation correspondence using syntactic labeling, and plausible representation of information extraction. By making this attempts, the threshold for the information extraction goes far beyond its simple epresentation state, which is a tripple. Although this article tries to overally outline the approach of using an advanced representation of information extraction, it specifically investigates the use of this representation in QA systems.

Lexicon-Grammar based open information extraction from natural language sentences in Italian

In the last decade, the quantity of readily accessible text has grown rapidly and enormously, long exceeding the capacity of humans to read and understand it. One of the most interesting strategies proposed to fulfill this need is known as Open Information Extraction (OIE). It is essentially devised to read in sentences and rapidly extract one or more domain-independent coherent propositions, each represented by a verb relation and its arguments. Even though many OIE approaches exist for English, no significant research has been conducted about OIE on Italian texts. Due to the usage of language-specific features, OIE systems operating in other languages are not directly applicable for Italian. Therefore, this paper proposes, as first contribution, a novel approach to perform OIE for Italian language, based on standard linguistic structures to analyze sentences and on a set of verbal behavior patterns to extract information from them. These patterns are built combining a solid linguistic theoretical framework, i.e. Lexicon-Grammar (LG), and distributional profiles extracted from a contemporary Italian corpus, i.e. itWaC. Starting from simple sentences, the approach is able to determine elementary tuples, then, all their permutations, by adding complements and adverbials, and, finally, n-ary propositions, by granting syntactic invariance, preserving the overall grammaticality and also respecting some syntactic constraints and selection preferences, thus approximating a first level of semantic acceptability. As second contribution of this work, a gold standard dataset for the Italian language has been built from the itWaC corpus, aimed at being widely used to enable the experimental validation of OIE solutions. It has been manually and independently labeled by four Italian native speakers with all the n-ary propositions that can be extracted, following the criteria of grammaticality and acceptability, i.e. granting syntactic well-formedness and meaningfulness in the context. Finally, the proposed approach has been experimented and quantitatively validated on this gold standard dataset, also in comparison with an indirect approach translating input sentences and output propositions from Italian to English and vice versa and embedding an OIE approach for English, as well as with an OIE system for Italian previously presented by the authors. The results obtained have shown the effectiveness of the proposed approach in generating propositions with respect to these criteria of grammaticality and acceptability. Even if the approach has been evaluated for the Italian language, it is essentially based on linguistic resources produced by LG, which exist for many languages besides Italian and a representative corpus for the language under consideration. Given these premises, it has a general basis from a methodological perspective and can be proficiently extended also to other languages.

Swarm based Intelligent Feature Optimization Technique for ECG based Biometric Human Recognition

Recently, rapid growth in network technology and communication leads to widening human activities, so that a strong identification system is necessary. This work aims to build an accurate identification technology based on unique physiological characteristics of ECG. The Biometric recognition of ECG primarily depends on the quality of its features. Feature extraction is performed on parameters of a cardiac cycle based on the fiducial approach and large data sets of features have been extracted. The extracted dataset contains irrelevant, correlated and over-fitted features, which misleads the biometric system performance so that an effective feature optimization is needed to sort out those features to avoid redundancy in the data. In this paper, a novel swarm based intelligent feature optimization method; Particle Swarm Optimization (PSO) is used to generate feature subset based on a fitness function with joint entropy. The dataset from optimization phase are fed to classifiers such as ANN, K-NN and SVM for recognition. The proposed approach is tested with available open source MIT-BIH ECG ID database. Finally, a comparison is made with and without feature optimization in which PSO with KNN shows recognition accuracy of 97.8931%.

Multilingual Open Information Extraction: Challenges and Opportunities

The number of documents published on the Web in languages other than English grows every year. As a consequence, the need to extract useful information from different languages increases, highlighting the importance of research into Open Information Extraction (OIE) techniques. Different OIE methods have dealt with features from a unique language; however, few approaches tackle multilingual aspects. In those approaches, multilingualism is restricted to processing text in different languages, rather than exploring cross-linguistic resources, which results in low precision due to the use of general rules. Multilingual methods have been applied to numerous problems in Natural Language Processing, achieving satisfactory results and demonstrating that knowledge acquisition for a language can be transferred to other languages to improve the quality of the facts extracted. We argue that a multilingual approach can enhance OIE methods as it is ideal to evaluate and compare OIE systems, and therefore can be applied to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning state of the art studies. Additionally, we evaluate the transfer of knowledge to improve the quality of the facts extracted in each language. Moreover, we discuss the importance of a parallel corpus to evaluate and compare multilingual systems.

HClaimE: A tool for identifying health claims in health news headlines

This study tackles the problem of extracting health claims from health research news headlines, in order to carry out veracity check. A health claim can be formally defined as a triplet consisting of an independent variable (IV – namely, what is being manipulated), a dependent variable (DV – namely, what is being measured), and the relation between the two. In this study, we develop HClaimE, an information extraction tool for identifying health claims in news headlines. Unlike the existing open information extraction (OpenIE) systems that rely on verbs as relation indicators, HClaimE focuses on finding relations between nouns, and draws on the linguistic characteristics of news headlines. HClaimE uses a Naïve Bayes classifier that combines syntactic and lexical features for identifying IV and DV nouns, and recognizes relations between IV and DV through a rule-based method. We conducted an evaluation on a set of health news headlines from ScienceDaily.com, and the results show that HClaimE outperforms current OpenIE systems: the F-measures for identifying headlines without health claims is 0.60 and that for extracting IV-relation-DV is 0.69. Our study shows that nouns can provide more clues than verbs for identifying health claims in news headlines. Furthermore, it also shows that dependency relations and bag-of-words can distinguish IV-DV noun pairs from other noun pairs. In practice, HClaimE can be used as a helpful tool to identifying health claims in news headlines, which can then be further compared against authoritative health claims for veracity. Given the linguistic similarity between health claims and other causal claims, e.g., impacts of pollution on the environment, HClaimE may also be applicable for extracting claims in other domains.

ReUS: a Real-time Unsupervised System For Monitoring Opinion Streams

An actual challenge within the sentiment analysis research area is the extraction of polarity values associated with specific aspects (or opinion targets) contained in user-generated content. This task, called aspect-based sentiment analysis, brings new challenges like the disambiguation of words’ role within a text and the inference of correct polarity values based on the domain in which a text occurs. The former requires strategies able to understand how each word is used in a specific context in order to annotate it as aspect or not. The latter need to be addressed with unsupervised solutions in order to make a system efficient for real-time tasks and at the same time flexible in order to adopt it in any domain without requiring the training of sentiment models. Finally, the deployment of such a system into real-world scenarios needs the development of usable solutions for accessing and analyzing data. This paper presents the ReUS platform: a system integrating an unsupervised approach, based on open information extraction strategies, for performing real-time aspect-based sentiment analysis together with facilities supporting decision-makers in the analysis and visualization of collected data. The ReUS platform has been validated from a quantitative and qualitative perspectives. First, the aspect extraction and polarity inference capabilities have been evaluated on three datasets used in likewise editions of SemEval. Second, a user group has been invited to judge the usability of the platform. The developed platform demonstrated to be suitable for being used into real-world scenarios requiring (i) the capability of processing real-time opinion-based documents streams and (ii) the availability of usable facilities for analyzing and visualizing collected data. Examples of possible analysis and visualizations include the presentation of lists ranking aspects by the importance of their polarity values computed within the whole data repository. This kind of analysis enables, for instance, the discovery of product issues.

Feature-enriched matrix factorization for relation extraction

Relation extraction aims at finding meaningful relationships between two named entities from within unstructured textual content. In this paper, we define the problem of information extraction as a matrix completion problem where we employ the notion of universal schemas formed as a collection of patterns derived from open information extraction systems as well as additional features derived from grammatical clause patterns and statistical topic models. One of the challenges with earlier work that employ matrix completion methods is that such approaches require a sufficient number of observed relation instances to be able to make predictions. However, in practice there is often insufficient number of explicit evidence supporting each relation type that could be used within the matrix model. Hence, existing work suffer from a low recall. In our work, we extend the work in the state of the art by proposing novel ways of integrating two sets of features, i.e., topic models and grammatical clause structures, for alleviating the low recall problem. More specifically, we propose that it is possible to (1) employ grammatical clause information from textual sentences to serve as an implicit indication of relation type and argument similarity. The basis for this is that it is likely that similar relation types and arguments are observed within similar grammatical structures, and (2) benefit from statistical topic models to determine similarity between relation types and arguments. We employ statistical topic models to determine relation type and argument similarity based on their co-occurrence within the same topics. We have performed extensive experiments based on both gold standard and silver standard datasets. The experiments show that our approach has been able to address the low recall problem in existing methods, by showing an improvement of 21% on recall and 8% on f-measure over the state of the art baseline.

A Review of Open Information Extraction Techniques

Nowadays, massive amount of data flows all the time. Approximately between 20 or 30 percent of these data is text. This data is always organized in semi-structured text, which cannot be used directly. To make use of such huge amounts of textual data, there is a need to detect, extract, and structure the information conveyed through this data in a fast and scalable manner. This can be performed using Information Extraction Techniques. However, the task of information extraction is one of the main challenges in Natural Language Processing and there are limitations for its implementation on a large scale of data. Open Information Extraction (OIE) is an open-domain and relation-independent paradigm to perform information extraction in an unsupervised manner. This technique can lead to high-speed and scalable performance. The review of previous research proposals reveals that there are OIE experiments among different languages, such as English, Portuguese, Spanish, Vietnamese, Chinese, and Germany. This paper reviews the OIE techniques, compare their performance in some languages, and then integrates these results with the languages complexity levels to reveal the relationship between the suitable model and the language complexity level. Nowadays, massive amount of data flows all the time. Approximately between 20 or 30 percent of these data is text. This data is always organized in semi-structured text, which cannot be used directly. To make use of such huge amounts of textual data, there is a need to detect, extract, and structure the information conveyed through this data in a fast and scalable manner. This can be performed using Information Extraction Techniques. However, the task of information extraction is one of the main challenges in Natural Language Processing and there are limitations for its implementation on a large scale of data. Open Information Extraction (OIE) is an open-domain and relation-independent paradigm to perform information extraction in an unsupervised manner. This technique can lead to high-speed and scalable performance. The review of previous research proposals reveals that there are OIE experiments among different languages, such as English, Portuguese, Spanish, Vietnamese, Chinese, and Germany. This paper reviews the OIE techniques, compare their performance in some languages, and then integrates these results with the languages complexity levels to reveal the relationship between the suitable model and the language complexity level.Keywords—Open Information Extraction; Natural Language Processing

A New Open Information Extraction System Using Sentence Difficulty Estimation

The World Wide Web has a considerable amount of information expressed using natural language. While unstructured text is often difficult for machines to understand, Open Information Extraction (OIE) is a relation-independent extraction paradigm designed to extract assertions directly from massive and heterogeneous corpora. Allocation of low-cost computational resources is a main demand for Open Relation Extraction (ORE) systems. A large number of ORE methods have been proposed recently, covering a wide range of NLP tools, from ``shallow'' (e.g., part-of-speech tagging) to ``deep'' (e.g., semantic role labeling). There is a trade-off between NLP tools depth versus efficiency (computational cost) of ORE systems. This paper describes a novel approach called Sentence Difficulty Estimator for Open Information Extraction (SDE-OIE) for automatic estimation of relation extraction difficulty by developing some difficulty classifiers. These classifiers dedicate the input sentence to an appropriate OIE extractor in order to decrease the overall computational cost. Our evaluations show that an intelligent selection of a proper depth of ORE systems has a significant improvement on the effectiveness and scalability of SDE-OIE. It avoids wasting resources and achieves almost the same performance as its constituent deep extractor in a more reasonable time.