Exploiting Linked Open Data to Uncover Entity Types

Extracting structured information from text plays a crucial role in automatic knowledge acquisition and is at the core of any knowledge representation and reasoning system. Traditional methods rely on hand-crafted rules and are restricted by the performance of various linguistic pre-processing tools. More recent approaches rely on supervised learning of relations trained on labelled examples, which can be manually created or sometimes automatically generated (referred as distant supervision). We propose a supervised method for entity typing and alignment. We argue that a rich feature space can improve extraction accuracy and we propose to exploit Linked Open Data (LOD) for feature enrichment. Our approach is tested on task-2 of the Open Knowledge Extraction challenge, including automatic entity typing and alignment. Our approach demonstrate that by combining evidences derived from LOD (e.g. DBpedia) and conventional lexical resources (e.g. WordNet) (i) improves the accuracy of the supervised induction method and (ii) enables easy matching with the Dolce+DnS Ultra Lite ontology classes

Ripple-down rules based open information extraction for the web documents

The World Wide Web contains a massive amount of information in unstructured natural language and obtaining valuable information from informally written Web documents is a major research challenge. One research focus is Open Information Extraction (OIE) aimed at developing relation-independent information extraction. Open Information Extraction systems seek to extract all potential relations from the text rather than extracting few pre-defined relations. Previous machine learning-based Open Information Extraction systems require large volumes of labelled training examples and have trouble handling NLP tools errors caused by Web s informality. These systems used self-supervised learning that generates a labelled training dataset automatically using NLP tools with some heuristic rules. As the number of NLP tool errors increase because of the Web s informality, the self-supervised learning-based labelling technique produces noisy label and critical extraction errors. This thesis presents Ripple-Down Rules based Open Information Extraction (RDROIE) an approach to Open Information Extraction that uses Ripple-Down Rules (RDR) incremental learning technique. The key advantages of this approach are that it does not require labelled training dataset and can handle the freer writing style that occurs in Web documents and can correct errors introduced by NLP tools. The RDROIE system, with minimal low-cost rule addition, outperformed previous OIE systems on informal Web documents

Semantic Relation Extraction. Resources, Tools and Strategies

[Abstract] Relation extraction is a subtask of information extraction that aims at obtaining instances of semantic relations present in texts. This information can be arranged in machine-readable formats, useful for several applications that need structured semantic knowledge. The work presented in this paper explores different strategies to automate the extraction of semantic relations from texts in Portuguese, Galician and Spanish. Both machine learning (distant-supervised and supervised) and rule-based techniques are investigated, and the impact of the different levels of linguistic knowledge is analyzed for the various approaches. Regarding domains, the experiments are focused on the extraction of encyclopedic knowledge, by means of the development of biographical relations classifiers (in a closed domain) and the evaluation of an open information extraction tool. To implement the extraction systems, several natural language processing tools have been built for the three research languages: From sentence splitting and tokenization modules to part-of-speech taggers, named entity recognizers and coreference resolution systems. Furthermore, several lexica and corpora have been compiled and enriched with different levels of linguistic annotation, which are useful for both training and testing probabilistic and symbolic models. As a result of the performed work, new resources and tools are available for automated processing of texts in Portuguese, Galician and Spanish.Ministerio de Economía y Competitividad; FFI2014-51978-C2-1-RMinisterio de Economía y Competitividad; FJCI-2014-2285

Supervised Random Walks: Predicting and Recommending Links in Social Networks

Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open. We develop an algorithm based on Supervised Random Walks that naturally combines the information from the network structure with node and edge level attributes. We achieve this by using these attributes to guide a random walk on the graph. We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links will be created in the future. We develop an efficient training algorithm to directly learn the edge strength estimation function. Our experiments on the Facebook social graph and large collaboration networks show that our approach outperforms state-of-the-art unsupervised approaches as well as approaches that are based on feature extraction

Multi-domain clinical natural language processing with MedCAT: The Medical Concept Annotation Toolkit

Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of information extraction (IE) technologies to enable clinical analysis. We present the open source Medical Concept Annotation Toolkit (MedCAT) that provides: (a) a novel self-supervised machine learning algorithm for extracting concepts using any concept vocabulary including UMLS/SNOMED-CT; (b) a feature-rich annotation interface for customizing and training IE models; and (c) integrations to the broader CogStack ecosystem for vendor-agnostic health system deployment. We show improved performance in extracting UMLS concepts from open datasets (F1:0.448-0.738 vs 0.429-0.650). Further real-world validation demonstrates SNOMED-CT extraction at 3 large London hospitals with self-supervised training over ∼8.8B words from ∼17M clinical records and further fine-tuning with ∼6K clinician annotated examples. We show strong transferability (F1 > 0.94) between hospitals, datasets and concept types indicating cross-domain EHR-agnostic utility for accelerated clinical and research use cases

Arabic open information extraction system using dependency parsing

Arabic is a Semitic language and one of the most natural languages distinguished by the richness in morphological enunciation and derivation. This special and complex nature makes extracting information from the Arabic language difficult and always needs improvement. Open information extraction systems (OIE) have been emerged and used in different languages, especially in English. However, it has almost not been used for the Arabic language. Accordingly, this paper aims to introduce an OIE system that extracts the relation tuple from Arabic web text, exploiting Arabic dependency parsing and thinking carefully about all possible text relations. Based on clause types' propositions as extractable relations and constituents' grammatical functions, the identities of corresponding clause types are established. The proposed system named Arabic open information extraction(AOIE) can extract highly scalable Arabic text relations while being domain independent. Implementing the proposed system handles the problem using supervised strategies while the system relies on unsupervised extraction strategies. Also, the system has been implemented in several domains to avoid information extraction in a specific field. The results prove that the system achieves high efficiency in extracting clauses from large amounts of text