Extracting useful entities and attribute values from illicit domains such as human trafficking is a challenging problem with the potential for widespread social impact. Such domains employ atypical language models, have 'long tails' and suffer from the problem of concept drift. In this paper, we propose a lightweight, feature-agnostic Information Extraction (IE) paradigm specifically designed for such domains. Our approach uses raw, unlabeled text from an initial corpus, and a few (12-120) seed annotations per domain-specific attribute, to learn robust IE models for unobserved pages and websites. Empirically, we demonstrate that our approach can outperform feature-centric Conditional Random Field baselines by over 18 F-Measure on five annotated sets of real-world human trafficking datasets in both low-supervision and high-supervision settings. We also show that our approach is demonstrably robust to concept drift, and can be efficiently bootstrapped even in a serial computing environment.
In the Semantic Web, domain-specific extraction of entities and properties is a fundamental aspect in constructing instance-rich knowledge bases (from unstructured corpora) that contribute to the Semantic Web vision and to ecosystems like Linked Open Data @cite_29 , @cite_4 . A good example of such a system is Lodifier @cite_6 . This work is along the same lines, in that we are interested in user-specified attributes and wish to construct a knowledge base (KB) with those attribute values using raw Web corpora. However, we are not aware of any IE work in the Semantic Web that has used word representations to accomplish this task, or that has otherwise outperformed state-of-the-art systems without manual feature engineering.
@cite_29 @cite_4 @cite_6
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The approach towards Semantic Web Information Extraction (IE) presented here is implemented in KIM – a platform for semantic indexing, annotation, and retrieval. It combines IE based on the mature text engineering platform (GATE1) with Semantic Web-compliant knowledge representation and management. The cornerstone is automatic generation of named-entity (NE) annotations with class and instance references to a semantic repository. Simplistic upper-level ontology, providing detailed coverage of the most popular entity types (Person, Organization, Location, etc.; more than 250 classes) is designed and used. A knowledge base (KB) with de-facto exhaustive coverage of real-world entities of general importance is maintained, used, and constantly enriched. Extensions of the ontology and KB take care of handling all the lexical resources used for IE, most notable, instead of gazetteer lists, aliases of specific entities are kept together with them in the KB. A Semantic Gazetteer uses the KB to generate lookup annotations. Ontologyaware pattern-matching grammars allow precise class information to be handled via rules at the optimal level of generality. The grammars are used to recognize NE, with class and instance information referring to the KIM ontology and KB. Recognition of identity relations between the entities is used to unify their references to the KB. Based on the recognized NE, template relation construction is performed via grammar rules. As a result of the latter, the KB is being enriched with the recognized relations between entities. At the final phase of the IE process, previously unknown aliases and entities are being added to the KB with their specific types. The automated extraction of information from text and its transformation into a formal description is an important goal in both Semantic Web research and computational linguistics. The extracted information can be used for a variety of tasks such as ontology generation, question answering and information retrieval. LODifier is an approach that combines deep semantic analysis with named entity recognition, word sense disambiguation and controlled Semantic Web vocabularies in order to extract named entities and relations between them from text and to convert them into an RDF representation which is linked to DBpedia and WordNet. We present the architecture of our tool and discuss design decisions made. An evaluation of the tool on a story link detection task gives clear evidence of its practical potential. Traditionally, Information Extraction (IE) has focused on satisfying precise, narrow, pre-specified requests from small homogeneous corpora (e.g., extract the location and time of seminars from a set of announcements). Shifting to a new domain requires the user to name the target relations and to manually create new extraction rules or hand-tag new training examples. This manual labor scales linearly with the number of target relations. This paper introduces Open IE (OIE), a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input. The paper also introduces TEXTRUNNER, a fully implemented, highly scalable OIE system where the tuples are assigned a probability and indexed to support efficient extraction and exploration via user queries. We report on experiments over a 9,000,000 Web page corpus that compare TEXTRUNNER with KNOWITALL, a state-of-the-art Web IE system. TEXTRUNNER achieves an error reduction of 33 on a comparable set of extractions. Furthermore, in the amount of time it takes KNOWITALL to perform extraction for a handful of pre-specified relations, TEXTRUNNER extracts a far broader set of facts reflecting orders of magnitude more relations, discovered on the fly. We report statistics on TEXTRUNNER's 11,000,000 highest probability tuples, and show that they contain over 1,000,000 concrete facts and over 6,500,000 more abstract assertions. The integration of facts derived from information extraction systems into existing knowledge bases requires a system to disambiguate entity mentions in the text. This is challenging due to issues such as non-uniform variations in entity names, mention ambiguity, and entities absent from a knowledge base. We present a state of the art system for entity disambiguation that not only addresses these challenges but also scales to knowledge bases with several million entries using very little resources. Further, our approach achieves performance of up to 95 on entities mentioned from newswire and 80 on a public test set that was designed to include challenging queries.