Named Entity Extraction for Knowledge Graphs: A Literature Overview

An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other's context. The current culmination of these trends are the deep-learning approaches that have recently reported promising results.publishedVersio

Semantic recovery of traceability links between system artifacts

This paper introduces a mechanism to recover traceability links between the requirements and logical models in the context of critical systems development. Currently, lifecycle processes are covered by a good number of tools that are used to generate different types of artifacts. One of the cornerstone capabilities in the development of critical systems lies in the possibility of automatically recovery traceability links between system artifacts generated in different lifecycle stages. To do so, it is necessary to establish to what extent two or more of these work products are similar, dependent or should be explicitly linked together. However, the different types of artifacts and their internal representation depict a major challenge to unify how system artifacts are represented and, then, linked together. That is why, in this work, a concept-based representation is introduced to provide a semantic and unified description of any system artifact. Furthermore, a traceability function is defined and implemented to exploit this new semantic representation and to support the recovery of traceability links between different types of system artifacts. In order to evaluate the traceability function, a case study in the railway domain is conducted to compare the precision and recall of recovery traceability links between text-based requirements and logical model elements. As the main outcome of this work, the use of a concept-based paradigm to represent that system artifacts are demonstrated as a building block to automatically recover traceability links within the development lifecycle of critical systems.The research leading to these results has received funding from the H2020 ECSEL Joint Undertaking (JU) under Grant Agreement No. 826452 \Arrowhead Tools for Engineering of Digitalisation Solutions" and from speci¯c national programs and/or funding authorities

Meeting of the MINDS: an information retrieval research agenda

Since its inception in the late 1950s, the field of Information Retrieval (IR) has developed tools that help people find, organize, and analyze information. The key early influences on the field are well-known. Among them are H. P. Luhn's pioneering work, the development of the vector space retrieval model by Salton and his students, Cleverdon's development of the Cranfield experimental methodology, Spärck Jones' development of idf, and a series of probabilistic retrieval models by Robertson and Croft. Until the development of the WorldWideWeb (Web), IR was of greatest interest to professional information analysts such as librarians, intelligence analysts, the legal community, and the pharmaceutical industry

Email Analysis and Information Extraction for Enterprise Benefit

In spite of rapid advances in multimedia and interactive technologies, enterprise users prefer to battle with email spam and overload rather than lose the benefits of communicating, collaborating and solving business tasks over email. Many aspects of email have significantly improved over time, but its overall integration with the enterprise environment remained practically the same. In this paper we describe and evaluate a light-weight approach to enterprise email communication analysis and information extraction. We provide several use cases exploiting the extracted information, such as the enrichment of emails with relevant contextual information, social network extraction and its subsequent search, creation of semantic objects as well as the relationship between email analysis and information extraction on one hand, and email protocols and email servers on the other. The proposed approach was partially tested on several small and medium enterprises (SMEs) and seems to be promising for enterprise interoperability and collaboration in SMEs that depend on emails to accomplish their daily business tasks

Natural Language Interfaces to Data

Recent advances in NLU and NLP have resulted in renewed interest in natural language interfaces to data, which provide an easy mechanism for non-technical users to access and query the data. While early systems evolved from keyword search and focused on simple factual queries, the complexity of both the input sentences as well as the generated SQL queries has evolved over time. More recently, there has also been a lot of focus on using conversational interfaces for data analytics, empowering a line of non-technical users with quick insights into the data. There are three main challenges in natural language querying (NLQ): (1) identifying the entities involved in the user utterance, (2) connecting the different entities in a meaningful way over the underlying data source to interpret user intents, and (3) generating a structured query in the form of SQL or SPARQL. There are two main approaches for interpreting a user's NLQ. Rule-based systems make use of semantic indices, ontologies, and KGs to identify the entities in the query, understand the intended relationships between those entities, and utilize grammars to generate the target queries. With the advances in deep learning (DL)-based language models, there have been many text-to-SQL approaches that try to interpret the query holistically using DL models. Hybrid approaches that utilize both rule-based techniques as well as DL models are also emerging by combining the strengths of both approaches. Conversational interfaces are the next natural step to one-shot NLQ by exploiting query context between multiple turns of conversation for disambiguation. In this article, we review the background technologies that are used in natural language interfaces, and survey the different approaches to NLQ. We also describe conversational interfaces for data analytics and discuss several benchmarks used for NLQ research and evaluation.Comment: The full version of this manuscript, as published by Foundations and Trends in Databases, is available at http://dx.doi.org/10.1561/190000007

Semantic Knowledge Graphs for the News: A Review

ICT platforms for news production, distribution, and consumption must exploit the ever-growing availability of digital data. These data originate from different sources and in different formats; they arrive at different velocities and in different volumes. Semantic knowledge graphs (KGs) is an established technique for integrating such heterogeneous information. It is therefore well-aligned with the needs of news producers and distributors, and it is likely to become increasingly important for the news industry. This article reviews the research on using semantic knowledge graphs for production, distribution, and consumption of news. The purpose is to present an overview of the field; to investigate what it means; and to suggest opportunities and needs for further research and development.publishedVersio