Recent posts

OrganismTagger: detection, normalization and grounding of organism entities in biomedical documents

Abstract

OrganismTagger example result annotationOrganismTagger example result annotation
Motivation: Semantic tagging of organism mentions in full-text articles is an important part of literature mining and semantic enrichment solutions. Tagged organism mentions also play a pivotal role in disambiguating other entities in a text, such as proteins. A high-precision organism tagging system must be able to detect the numerous forms of organism mentions, including common names as well as the traditional taxonomic groups: genus, species and strains. In addition, such a system must resolve abbreviations and acronyms, assign the scientific name and if possible link the detected mention to the NCBI Taxonomy database for further semantic queries and literature navigation.

Results: We present the OrganismTagger, a hybrid rule-based/machine learning system to extract organism mentions from the literature. It includes tools for automatically generating lexical and ontological resources from a copy of the NCBI Taxonomy database, thereby facilitating system updates by end users. Its novel ontology-based resources can also be reused in other semantic mining and linked data tasks. Each detected organism mention is normalized to a canonical name through the resolution of acronyms and abbreviations and subsequently grounded with an NCBI Taxonomy database ID. In particular, our system combines a novel machine-learning approach with rule-based and lexical methods for detecting strain mentions in documents. On our manually annotated OT corpus, the OrganismTagger achieves a precision of 95%, a recall of 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linnaeus-100, the results show a precision of 99%, recall of 97% and grounding accuracy of 97.4%.

Availability: The OrganismTagger, including supporting tools, resources, training data and manual annotations, as well as end user and developer documentation, is freely available under an open-source license at http://www.semanticsoftware.info/organism-tagger.

Intelligent Software Development Environments: Integrating Natural Language Processing with the Eclipse Platform

Abstract

Software engineers need to be able to create, modify, and analyze knowledge stored in software artifacts. A significant amount of these artifacts contain natural language, like version control commit messages, source code comments, or bug reports. Integrated software development environments (IDEs) are widely used, but they are only concerned with structured software artifacts – they do not offer support for analyzing unstructured natural language and relating this knowledge with the source code. We present an integration of natural language processing capabilities into the Eclipse framework, a widely used software IDE. It allows to execute NLP analysis pipelines through the Semantic Assistants framework, a service-oriented architecture for brokering NLP services based on GATE. We demonstrate a number of semantic analysis services helpful in software engineering tasks, and evaluate one task in detail, the quality analysis of source code comments.

Integrating Wiki Systems, Natural Language Processing, and Semantic Technologies for Cultural Heritage Data Management

Abstract

Modern documents can easily be structured and augmented to have the characteristics of a semantic knowledge base. Many older documents may also hold a trove of knowledge that would deserve to be organized as such a knowledge base. In this chapter, we show that modern semantic technologies offer the means to make these heritage documents accessible by transforming them into a semantic knowledge base. Using techniques from natural language processing and Semantic Computing, we automatically populate an ontology. Additionally, all content is made accessible in a user-friendly Wiki interface, combining original text with NLP-derived metadata and adding annotation capabilities for collaborative use. All these functions are combined into a single, cohesive system architecture that addresses the different requirements from end users, software engineering aspects, and knowledge discovery paradigms. The ideas were implemented and tested with a volume from the historic Encyclopedia of Architecture and a number of different user groups.

Algorithms and semantic infrastructure for mutation impact extraction and grounding

Abstract

Mutation Impact OntologyMutation Impact Ontology

Background

Mutation impact extraction is a hitherto unaccomplished task in state of the art mutation extraction systems. Protein mutations and their impacts on protein properties are hidden in scientific literature, making them poorly accessible for protein engineers and inaccessible for phenotype-prediction systems that currently depend on manually curated genomic variation databases.

Results

We present the first rule-based approach for the extraction of mutation impacts on protein properties, categorizing their directionality as positive, negative or neutral. Furthermore protein and mutation mentions are grounded to their respective UniProtKB IDs and selected protein properties, namely protein functions to concepts found in the Gene Ontology. The extracted entities are populated to an OWL-DL Mutation Impact ontology facilitating complex querying for mutation impacts using SPARQL. We illustrate retrieval of proteins and mutant sequences for a given direction of impact on specific protein properties. Moreover we provide programmatic access to the data through semantic web services using the SADI (Semantic Automated Discovery and Integration) framework.

Conclusion

We address the problem of access to legacy mutation data in unstructured form through the creation of novel mutation impact extraction methods which are evaluated on a corpus of full-text articles on haloalkane dehalogenases, tagged by domain experts. Our approaches show state of the art levels of precision and recall for Mutation Grounding and respectable level of precision but lower recall for the task of Mutant-Impact relation extraction. The system is deployed using text mining and semantic web technologies with the goal of publishing to a broad spectrum of consumers.

SE-Advisor

The SE-ADVISOR tool presents a novel approach to support software evolution, by integrating maintenance relevant knowledge resources, processes, and their constituents. We demonstrate how our SE-ADVISOR environment can provide contextual guidance during typical maintenance tasks through the use of ontological queries and reasoning services.

Semantic Assistants: SOA for Text Mining

With the rapidly growing amount of information available, employees spend an ever-increasing proportion of their time searching for the right information. Information overload has become a serious threat to productivity. We address this challenge with a service-oriented architecture that integrates semantic natural language processing services into desktop applications.

Beyond Information Silos — An Omnipresent Approach to Software Evolution

Abstract

Nowadays, software development and maintenance are highly distributed processes that involve a multitude of supporting tools and resources. Knowledge relevant for a particular software maintenance task is typically dispersed over a wide range of artifacts in different representational formats and at different abstraction levels, resulting in isolated 'information silos'. An increasing number of task-specific software tools aim to support developers, but this often results in additional challenges, as not every project member can be familiar with every tool and its applicability for a given problem. Furthermore, historical knowledge about successfully performed modifications is lost, since only the result is recorded in versioning systems, but not how a developer arrived at the solution. In this research, we introduce conceptual models for the software domain that go beyond existing program and tool models, by including maintenance processes and their constituents. The models are supported by a pro-active, ambient, knowledge-based environment that integrates users, tasks, tools, and resources, as well as processes and history-specific information. Given this ambient environment, we demonstrate how maintainers can be supported with contextual guidance during typical maintenance tasks through the use of ontology queries and reasoning services.

Converting a Historical Architecture Encyclopedia into a Semantic Knowledge Base

Abstract

Digitizing a historical document using ontologies and natural language processing techniques can transform it from arcane text to a useful knowledge base.

Proceedings of the Workshop New Challenges for NLP Frameworks (NLPFrameworks 2010)

Background

Natural language processing frameworks like GATE and UIMA have significantly changed the way NLP applications are designed, developed, and deployed. Features such as component-based design, test-driven development, and resource meta-descriptions now routinely provide higher robustness, better reusability, faster deployment, and improved scalability. They have become the staple of both NLP research and industrial application, fostering a new generation of NLP users and developers.

These are the proceedings of the workshop New Challenges for NLP Frameworks (NLPFrameworks 2010), held in conjunction with LREC 2010, which brought together users and developers of major NLP frameworks.

Ontology-Based Extraction and Summarization of Protein Mutation Impact Information

Introduction

Poster at BioNLP 2010: Ontology-Based Extraction and Summarization of Protein Mutation Impact InformationPoster at BioNLP 2010: Ontology-Based Extraction and Summarization of Protein Mutation Impact InformationNLP methods for extracting mutation information from the bibliome have become an important new research area within bio-NLP, as manually curated databases, like the Protein Mutant Database (PMD) (Kawabata et al., 1999), cannot keep up with the rapid pace of mutation research. However, while significant progress has been made with respect to mutation detection, the automated extraction of the impacts of these mutations has so far not been targeted. In this paper, we describe the first work to automatically summarize impact information from protein mutations. Our approach is based on populating an OWL-DL ontology with impact information, which can then be queried to provide structured information, including a summary.

Automatic Quality Assessment of Source Code Comments: The JavadocMiner

Abstract

An important software engineering artefact used by developers and maintainers to assist in software comprehension and maintenance is source code documentation. It provides insights that help software engineers to effectively perform their tasks, and therefore ensuring the quality of the documentation is extremely important. Inline documentation is at the forefront of explaining a programmer's original intentions for a given implementation. Since this documentation is written in natural language, ensuring its quality needs to be performed manually. In this paper, we present an effective and automated approach for assessing the quality of inline documentation using a set of heuristics, targeting both quality of language and consistency between source code and its comments. We apply our tool to the different modules of two open source applications (ArgoUML and Eclipse), and correlate the results returned by the analysis with bug defects reported for the individual modules in order to determine connections between documentation and code quality.

Semantic Content Access using Domain-Independent NLP Ontologies

Abstract

We present a lightweight, user-centred approach for document navigation and analysis that is based on an ontology of text mining results. This allows us to bring the result of existing text mining pipelines directly to end users. Our approach is domain-independent and relies on existing NLP analysis tasks such as automatic multi-document summarization, clustering, question-answering, and opinion mining. Users can interactively trigger semantic processing services for tasks such as analyzing product reviews, daily news, or other document sets.

Leverage of OWL-DL axioms in a Contact Centre for Technical Product Support

Abstract

Real-time access to complex knowledge is a business driver in the contact centre environment. In this paper we outline for the domain of telecom technical product support a knowledge sharing paradigm in which a desktop client annotates named entities in technical documents with canonical names, class names or relevant class axioms, derived from an ontology by means of a web services framework. We described the system and its core components; OWL-DL telecom hardware ontology, ontological-natural language processing pipeline, an ontology axiom?extractor; and the Semantic Assistants framework.

Flexible Ontology Population from Text: The OwlExporter

Abstract

Ontology population from text is becoming increasingly important for NLP applications. Ontologies in OWL format provide for a standardized means of modeling, querying, and reasoning over large knowledge bases. Populated from natural language texts, they offer significant advantages over traditional export formats, such as plain XML. The development of text analysis systems has been greatly facilitated by modern NLP frameworks, such as the General Architecture for Text Engineering (GATE). However, ontology population is not currently supported by a standard component. We developed a GATE resource called the OwlExporter that allows to easily map existing NLP analysis pipelines to OWL ontologies, thereby allowing language engineers to create ontology population systems without requiring extensive knowledge of ontology APIs. A particular feature of our approach is the concurrent population and linking of a domain- and NLP-ontology, including NLP-specific features such as safe reasoning over coreference chains.

Generating an NLP Corpus from Java Source Code: The SSL Javadoc Doclet

Abstract

Source code contains a large amount of natural language text, particularly in the form of comments, which makes it an emerging target of text analysis techniques. Due to the mix with program code, it is difficult to process source code comments directly within NLP frameworks such as GATE. Within this work we present an effective means for generating a corpus using information found in source code and in-line documentation, by developing a custom doclet for the Javadoc tool. The generated corpus uses a schema that is easily processed by NLP applications, which allows language engineers to focus their efforts on text analysis tasks, like automatic quality control of source code comments. The SSLDoclet is available as open source software.

Predicate-Argument EXtractor (PAX)

Abstract

Screenshot of MultiPAX resultsScreenshot of MultiPAX resultsIn this paper, we describe the open source GATE component PAX for extracting predicate-argument structures (PASs). PASs are used in various contexts to represent relations within a sentence structure. Different ``semantic'' parsers extract relational information from sentences but there exists no common format to store this information. Our predicate-argument extractor component (PAX) takes the annotations generated by selected parsers and transforms the parsers' results to predicate-argument structures represented as triples (subject-verb-object). This allows downstream components in an analysis pipeline to process PAS triples independent of the deployed parser, as well as combine the results from several parsers within a single pipeline.

Believe It or Not: Solving the TAC 2009 Textual Entailment Tasks through an Artificial Believer System

Abstract

The Text Analysis Conference (TAC) 2009 competition featured a new textual entailment search task, which extends the 2008 textual entailment task. The goal is to find information in a set of documents that are entailed from a given statement. Rather than designing a system specifically for this task, we investigated the adaptation of an existing artificial believer system to solve this task. The results show that this is indeed possible, and furthermore allows to recast the existing, divergent tasks of textual entailment and automatic summarization under a common umbrella.

A Quality Perspective of Evolvability Using Semantic Analysis

Abstract

Software development and maintenance are highly distributed processes that involve a multitude of supporting tools and resources. Knowledge relevant to these resources is typically dispersed over a wide range of artifacts, representation formats, and abstraction levels. In order to stay competitive, organizations are often required to assess and provide evidence that their software meets the expected requirements. In our research, we focus on assessing non-functional quality requirements, specifically evolvability, through semantic modeling of relevant software artifacts. We introduce our SE-Advisor that supports the integration of knowledge resources typically found in software ecosystems by providing a unified ontological representation. We further illustrate how our SE-Advisor takes advantage of this unified representation to support the analysis and assessment of different types of quality attributes related to the evolvability of software ecosystems.

A Belief Revision Approach to Textual Entailment Recognition

Abstract

An artificial believer has to recognize textual entailment to categorize beliefs. We describe our system – the Fuzzy Believer system – and its application to the TAC/RTE three-way task.

ERSS at TAC 2008

Abstract

An Automatically Generated SummaryAn Automatically Generated Summary
ERSS 2008 attempted to rectify certain issues of ERSS 2007. The improvements to readability, however, do not re?ect in signi?cant score increases, and in fact the system fell in overall ranking. While we have not concluded our analysis, we present some preliminary observations here.

Semantic Assistants – User-Centric Natural Language Processing Services for Desktop Clients

Abstract

Semantic Assistants Workflow OverviewSemantic Assistants Workflow OverviewToday's knowledge workers have to spend a large amount of time and manual effort on creating, analyzing, and modifying textual content. While more advanced semantically-oriented analysis techniques have been developed in recent years, they have not yet found their way into commonly used desktop clients, be they generic (e.g., word processors, email clients) or domain-specific (e.g., software IDEs, biological tools). Instead of forcing the user to leave his current context and use an external application, we propose a ``Semantic Assistants'' approach, where semantic analysis services relevant for the user's current task are offered directly within a desktop application. Our approach relies on an OWL ontology model for context and service information and integrates external natural language processing (NLP) pipelines through W3C Web services.

Story-driven Approach to Software Evolution


Abstract

From a maintenance perspective, only software that is well understood can evolve in a controlled and high-quality manner. Software evolution itself is a knowledge-driven process that requires the use and integration of different knowledge resources. The authors present a formal representation of an existing process model to support the evolution of software systems by representing knowledge resources and the process model using a common representation based on ontologies and description logics. This formal representation supports the use of reasoning services across different knowledge resources, allowing for the inference of explicit and implicit relations among them. Furthermore, an interactive story metaphor is introduced to guide maintainers during their software evolution activities and to model the interactions between the users, knowledge resources and process model.

Ontological Approach for the Semantic Recovery of Traceability Links between Software Artifacts

Sudoku

Abstract

Traceability links provide support for software engineers in understanding relations and dependencies among software artefacts created during the software development process. The authors focus on re-establishing traceability links between existing source code and documentation to support software maintenance. They present a novel approach that addresses this issue by creating formal ontological representations for both documentation and source code artefacts. Their approach recovers traceability links at the semantic level, utilising structural and semantic information found in various software artefacts. These linked ontologies are supported by ontology reasoners to allow the inference of implicit relations among these software artefacts.

A General Architecture for Connecting NLP Frameworks and Desktop Clients using Web Services


Abstract

Despite impressive advances in the development of generic NLP frameworks, content-specific text mining algorithms, and NLP services, little progress has been made in enhancing existing end-user clients with text analysis capabilities. To overcome this software engineering gap between desktop environments and text analysis frameworks, we developed an open service-oriented architecture, based on Semantic Web ontologies and W3C Web services, which makes it possible to easily integrate any NLP service into client applications.

Semantic Technologies in System Maintenance (STSM 2008)


Abstract

This paper gives a brief overview of the International Workshop on Semantic Technologies in System Maintenance. It describes a number of semantic technologies (e.g., ontologies, text mining, and knowledge integration techniques) and identifies diverse tasks in software maintenance where the use of semantic technologies can be beneficial, such as traceability, system comprehension, software artifact analysis, and information integration.