Abstract The growing number of publicly available information sources makes it impossible for individuals to keep track of all the various opinions on one topic. The goal of ourFuzzy Believersystem presented in this paper is to extract and analyze statements of opinion from newspaper articles. Beliefs are modeled using the fuzzy set theory, applied after Natural Language Processing-based information extraction. The Fuzzy Believer models a human agent, deciding what statements to believe or reject based on a range of configurable strategies.

A software requirements specification (SRS) contains all the requirements for a system-to-be. These are typically separated into functional requirements (FR), which describe the features of the system under development, and the non-functional requirements (NFR), which include quality attributes, design constraints, among others. It is well known that NFRs have a large impact on the overall cost and time of the system development process, as they frequently describe cross-cutting concerns. In order to improve software development support, an automated analysis of SRS documents for different NFR types is required. Our work contains two significant contributions towards this goal: (1) A new gold standard corpus containing annotations for different NFR types, based on a requirements ontology, and (2) a Support Vector Machine (SVM) classifier to automatically categorize requirements sentences into different ontology classes. Results obtained from two different SRS corpora demonstrate the effectiveness of our approach.

The requirements engineering phase within a software project is a heavily knowledge-driven, collaborative process that typically involves the analysis and creation of a large number of textual artifacts. We know that requirements engineering has a large impact on the success of a project, yet sophisticated tool support, especially for small to mid-size enterprises, is still lacking. We present Reqwiki, a novel open source web-based approach based on a semantic wiki that includes natural language processing (NLP) assistants, which work collaboratively with humans on the requirements specification documents. We evaluated Reqwiki with a number of software engineers to investigate the impact of our novel semantic support on software requirements engineering. Our user studies prove that (i) software engineers unfamiliar with NLP can easily leverage these assistants and (ii) semantic assistants can help to significantly improve the quality of requirements specifications.

Personalization nowadays is a commodity in a broad spectrum of computer systems. Examples range from online shops recommending products identified based on the user's previous purchases to web search engines sorting search hits based on the user browsing history. The aim of such adaptive behavior is to help users to find relevant content easier and faster. However, there are a number of negative aspects of this behavior. Adaptive systems have been criticized for violating the usability principles of direct manipulation systems, namely controllability, predictability, transparency, and unobtrusiveness. In this paper, we propose an approach to controlling adaptive behavior in recommender systems. It allows users to get an overview of personalization effects, view the user profile that is used for personalization, and adjust the profile and personalization effects to their needs and preferences. We present this approach using an example of a personalized portal for biochemical literature, whose users are biochemists, biologists and genomicists. Also, we report on a user study evaluating the impacts of controllable personalization on the usefulness, usability, user satisfaction, transparency, and trustworthiness of personalized systems.

The number of scientific publications available in multiple repositories is huge and rapidly growing. Accessing this information is of critical importance to conducting research and designing experiments. However, retrieving data of particular interest for a specific research field in such a large volume of publications is often like looking for a needle in a haystack. We present a web platform that supports researchers in navigating and curating biochemical literature. Our platform provides a single-point of access to abstracts of publications harvested from multiple databases and supports further analysis of these abstracts. It also allows users to obtain a personalized view of the literature and its semantic analysis results.

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. CONTACT: [email protected].

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.

On first glance agent technology seems more like a hostile intruder into the database world. On the other hand, the two could easily complement each other, since agents carry out information processes whereas databases supply information to processes. Nonetheless, to view agent technology from a database perspective seems to question some of the basic paradigms of database technology, particularly the premise of semantic consistency of a database. The paper argues that the ensuing uncertainty in distributed databases can be modelled by beliefs, and develops the basic concepts for adjusting peer-to-peer databases to the individual beliefs in single nodes and collective beliefs in the entire distributed database.