An overview of the Applicationsof Natural Language to Information Systems
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paper_title: Semantic (Web) Technology In Action: Ontology Driven Information Systems for Search, Integration and Analysis

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Semantics is seen as the key ingredient in the next phase of the Web infrastructure as well as the next generation of information systems applications. In this context, we review some of the reservations expressed about the viability of the Semantic Web. We respond to these by identifying a Semantic Technology that supports the key capabilities also needed to realize the Semantic Web vision, namely representing, acquiring and utilizing knowledge. Given that scalability is a key challenge, we briefly review our observations from developing three classes of real world applications and corresponding technology components: search/browsing, integration, and analytics. We distinguish this proven technology from some parts of the Semantic Web approach and offer subjective remarks which we hope will foster additional debate.
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paper_title: The internet of services: Global industrialization of information intensive services

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Web Services have experienced great interest during the last years as they were expected to act as enablers of seamless application-to-application integration both within company boundaries and on a global scale. However, a truly global mesh of interoperable services has not yet become reality due to various reasons. Novel technologies and design principles are now about to emerge which shift the Internet towards a major inflection point: It is envisioned to evolve as a global platform for the retrieval, combination and utilization of interoperable resources. In this article, we contrast the Internet of today and the envisioned Internet of Services along three major categories: Stakeholders, Applications and finally Technology. We conclude with an analysis of the important role this emerging Internet of Services will play as technological enabler of information services industrialization: By reducing different kinds of waste, the Internet of Services acts as enabler of Lean Service Management
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paper_title: COMPENDIUM: a text summarization system for generating abstracts of research papers

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This paper presents COMPENDIUM, a text summarization system, which has achieved good results in extractive summarization. Therefore, our main goal in this research is to extend it, suggesting a new approach for generating abstractive-oriented summaries of research papers. We conduct a preliminary analysis where we compare the extractive version of COMPENDIUM (COMPENDIUME) with the new abstractiveoriented approach (COMPENDIUME-A). The final summaries are evaluated according to three criteria (content, topic, and user satisfaction) and, from the results obtained, we can conclude that the use of COMPENDIUM is appropriate for producing summaries of research papers automatically, going beyond the simple selection of sentences.
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paper_title: Querying Linked Data using Semantic Relatedness: A Vocabulary Independent Approach

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Linked Data brings the promise of incorporating a new dimension to the Web where the availability of Web-scale data can determine a paradigmatic transformation of the Web and its applications. However, together with its opportunities, Linked Data brings inherent challenges in the way users and applications consume the available data. Users consuming Linked Data on the Web, or on corporate intranets, should be able to search and query data spread over potentially a large number of heterogeneous, complex and distributed datasets. Ideally, a query mechanism for Linked Data should abstract users from the representation of data. This work focuses on the investigation of a vocabulary independent natural language query mechanism for Linked Data, using an approach based on the combination of entity search, a Wikipediabased semantic relatedness measure and spreading activation. The combination of these three elements in a query mechanism for Linked Data is a new contribution in the space. Wikipedia-based relatedness measures address existing limitations of existing works which are based on similarity measures/term expansion based on WordNet. Experimental results using the query mechanism to answer 50 natural language queries over DBPedia achieved a mean reciprocal rank of 61.4%, an average precision of 48.7% and average recall of 57.2%, answering 70% of the queries.
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paper_title: Extracting Explicit and Implicit Causal Relations from Sparse, Domain-Specific Texts

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Various supervised algorithms for mining causal relations from large corpora exist. These algorithms have focused on relations explicitly expressed with causal verbs, e.g. "to cause". However, the challenges of extracting causal relations from domain-specific texts have been overlooked. Domain-specific texts are rife with causal relations that are implicitly expressed using verbal and non-verbal patterns, e.g. "reduce", "drop in", "due to". Also, readily-available resources to support supervised algorithms are inexistent in most domains. To address these challenges, we present a novel approach for causal relation extraction. Our approach is minimally-supervised, alleviating the need for annotated data. Also, it identifies both explicit and implicit causal relations. Evaluation results revealed that our technique achieves state-of-the-art performance in extracting causal relations from domain-specific, sparse texts. The results also indicate that many of the domain-specific relations were unclassifiable in existing taxonomies of causality.
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paper_title: EmotiNet: a knowledge base for emotion detection in text built on the appraisal theories

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The automatic detection of emotions is a difficult task in Artificial Intelligence. In the field of Natural Language Processing, the challenge of automatically detecting emotion from text has been tackled from many perspectives. Nonetheless, the majority of the approaches contemplated only the word level. Due to the fact that emotion is most of the times not expressed through specific words, but by evoking situations that have a commonsense affective meaning, the performance of existing systems is low. This article presents the EmotiNet knowledge base - a resource for the detection of emotion from text based on commonsense knowledge on concepts, their interaction and their affective consequence. The core of the resource is built from a set of self-reported affective situations and extended with external sources of commonsense knowledge on emotion-triggering concepts. The results of the preliminary evaluations show that the approach is appropriate for capturing and storing the structure and the semantics of real situations and predict the emotional responses triggered by actions presented in text.
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Title: An Overview of the Applications of Natural Language to Information Systems

Section 1: Introduction
Description 1: Write an introduction to the paper outlining the historical context, the growth of Human Language Technologies (HLT) in information systems, and the purpose of the NLDB conference series.

Section 2: Topics of Interest for Researchers in Natural Language and Information Systems
Description 2: Discuss the main research topics and areas of interest that are a priority for the NLDB community, including Natural Language for Web Information-Intensive Services, Conceptual Modeling, and others.

Section 3: Special Issue Articles and Their Contributions
Description 3: Introduce the special issue articles selected from the NLDB conference, summarizing the key contributions and findings of each paper included in the issue.

Section 4: EmotiNet. A Knowledge Base for Emotion Detection in Text Built on the Appraisal Theories
Description 4: Describe the EmotiNet system for detecting emotions in text based on appraisal theories, including the methodology and evaluation results.

Section 5: Querying Linked Data Using Semantic Relatedness: A Vocabulary Independent Approach
Description 5: Summarize the new method for querying Linked Data using semantic relatedness and the Semantic Relatedness Spreading Activation algorithm introduced by researchers from Ireland and Brazil.

Section 6: Extracting Explicit and Implicit Causal Relations from Sparse, Domain-Specific Texts
Description 6: Explain the approach for mining causal relations from domain-specific texts using a minimally supervised algorithm, the benefits of using a broad-coverage corpus, and the evaluation outcomes.

Section 7: COMPENDIUM: A Text Summarization System for Generating Abstracts of Research Papers
Description 7: Outline the COMPENDIUM text summarization system, its methods for generating abstracts, and the evaluation of extractive versus abstractive summarization techniques, along with user satisfaction.