Semantic Web Machine Reading with FRED

Gangemi, Aldo; Presutti, Valentina; Recupero, Diego Reforgiato; Nuzzolese, Andrea Giovanni; Draicchio, Francesco; Mongiovì, Misael

doi:10.3233/sw-160240

Cited by 138 publications

(106 citation statements)

References 40 publications

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“…Nonetheless in many of these cases a preliminary classification by an automatic system may still alleviate the expert work load, e.g., by reducing the set of publications that need to be manually analysed. In addition, the performance of entity extraction and linking tools is steadily improving [4,18,47], allowing to extract increasingly better representations of research knowledge from scientific articles. Therefore, the number of research questions that can be addressed algorithmically may increase over the following years.…”

Section: Limitationsmentioning

confidence: 99%

Reducing the effort for systematic reviews in software engineering

Osborne

Muccini

Lago

et al. 2019

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Context. Systematic Reviews (SRs) are means for collecting and synthesizing evidence from the identification and analysis of relevant studies from multiple sources. To this aim, they use a well-defined methodology meant to mitigate the risks of biases and ensure repeatability for later updates. SRs, however, involve significant effort. Goal. The goal of this paper is to introduce a novel methodology that reduces the amount of manual tedious tasks involved in SRs while taking advantage of the value provided by human expertise. Method. Starting from current methodologies for SRs, we replaced the steps of keywording and data extraction with an automatic methodology for generating a domain ontology and classifying the primary studies. This methodology has been applied in the Software Engineering sub-area of Software Architecture and evaluated by human annotators. Results. The result is a novel Expert-Driven Automatic Methodology, EDAM, for assisting researchers in performing SRs. EDAM combines ontology-learning techniques and semantic technologies with the human-in-the-loop. The first (thanks to automation) fosters scalability, objectivity, reproducibility and granularity of the studies; the second allows tailoring to the specific focus of the study at hand and knowledge reuse from domain experts. We evaluated EDAM on the field of Software Architecture against six senior researchers. As a result, we found that the performance of the senior researchers in classifying papers was not statistically significantly different from EDAM. Conclusions. Thanks to automation of the less-creative steps in SRs, our methodology allows researchers to skip the tedious tasks of keywording and manually classifying primary studies, thus freeing effort for the analysis and the discussion.

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Section: Limitationsmentioning

confidence: 99%

Reducing the effort for systematic reviews in software engineering

Osborne

Muccini

Lago

et al. 2019

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“…• Input 3: Lexicons and ontologies play an important role in current approaches to semantic parsing and argumentation mining (e.g. the Boxer system has been extended with entity linking to external ontologies [1,38]). Furthermore, existing ontologies can serve as sources during the abductive step of coming up with candidate meaning postulates.…”

Section: Technological Feasibility and Implementation Approachesmentioning

confidence: 99%

A Computational-Hermeneutic Approach for Conceptual Explicitation

Fuenmayor

Benzmüller

2019

Model-Based Reasoning in Science and Technology

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We present a computer-supported approach for the logical analysis and conceptual explicitation of argumentative discourse. Computational hermeneutics harnesses recent progresses in automated reasoning for higher-order logics and aims at formalizing natural-language argumentative discourse using flexible combinations of expressive nonclassical logics. In doing so, it allows us to render explicit the tacit conceptualizations implicit in argumentative discursive practices. Our approach operates on networks of structured arguments and is iterative and two-layered. At one layer we search for logically correct formalizations for each of the individual arguments. At the next layer we select among those correct formalizations the ones which honor the argument's dialectic role, i.e. attacking or supporting other arguments as intended. We operate at these two layers in parallel and continuously rate sentences' formalizations by using, primarily, inferential adequacy criteria. An interpretive, logical theory will thus gradually evolve. This theory is composed of meaning postulates serving as explications for concepts playing a role in the analyzed arguments. Such a recursive, iterative approach to interpretation does justice to the inherent circularity of understanding: the whole is understood compositionally on the basis of its parts, while each part is understood only in the context of the whole (hermeneutic circle). We summarily discuss previous work on exemplary applications of human-in-the-loop computational hermeneutics in metaphysical discourse. We also discuss some of the main challenges involved in fully-automating our approach. By sketching some design ideas and reviewing relevant technologies, we argue for the technological feasibility of a highly-automated computational hermeneutics.

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“…The main techniques in ontology learning from text are linguisticsbased and statistics-based. Linguistics-based techniques involve exploitation of lexico-syntactic patterns [15], utilization of knowledge-rich resources such as linked data or ontologies [13], and syntactic transformation [24]. Statistics-based techniques involve relevance analysis [2], co-occurrence analysis, clustering, formal concept analysis, association rule mining [8] and deep learning [20].…”

Section: Ontology Learningmentioning

confidence: 99%

Extracting Ontological Knowledge from Textual Descriptions through Grammar-based Transformation

Mathews

Kumar

2017

Proceedings of the Knowledge Capture Conference

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Authoring of OWL-DL ontologies is intellectually challenging and to make this process simpler, many systems accept natural language text as input. A text-based ontology authoring approach can be successful only when it is combined with an effective method for extracting ontological axioms from text. Extracting axioms from unrestricted English input is a substantially challenging task due to the richness of the language. Controlled natural languages (CNLs) have been proposed in this context and these tend to be highly restrictive. In this paper, we propose a new CNL called TEDEI (TExtual DEscription Identifier) whose grammar is inspired by the different ways OWL-DL constructs are expressed in English. We built a system that transforms TEDEI sentences into corresponding OWL-DL axioms. Now, ambiguity due to different possible lexicalizations of sentences and semantic ambiguity present in sentences are challenges in this context. We find that the best way to handle these challenges is to construct axioms corresponding to alternative formalizations of the sentence so that the end-user can make an appropriate choice. The output is compared against human-authored axioms and in substantial number of cases, human-authored axiom is indeed one of the alternatives given by the system. The proposed system substantially enhances the types of sentence structures that can be used for ontology authoring.

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Semantic Web Machine Reading with FRED

Cited by 138 publications

References 40 publications

Reducing the effort for systematic reviews in software engineering

Reducing the effort for systematic reviews in software engineering

A Computational-Hermeneutic Approach for Conceptual Explicitation

Extracting Ontological Knowledge from Textual Descriptions through Grammar-based Transformation

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