After years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field.

/pdf/ontology-matching-state-of-the-art-and-future-challenges-486jer3je8.pdf

Ontology Matching: State of the Art and Future Challenges

Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: parsing brief and informal messages, handling information overload, and prioritizing different types of information found in messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages and highlight both their contributions and shortcomings. In addition, we examine their particularities, and methodically examine a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries. Research thus far has, to a large extent, produced methods to extract situational awareness information from social media. In this survey, we cover these various approaches, and highlight their benefits and shortcomings. We conclude with research challenges that go beyond situational awareness, and begin to look at supporting decision making and coordinating emergency-response actions.

/pdf/processing-social-media-messages-in-mass-emergency-a-survey-2wkyj3y36n.pdf

Processing Social Media Messages in Mass Emergency: A Survey

Ontology alignment identifies semantically matching entities in different ontologies. Various ontology alignment strategies have been proposed; however, few systems have explored how to automatically combine multiple strategies to improve the matching effectiveness. This paper presents a dynamic multistrategy ontology alignment framework, named RiMOM. The key insight in this framework is that similarity characteristics between ontologies may vary widely. We propose a systematic approach to quantitatively estimate the similarity characteristics for each alignment task and propose a strategy selection method to automatically combine the matching strategies based on two estimated factors. In the approach, we consider both textual and structural characteristics of ontologies. With RiMOM, we participated in the 2006 and 2007 campaigns of the Ontology Alignment Evaluation Initiative (OAEI). Our system is among the top three performers in benchmark data sets.

/pdf/rimom-a-dynamic-multistrategy-ontology-alignment-framework-4y414jq7zp.pdf

RiMOM: A Dynamic Multistrategy Ontology Alignment Framework

http://disi.unitn.it/~p2p/RelatedWork/Matching/ontology_matching_with_semantic_verification.pdf

Ontology matching with semantic verification

We present a literature review regarding articles on ontology matching published in the last decade.It serves the purpose of offering an up-to-date review of the field and showing its evolution trends.Over 1600 papers have been sorted according to a classification framework that we have defined.This framework helps in identifying the distribution of the load work in the last decade.Practitioners have been consulted to contrast and validate the results of the review. The amount of research papers published nowadays related to ontology matching is remarkable and we believe that reflects the growing interest of the research community. However, for new practitioners that approach the field, this amount of information might seem overwhelming. Therefore, the purpose of this work is to help in guiding new practitioners get a general idea on the state of the field and to determine possible research lines.To do so, we first perform a literature review of the field in the last decade by means of an online search. The articles retrieved are sorted using a classification framework that we propose, and the different categories are revised and analyzed. The information in this review is extended and supported by the results obtained by a survey that we have designed and conducted among the practitioners.

http://disi.unitn.it/~p2p/RelatedWork/Matching/Cerdeira-Ontology%20Matching-2015.pdf

Ontology matching

http://keg.cs.tsinghua.edu.cn/jietang/publications/JWS06-Tang-et-al-Using%20Bayesian%20Decision%20to%20Mapping%20Ontology.pdf

Using Bayesian decision for ontology mapping

With the advent of the Semantic Web, there is a great need to upgrade existing web content to semantic web content. This can be accomplished through semantic annotations. Unfortunately, manual annotation is tedious, time consuming and error-prone. In this paper, we propose a tool, called iASA, that learns to automatically annotate web documents according to an ontology. iASA is based on the combination of information extraction (specifically, the Similarity-based Rule Learner—SRL) and machine learning techniques. Using linguistic knowledge and optimal dynamic window size, SRL produces annotation rules of better quality than comparable semantic annotation systems. Similarity-based learning efficiently reduces the search space by avoiding pseudo rule generalization. In the annotation phase, iASA exploits ontology knowledge to refine the annotation it proposes. Moreover, our annotation algorithm exploits machine learning methods to correctly select instances and to predict missing instances. Finally, iASA provides an explanation component that explains the nature of the learner and annotator to the user. Explanations can greatly help users understand the rule induction and annotation process, so that they can focus on correcting rules and annotations quickly. Experimental results show that iASA can reach high accuracy quickly.

Xiaotong Huang

Papers

Using Bayesian decision for ontology mapping

iASA: learning to annotate the semantic web

Surface guided radiation therapy with an innovative open-face mask and mouth bite: patient motion management in brain stereotactic radiotherapy.