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.

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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

Text categorization-assignment of natural language texts to one or more predefined categories based on their content-is an important component in many information organization and management tasks.Different automatic learning algorithms for text categori-zation have different classification accuracy.Very accurate text classifiers can be learned automatically from training examples.

Inductive learning algorithms and representations for text categorization

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

The large volume of web content needs to be annotated by ontologies (called Semantic Annotation), and our empirical study shows that strong dependencies exist across different types of information (it means that identification of one kind of information can be used for identifying the other kind of information). Conditional Random Fields (CRFs) are the state-of-the-art approaches for modeling the dependencies to do better annotation. However, as information on a Web page is not necessarily linearly laid-out, the previous linear-chain CRFs have their limitations in semantic annotation. This paper is concerned with semantic annotation on hierarchically dependent data (hierarch-ical semantic annotation). We propose a Tree-structured Conditional Random Field (TCRF) model to better incorporate dependencies across the hierarchic-ally laid-out information. Methods for performing the tasks of model-parameter estimation and annotation in TCRFs have been proposed. Experimental results indicate that the proposed TCRFs for hierarchical semantic annotation can significantly outperform the existing linear-chain CRF model.

http://keg.cs.tsinghua.edu.cn/jietang/publications/ISWC06-Tang-et-al-TCRFs.pdf

Tree-structured conditional random fields for semantic annotation

With the Web content having been changed from homogeneity to heterogeneity, the recommendation becomes a more challenging issue. In this paper, we have investigated the recommendation problem on a general heterogeneous Web social network. We categorize the recommendation needs on it into two main scenarios: recommendation when a person is doing a search and recommendation when the person is browsing the information. We formalize the recommendation as a ranking problem over the heterogeneous network. Moreover, we propose using a random walk model to simultaneously ranking different types of objects and propose a pair-wise learning algorithm to learn the weight of each type of relationship in the model. Experimental results on two real-world data sets show that improvements can be obtained by comparing with the baseline methods.

/pdf/recommendation-over-a-heterogeneous-social-network-4l4mnn4eqh.pdf

Recommendation over a Heterogeneous Social Network

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.

iASA: learning to annotate the semantic web

The key point to reach interoperability over distributed ontologies is the mediation between them, called ontology mapping. Absolutely manually specified mapping is tedious and time consumption. Additional, how to ensure the consistency and deal with error prone in manual process, further how to maintain the mapping with the evolution of ontologies are all beyond manual work. Therefore, it is indeed necessary to automatically discover the mapping between ontologies so that mergence and translation of different ontology-based annotations become possible. Existing (semi-)automatic processing system are restricted to limited information, which depress the performance especially when the taxonomy structures have little overlapping or the instances have few commons. In this paper, based on Bayesian decision theory, we propose an approach called RiMOM to automatically discover mapping between ontologies. RiMOM treats the entire mapping problem as a decision problem instead of similarity problem in previous work. It explicitly and formally gives a complete decision model for ontology mapping. Based on shallow NLP, this paper also introduces a method to deal with instances heterogeneity, which is a long-standing problem for information processing. Experiments on real world data show that RiMOM is promising.

Bangyong Liang

Papers

Using Bayesian decision for ontology mapping

Tree-structured conditional random fields for semantic annotation

Recommendation over a Heterogeneous Social Network

iASA: learning to annotate the semantic web

Risk Minimization Based Ontology Mapping