Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100 times more data. We open-source our pretrained models and code.

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Universal Language Model Fine-tuning for Text Classification

Schema and ontology matching is a critical problem in many application domains, such as semantic web, schema/ontology integration, data warehouses, e-commerce, etc. Many different matching solutions have been proposed so far. In this paper we present a new classification of schema-based matching techniques that builds on the top of state of the art in both schema and ontology matching. Some innovations are in introducing new criteria which are based on (i) general properties of matching techniques, (ii) interpretation of input information, and (iii) the kind of input information. In particular, we distinguish between approximate and exact techniques at schema-level; and syntactic, semantic, and external techniques at element- and structure-level. Based on the classification proposed we overview some of the recent schema/ontology matching systems pointing which part of the solution space they cover. The proposed classification provides a common conceptual basis, and, hence, can be used for comparing different existing schema/ontology matching techniques and systems as well as for designing new ones, taking advantages of state of the art solutions.

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A survey of schema-based matching approaches

C4.5: Programs for Machine Learning (書評)

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.

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Processing Social Media Messages in Mass Emergency: A Survey

Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awareness) in all domains. Here, we present a geovisual analytics approach to supporting SA for crisis events using one source of social media, Twitter. Specifically, we focus on leveraging explicit and implicit geographic information for tweets, on developing place-time-theme indexing schemes that support overview+detail methods and that scale analytical capabilities to relatively large tweet volumes, and on providing visual interface methods to enable understanding of place, time, and theme components of evolving situations. Our approach is user-centered, using scenario-based design methods that include formal scenarios to guide design and validate implementation as well as a systematic claims analysis to justify design choices and provide a framework for future testing. The work is informed by a structured survey of practitioners and the end product of Phase-I development is demonstrated / validated through implementation in SensePlace2, a map-based, web application initially focused on tweets but extensible to other media.

SensePlace2: GeoTwitter analytics support for situational awareness

In case of emergencies (e.g., earthquakes, flooding), rapid responses are needed in order to address victims’ requests for help. Social media used around crises involves self-organizing behavior that can produce accurate results, often in advance of official communications. This allows affected population to send tweets or text messages, and hence, make them heard. The ability to classify tweets and text messages automatically, together with the ability to deliver the relevant information to the appropriate personnel are essential for enabling the personnel to timely and efficiently work to address the most urgent needs, and to understand the emergency situation better. In this study, we developed a reusable information technology infrastructure, called Enhanced Messaging for the Emergency Response Sector (EMERSE), which classifies and aggregates tweets and text messages about the Haiti disaster relief so that non-governmental organizations, relief workers, people in Haiti, and their friends and families can easily access them.

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Classifying text messages for the haiti earthquake

Most existing ontology mapping tools are inexact. Inexact ontology mapping rules, if not rectified, result in imprecision in the applications that use them. We describe a framework to probabilistically improve existing ontology mappings using a Bayesian Network. Omen, an Ontology Mapping ENhancer, is based on a set of meta-rules that captures the influence of the ontology structure and the existing matches to match nodes that are neighbours to matched nodes in the two ontologies. We have implemented a protype ontology matcher that can either map concepts across two input ontologies or enhance existing matches between ontology concepts. Preliminary experiments demonstrate that Omen enhances existing ontology mappings in our test cases.

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OMEN: a probabilistic ontology mapping tool

Methods, systems and processor-readable media for extracting and classifying location information utilizing social media messages and/or data thereof. The social media messages can be sampled from a social media database and the messages filtered based on a heuristic rule. A geolocation entity from the unstructured social media messages can be extracted utilizing a geolocation entity extracting module. The messages with the geoentities can be uploaded onto a crowd sourcing platform to manually annotate the messages with a label. A text classification model can be built and learned from the label utilizing a machine learning algorithm and the messages can be classified by a location classifier in order to extract the user location. The user location can then be transformed into a geocode so that a spatial search can be enabled and the distance between the locations can be easily calculated.

Method and system for extracting and classifying geolocation information utilizing electronic social media

Resolving semantic heterogeneity among information sources is a central problem in information interoperation, information integration, and information sharing among websites. Ontologies express the semantics of the terminology used in these websites. Semantic heterogeneity can be resolved by mapping ontologies from diverse sources. Mapping large ontologies manually is almost impossible and results in a number of errors of omission and commission. Therefore, automated ontology mapping algorithms are a must. However, most existing ontology mapping tools do not provide exact mappings. Rather, there is usually some degree of uncertainty. We describe a framework to improve existing ontology mappings using a Bayesian Network. Omen, an Ontology Mapping ENhancer uses a set of meta-rules that capture the influence of the ontology structure and the semantics of ontology relations and matches nodes that are neighbors of already matched nodes in the two ontologies. We have implemented a protype ontology matcher using probabilistic methods that can enhance existing matches between ontology concepts. Experiments demonstrate that Omen successfully identifies and enhances ontology mappings significantly.

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Anuj R. Jaiswal

Papers

SensePlace2: GeoTwitter analytics support for situational awareness

Classifying text messages for the haiti earthquake

OMEN: a probabilistic ontology mapping tool

Method and system for extracting and classifying geolocation information utilizing electronic social media

Ontology Mapping Discovery with Uncertainty