Background and foreground knowledge in dynamic ontology construction

Ontologies have become a key component in the Semantic Web and Knowledge management. One accepted goal is to construct ontologies from a domain specific set of texts. An ontology reflects the background knowledge used in writing and reading a text. However, a text is an act of knowledge maintenance, in that it re-enforces the background assumptions, alters links and associations in the ontology, and adds new concepts. This means that background knowledge is rarely expressed in a machine interpretable manner. When it is, it is usually in the conceptual boundaries of the domain, e.g. in textbooks or when ideas are borrowed into other domains. We argue that a partial solution to this lies in searching external resources such as specialized glossaries and the internet. We show that a random selection of concept pairs from the Gene Ontology do not occur in a relevant corpus of texts from the journal Nature. In contrast, a significant proportion can be found on the internet. Thus, we conclude that sources external to the domain corpus are necessary for the automatic construction of ontologies.

Automatic identification of best answers in online enquiry communities

Online communities are prime sources of information. The Web is rich with forums and Question Answering (Q&A) communities where people go to seek answers to all kinds of questions. Most systems employ manual answer-rating procedures to encourage people to provide quality answers and to help users locate the best answers in a given thread. However, in the datasets we collected from three online communities, we found that half their threads lacked best answer markings. This stresses the need for methods to assess the quality of available answers to: 1) provide automated ratings to fill in for, or support, manually assigned ones, and; 2) to assist users when browsing such answers by filtering in potential best answers. In this paper, we collected data from three online communities and converted it to RDF based on the SIOC ontology. We then explored an approach for predicting best answers using a combination of content, user, and thread features. We show how the influence of such features on predicting best answers differs across communities. Further we demonstrate how certain features unique to some of our community systems can boost predictability of best answers.

Semantic sentiment analysis of Twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. Apple product) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

Reflections on five years of evaluating semantic search systems

Evaluations of semantic search systems are generally small scale and ad hoc due to the lack of appropriate resources such as test collections, agreed performance criteria and independent judgements of performance. By analysing our work in building and evaluating semantic tools over the last five years, we conclude that the growth of the semantic web led to an improvement in the available resources and the consequent robustness of performance assessments. We propose two directions for continuing evaluation work: the development of extensible evaluation benchmarks and the use of logging parameters for evaluating individual components of search systems.

AquaLog : an ontology-driven question answering system for organizational semantic intranets

The semantic web vision is one in which rich, ontology-based semantic markup will become widely available. The availability of semantic markup on the web opens the way to novel, sophisticated forms of question answering. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input, and returns answers drawn from one or more knowledge bases (KBs). We say that AquaLog is portable because the configuration time required to customize the system for a particular ontology is negligible. AquaLog presents an elegant solution in which different strategies are combined together in a novel way. It makes use of the GATE NLP platform, string metric algorithms, WordNet and a novel ontology-based relation similarity service to make sense of user queries with respect to the target KB. Moreover it also includes a learning component, which ensures that the performance of the system improves over the time, in response to the particular community jargon used by end users.

The usability of semantic search tools:a review

The goal of semantic search is to improve on traditional search methods by exploiting the semantic metadata. In this paper, we argue that supporting iterative and exploratory search modes is important to the usability of all search systems. We also identify the types of semantic queries the users need to make, the issues concerning the search environment and the problems that are intrinsic to semantic search in particular. We then review the four modes of user interaction in existing semantic search systems, namely keyword-based, form-based, view-based and natural language-based systems. Future development should focus on multimodal search systems, which exploit the advantages of more than one mode of interaction, and on developing the search systems that can search heterogeneous semantic metadata on the open semantic Web.

Semantic annotation for knowledge management:requirements and a survey of the state of the art

While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents. In the emerging Semantic Web, search, interpretation and aggregation can be addressed by ontology-based semantic mark-up. In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems. This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress.

Enhancing visibility in EPCIS governing agri-food supply chains via linked pedigrees

Data integration for the purposes of tracking, tracing and transparency are important challenges in the agri-food supply chain. The Electronic Product Code Information Services (EPCIS) is an event-oriented GS1 standard that aims to enable tracking and tracing of products through the sharing of event-based datasets that encapsulate the Electronic Product Code (EPC). In this paper, the authors propose a framework that utilises events and EPCs in the generation of "linked pedigrees" - linked datasets that enable the sharing of traceability information about products as they move along the supply chain. The authors exploit two ontology based information models, EEM and CBVVocab within a distributed and decentralised framework that consumes real time EPCIS events as linked data to generate the linked pedigrees. The authors exemplify the usage of linked pedigrees within the fresh fruit and vegetables supply chain in the agri-food sector.

A knowledge driven approach towards the validation of externally acquired traceability datasets in supply chain business processes

The sharing of near real-time traceability knowledge in supply chains plays a central role in coordinating business operations and is a key driver for their success. However before traceability datasets received from external partners can be integrated with datasets generated internally within an organisation, they need to be validated against information recorded for the physical goods received as well as against bespoke rules defined to ensure uniformity, consistency and completeness within the supply chain. In this paper, we present a knowledge driven framework for the runtime validation of critical constraints on incoming traceability datasets encapuslated as EPCIS event-based linked pedigrees. Our constraints are defined using SPARQL queries and SPIN rules. We present a novel validation architecture based on the integration of Apache Storm framework for real time, distributed computation with popular Semantic Web/Linked data libraries and exemplify our methodology on an abstraction of the pharmaceutical supply chain.

Semantic patterns for sentiment analysis of Twitter

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter

Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words' sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure. © 2014 Springer International Publishing.

Preface

The Semantic Web has come a long way since its inception in 2001, especially in terms of technical development and research progress. However, adoption by non- technical practitioners is still an ongoing process, and in some areas this process is just now starting. Emergency response is an area where reliability and timeliness of information and technologies is of essence. Therefore it is quite natural that more widespread adoption in this area has not been seen until now, when Semantic Web technologies are mature enough to support the high requirements of the application area. Nevertheless, to leverage the full potential of Semantic Web research results for this application area, there is need for an arena where practitioners and researchers can meet and exchange ideas and results. Our intention is for this workshop, and hopefully coming workshops in the same series, to be such an arena for discussion. The Extended Semantic Web Conference (ESWC - formerly the European Semantic Web conference) is one of the major research conferences in the Semantic Web field, whereas this is a suitable location for this workshop in order to discuss the application of Semantic Web technology to our specific area of applications. Hence, we chose to arrange our first SMILE workshop at ESWC 2013. However, this workshop does not focus solely on semantic technologies for emergency response, but rather Semantic Web technologies in combination with technologies and principles for what is sometimes called the "social web". Social media has already been used successfully in many cases, as a tool for supporting emergency response. The aim of this workshop is therefore to take this to the next level and answer questions like: "how can we make sense of, and furthermore make use of, all the data that is produced by different kinds of social media platforms in an emergency situation?" For the first edition of this workshop the chairs collected the following main topics of interest: • Semantic Annotation for understanding the content and context of social media streams. • Integration of Social Media with Linked Data. • Interactive Interfaces and visual analytics methodologies for managing multiple large-scale, dynamic, evolving datasets. • Stream reasoning and event detection. • Social Data Mining. • Collaborative tools and services for Citizens, Organisations, Communities. • Privacy, ethics, trustworthiness and legal issues in the Social Semantic Web. • Use case analysis, with specific interest for use cases that involve the application of Social Media and Linked Data methodologies in real-life scenarios. All of these, applied in the context of: • Crisis and Disaster Management • Emergency Response • Security and Citizen Journalism The workshop received 6 high-quality paper submissions and based on a thorough review process, thanks to our program committee, the decision was made to accept four of these papers for the workshop (67% acceptance rate). These four papers can be found later in this proceedings volume. Three out of four of these papers particularly discuss the integration and analysis of social media data, using Semantic Web technologies, e.g. for detecting complex events in social media streams, for visualizing and analysing sentiments with respect to certain topics in social media, or for detecting small-scale incidents entirely through the use of social media information. Finally, the fourth paper presents an architecture for using Semantic Web technologies in resource management during a disaster. Additionally, the workshop featured an invited keynote speech by Dr. Tomi Kauppinen from Aalto university. Dr. Kauppinen shared experiences from his work on applying Semantic Web technologies to application fields such as geoinformatics and scientific research, i.e. so-called Linked Science, but also recent ideas and applications in the emergency response field. His input was also highly valuable for the roadmapping discussion, which was held at the end of the workshop. A separate summary of the roadmapping session can be found at the end of these proceedings. Finally, we would like to thank our invited speaker Dr. Tomi Kauppinen, all our program committee members, as well as the workshop chair of ESWC2013, Johanna Völker (University of Mannheim), for helping us to make this first SMILE workshop a highly interesting and successful event!

Modelling and linking transformations in EPCIS governing supply chain business processes

Supply chains comprise of complex processes spanning across multiple trading partners. The various operations involved generate large number of events that need to be integrated in order to enable internal and external traceability. Further, provenance of artifacts and agents involved in the supply chain operations is now a key traceability requirement. In this paper we propose a Semantic web/Linked data powered framework for the event based representation and analysis of supply chain activities governed by the EPCIS specification. We specifically show how a new EPCIS event type called "Transformation Event" can be semantically annotated using EEM - The EPCIS Event Model to generate linked data, that can be exploited for internal event based traceability in supply chains involving transformation of products. For integrating provenance with traceability, we propose a mapping from EEM to PROV-O. We exemplify our approach on an abstraction of the production processes that are part of the wine supply chain.

The ontology: Chimaera or Pegasus

In the context of the needs of the Semantic Web and Knowledge Management, we consider what the requirements are of ontologies. The ontology as an artifact of knowledge representation is in danger of becoming a Chimera. We present a series of facts concerning the foundations on which automated ontology construction must build. We discuss a number of different functions that an ontology seeks to fulfill, and also a wish list of ideal functions. Our objective is to stimulate discussion as to the real requirements of ontology engineering and take the view that only a selective and restricted set of requirements will enable the beast to fly.

Knowledge representation with ontologies: present challenges future possibilities

Ontologies have become the knowledge representation medium of choice in recent years for a range of computer science specialities including the Semantic Web, Agents, and Bio-informatics. There has been a great deal of research and development in this area combined with hype and reaction. This special issue is concerned with the limitations of ontologies and how these can be addressed, together with a consideration of how we can circumvent or go beyond these constraints. The introduction places the discussion in context and presents the papers included in this issue.