Competitor data constitutes information significantly valuable for many business applications. Meltwater provides users with access to a large Company Information System (CIS), Owler, which contains competitor pairs and other useful information about companies. Meltwater has been seeking a practical solution to discover more competitor pairs in Owler. The first attempt, a fully-manual workflow (called MW_Manual) for finding more competitor pairs in Owler consisted of two manual steps: a filtering step that excludes obvious non-competitor company pairs, and a further inspection process that inspects each left company pair after the filtering step. MW_Manual was cost prohibitive because the results of the filtering step contained too many non-competitor pairs. Inspecting such non-competitor pairs caused an overhead to the overall workload. To reduce the manual workload, especially the required human effort in the manual inspection process, Meltwater has transformed MW_Manual into a semi-automatic workflow (called MW_CPFilter) by replacing the manual filtering with an automatic yet more precise process that adopts a system called CPFilter. This paper presents CPFilter, a system used in the filtering process of MW_CPFilter. CPFilter automatically pre-computes likely competitor pairs from existing competitor pairs in Owler. CPFilter combines (i) the generation of new competitor candidate pairs by inference from existing competitors and other company-specific knowledge, with (ii) the validation of each candidate competitor pair of two companies by checking whether or not empirical evidence that indicates the competitor relationships of these two companies can be found. CPFilter has three key advantages compared with the manual filtering process and previous works: (i) it resulted in a high workload reduction rate of 0.81, (ii) it is domain-independent so that it can be applied to different sectors in Owler, and (iii) its results are explainable so that humans can easily understand its results.

Data-driven websites are mostly accessed through search interfaces. Such sites follow a common publishing pattern that, surprisingly, has not been fully exploited for unsupervised data extraction yet: the result of a search is presented as a paginated list of result records. Each result record contains the main attributes about one single object, and links to a page dedicated to the details of that object.

The web is the largest bulletin board of the world. Events of all types, from flight arrivals to business meetings, are announced on this board. Tracking and reacting to such event announcements, however, is a tedious manual task, only slightly alleviated by email or similar notifications. Announcements are published with human readers in mind, and updates or delayed announcements are frequent. These characteristics have hampered attempts at automatic tracking. P ea CE provides the first integrated framework for event processing on top of web event ads, consisting of event extraction, complex event processing, and action execution in response to these events. Given a schema of the events to be tracked, the framework populates this schema by extracting events from announcement sources. This extraction is performed by little programs called wrappers that produce the events including updates and retractions. P ea CE then queries these events to detect complex events, often combining announcements from multiple sources. To deal with updates and delayed announcements, P ea CE’s schemas are bitemporal, to distinguish between occurrence and detection time. This allows complex event specifications to track updates and to react upon differences in occurrence and detection time. In case of new, changing, or deleted events, P ea CE allows one to execute actions, such as tweeting or sending out email notifications. Actions are typically specified as web interactions, for example, to fill and submit a form with attributes of the triggering event. Our evaluation shows that P ea CE’s processing is dominated by the time needed for accessing the web to extract events and perform actions, allotting to 97.4%. Thus, P ea CE requires only 2.6% overhead, and therefore, the complex event processor scales well even with moderate resources. We further show that simple and reasonable restrictions on complex event specifications and the timing of constituent events suffice to guarantee that P ea CE only requires a constant buffer to process arbitrarily many event announcements.

The web is overflowing with implicitly structured data, spread over hundreds of thousands of sites, hidden deep behind search forms, or siloed in marketplaces, only accessible as HTML. Automatic extraction of structured data at the scale of thousands of websites has long proven elusive, despite its central role in the "web of data". Through an extensive evaluation spanning over 10000 web sites from multiple application domains, we show that automatic, yet accurate full-site extraction is no longer a distant dream. diadem is the first automatic full-site extraction system that is able to extract structured data from different domains at very high accuracy. It combines automated exploration of websites, identification of relevant data, and induction of exhaustive wrappers. Automating these components is the first challenge. diadem overcomes this challenge by combining phenomenological and ontological knowledge. Integrating these components is the second challenge. diadem overcomes this challenge through a self-adaptive network of relational transducers that produces effective wrappers for a wide variety of websites. Our extensive and publicly available evaluation shows that, for more than 90% of sites from three domains, diadem obtains an effective wrapper that extracts all relevant data with 97% average precision. diadem also tolerates noisy entity recognisers, and its components individually outperform comparable approaches.

Forms are our gates to the web. They enable us to access the deep content of web sites. Automatic form understanding unlocks this content for applications ranging from crawlers to meta-search engines and is essential for improving usability and accessibility of the web. Form understanding has received surprisingly little attention other than as component in specific applications such as crawlers. No comprehensive approach to form understanding exists and previous works disagree even in the definition of the problem. In this paper, we present OPAL, the first comprehensive approach to form understanding. We identify form labeling and form interpretation as the two main tasks involved in form understanding. On both problems OPAL pushes the state of the art: For form labeling, it combines signals from the text, structure, and visual rendering of a web page, yielding robust characterisations of common design patterns. In extensive experiments on the ICQ and TEL-8 benchmarks and a set of 200 modern web forms OPAL outperforms previous approaches by a significant margin. For form interpretation, we introduce a template language to describe frequent form patterns. These two parts of OPAL combined yield form understanding with near perfect accuracy (> 98%).

Abstract The seaport of Gioia Tauro is the largest transshipment terminal of the Mediterranean coast. A crucial management task for the companies operating in the seaport is team-building: the problem of properly allocating the available personnel for serving the incoming ships. Teams have to be carefully arranged in order to meet several constraints, such as allocation of employees with appropriate skills, fair distribution of the working load, and turnover of the heavy/dangerous roles. This makes team-building a hard and expensive task requiring several hours of manual preparation per day. In this paper we present a system based on Answer Set Programming for the automatic generation of the teams of employees in the seaport of Gioia Tauro. The system is currently exploited in the Gioia Tauro seaport by ICO BLG, a company specialized in automobile logistics.

Although deep web analysis has been studied extensively, there is no succinct formalism to describe user interactions with AJAX-enabled web applications.

In this paper we present a successful application of logic programming for e-tourism: the iTravel system. The system exploits two technologies that are based on the state-of-the-art computational logic system DLV: (i) a system for ontology representa

Enterprise/Corporate ontologies are widely adopted to conceptualize business enterprise information. In this area, the semantic peculiarities of Answer Set Programming (ASP), like the Closed World Assumption (CWA) and the Unique Name Assumption (UNA), are more appropriate than the OntologyWeb Language (OWL) assumptions, also because such ontologies frequently stem from relational databases, where both CWA and UNA are adopted. This article presents OntoDLV, a system based on ASP for the specification and reasoning on enterprise ontologies. OntoDLV implements a powerful ontology representation language, called OntoDLP, extending (disjunctive) ASP with all the main ontology features including classes, inheritance, relations and axioms. OntoDLP is strongly typed, and includes also complex type constructors, like lists and sets. Importantly, OntoDLV supports a powerful interoperability mechanism with OWL, allowing the user to retrieve information from OWL ontologies, and build rule-based reasoning on top of OWL ontologies. The system is already used in a number of real-world applications including agent-based systems, information extraction, and text classification.