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Semantic Information Integration with Transformations for Stream Reasoning Semantic Information Integration with Transformations for Stream Reasoning Fredrik Heintz Daniel de Leng Department of Computer and Information Science Department of Information and Computing Sciences Linkoping¨ University, Sweden Utrecht University, The Netherlands [email protected] [email protected] Abstract—The automatic, on-demand, integration of informa- To address these problems we have developed a semantic tion from multiple diverse sources outside the control of the matching approach using semantic web technologies [2]. An application itself is central to many fusion applications. An ontology is used to define a common vocabulary over which important problem is to handle situations when the requested information is not directly available but has to be generated symbolic reasoning can be done. Streams are annotated with or adapted through transformations. This paper extends the ontological concepts to make semantic matching between semantic information integration approach used in the stream- symbols and streams possible. The output of the semantic based knowledge processing middleware DyKnow with support matching is a stream specification which is used to produce for finding and automatically applying transformations. Two a stream of time states corresponding to a temporal model of types of transformations are considered. Automatic transfor- mation between different units of measurements and between the symbols. One major advantage is that the semantics of streams of different types. DyKnow achieves semantic integration a stream can now be described by the creator of the stream by creating a common ontology, specifying the semantic content and then found by anyone based on this semantic annotation. of streams relative to the ontology and using semantic matching Previously the user had to know the meaning of the content to find relevant streams. By using semantic mappings between of streams to select the appropriate ones. ontologies it is also possible to do semantic matching over multiple ontologies. The complete stream reasoning approach is The semantic information integration approach extends the integrated in the Robot Operating System (ROS) and used in stream reasoning functionality of the stream-based knowledge collaborative unmanned aircraft systems missions.1 processing middleware framework DyKnow [3], [4]. DyKnow is integrated in the Robot Operating System (ROS) [5], which I. INTRODUCTION makes it available for a wide variety of robotic systems. An important problem for automatic, on-demand, integra- The information available to modern systems such as tion of information from multiple diverse sources outside the robots is often incremental in nature. A flow of incrementally control of an application itself is to handle situations when available time-stamped information is called a stream. As the the requested information is not directly available but has to number of sensors and other sources of streams increases there be generated or adapted through transformations. The work in is a growing need for incremental reasoning over streams to this paper further extends the semantic information integration draw relevant conclusions and react to new situations with approach with support for finding and automatically applying minimal delays. We call such reasoning stream reasoning. transformations. Two types of transformations are considered. Reasoning over incrementally available information is Automatic transformation between different units of measure- needed to support important functionalities such as situation ments and transformations between different types of streams. awareness, execution monitoring, and planning [1]. One major issue with regards to such stream reasoning is II. STREAM REASONING its integration in robotic systems. To do symbolic reasoning it One technique for incremental reasoning over streams is is necessary to map symbols to streams in a robotic system, progression of metric temporal logic formulas. This provides which provide them with the intended meaning for the real-time incremental evaluation of logical formulas as new particular robot. This is often done syntactically by mapping information becomes available. First order logic is a powerful symbols to streams based on their names. This makes a system technique for expressing complex relationships between fragile as any changes in existing streams or additions of new objects. Metric temporal logics extend first order logics with streams require that the mappings be checked and potentially temporal operators that allow metric temporal relationships changed. This also makes it hard to reason over streams of to be expressed. For example, our temporal logic, which information from multiple heterogeneous sources, since the is a fragment of the Temporal Action Logic (TAL) [6], name and content of streams must be known in advance. supports expressions which state that a formula F should hold within 30 seconds and that a formula F 0 should hold 1This work is partially supported by grants from the Swedish Foundation in every state between 10 and 20 seconds from now. This for Strategic Research (SSF) project CUAS, the Swedish Research Council (VR) Linnaeus Center CADICS, and the Center for Industrial Information is similar to the well known Metric Temporal Logic [7]. Technology CENIIT. φ τ φ Informally, ♦[τ1,τ2] (“eventually”) holds at iff holds at τ 0 ∈ [τ + τ , τ + τ ] φ some 1 2 , while [τ1,τ2] (“always”) holds is much harder for a designer to explicitly state and maintain 0 at τ iff φ holds at all τ ∈ [τ + τ1, τ + τ2]. Finally, φ U[τ1,τ2] ψ this mapping. Therefore automatic support for mapping 0 (“until”) holds at τ iff ψ holds at some τ ∈ [τ + τ1, τ + τ2] features in a formula to streams in a system is needed. such that φ holds in all states in (τ, τ 0). The purpose of this matching is to find one or more We have for example used this expressive metric temporal streams for each feature whose content matches the intended logic to monitor the execution of complex plans [8] and to meaning of the feature. This is a form of semantic matching express conditions for when to hypothesize the existence between features and contents of streams. The process of and classification of observed objects in an anchoring matching features to streams in a system requires that the framework [9]. For example, suppose that a UAV supports a meaning of the content of the streams is represented and that maximum continuous power usage of M, but can exceed this this representation can be used for matching the intended by a factor of f for up to τ units of time, if this is followed meaning of features with the actual content of streams. by normal power usage for a period of length at least τ 0. The The same approach can be used for symbols referring to following formula can be used during execution to detect objects and sorts. It is important to note that the semantics of violations of this specification: ∀uav :(power(uav) > M → the logic requires the set of objects to be fixed. This means power(uav) < f · M U[0,τ] [0,τ 0] power(uav) ≤ M). that the meaning of an object or a sort must be determined The semantics of these formulas are defined over infinite for a formula before it is evaluated and then stay the same. state sequences. To make metric temporal logic suitable for It is still possible to have different instances of the same stream reasoning, formulas are incrementally evaluated using formula with different interpretations of the sorts and objects. progression over a stream of time-stamped states. The result Our goal is to automate the process of matching the intended of progressing a formula through the first state in a stream is meaning of features, objects, and sorts to content of streams a new formula that holds in the remainder of the state stream in a system. Therefore the representation of the semantics if and only if the original formula holds in the complete state of streams needs to be machine readable. This allows the stream. If progression returns true (false), the entire formula system to reason about which stream content corresponds must be true (false), regardless of future states. Even though to which symbol in a logical formula. The knowledge about the size of a progressed formula may grow exponentially in the meaning of the content of streams needs to be specified the worst case, it is always possible to use bounded intervals to by a user, even though it could be possible to automatically limit the growth. It is also possible to introduce simplifications determine this in the future. By assigning meaning to stream which limits the growth for many common formulas [10]. content the streams do not have to use predetermined names, hard-coded in the system. This also makes the system domain III. SEMANTIC INFORMATION INTEGRATION independent meaning that it could be used to solve different A temporal logic formula consists of symbols representing problems in a variety of domains without reprogramming. variables, sorts, objects, features, and predicates besides In a previous paper [2] we presented a solution based on the symbols which are part of the logic. A feature creating an ontology acting as a common vocabulary of fea- represents a property or relation that may change values tures, objects, and sorts, a language (SSLT ) for representing over time. A sort is a collection of objects. Consider the content of streams relative to the ontology, and a semantic ∀x ∈ UAV : x 6= uav1 → XYDist[x, uav1] > 10, which matching algorithm for finding all streams which contain in- has the intended meaning that all UAVs, except uav1, should formation relevant for a feature, object, or sort in the ontology. always be more than 10 meters away from uav1. This formula This is in line with recent work on semantic modeling of contains the variable x, the sort UAV, the object uav1, the sensors [11], [12] and on semantic annotation of observations feature XYDist, the predicates 6= and >, and the constant value for the Semantic Sensor Web [13]–[15].
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